1. Introduction
Augmented reality (AR) is a transformative technology that overlies digital information in the physical environment, enabling user interaction with computer-generated imagery and information [1, 2]. This integration of virtual and real-world elements serves as a potent tool for knowledge transfer, particularly in education and industry, as evidenced in the studies in [3, 4]. AR is a leading technology of the 21st century due to its rapid growth and adoption in marketing, marking it as a promising trend [5–7]. The emergence of adaptive augmented reality (AAR) in response to real-time contexts and user characteristics has enhanced its usability and applicability [8]. AR integrates digital information into the physical environment, enabling user interaction through an overlay of computer-generated imagery [1]. This fusion of real and virtual worlds, with the real-time superimposition of digital elements, facilitates simultaneous interaction with the physical environment [9, 10]. The evolution of AR technology encompasses diverse instruments and approaches [11], and according to the authors in [12], AR is conceptualised from the technologies of virtual reality (VR) that introduce dynamic three-dimensional (3D) interfaces and virtual objects into the real world [12]. AR enhances the user experience and visualises scientific knowledge in electromobility-charging networks.
In electromobility, AR is particularly advantageous for designing and optimising a permanent magnet synchronous motor (PMSM) for EVs. AR technology is instrumental in visualising and analysing motor components and performance, thereby contributing to the development of efficient electric propulsion systems [13]. Within EV charging networks, AR addresses challenges related to inadequate infrastructure coverage by offering solutions for visualisation and optimisation. Integration into intelligent scheduling systems employing reinforcement learning and adaptive models enhances EV charging processes [14, 15]. Moreover, AR visualises data communication frameworks and Internet of Things (IoT) networks, providing immersive experiences in the context of the EV charging infrastructure [16]. AR finds applications in training future engineers, emphasising its potential for educational purposes [17]. Within electric vehicle (EV) charging networks, AR is valuable for addressing challenges related to infrastructure size and geographic coverage [14]. Integration into intelligent scheduling systems utilising reinforcement learning and adaptive models further optimises EV charging processes [15]. Moreover, AR visualises data communication frameworks and IoT networks, offering fully integrated and immersive experiences in the context of EV charging infrastructure [16].
AR has emerged as a transformative technology across various sectors including energy trading. Integrating AR into energy trading processes can enhance decision-making [18, 19], improve the visualisation of data [20, 21] and facilitate real-time interactions among stakeholders [22]. This study also explores the implications of AR in energy trading, focussing on its potential benefits, applications and underlying technological advancements that support its implementation. One of the primary advantages of AR in energy trading is its ability to provide immersive visualisation of complex data by overlaying digital information onto the physical environment. AR allows energy traders and analysts to interact with data more intuitively. For instance, AR can visualise energy consumption patterns [22], market trends [23] and predictive analytics in real time [24], enabling users to make informed decisions quickly. This validates the postulation that AR enhances user engagement and understanding, particularly in an educational context. However, specific applications in energy trading require further research [25]. The ability to dynamically visualise data can lead to improved operational efficiency and better strategic planning in energy markets. The adoption of AR technologies can foster collaboration between various stakeholders in the energy sector. As noted in [26], the integration of AR with human resources can significantly enhance the adaptability of teams to new technologies, ultimately benefiting organisational performance. In energy trading, where collaboration between traders, analysts and technical teams is crucial, AR can facilitate seamless communication and information sharing. This collaborative aspect is further supported by integrating AR with building information modelling (BIM), which allows for the real-time visualisation of energy systems and infrastructure [27]. Such integration can lead to more effective project delivery and operational management for energy trading. The technological evolution of AR has also played a critical role in its energy trading applications, and advancements in AR hardware and software have made it more accessible and practical for real-world applications. The findings in [27–29] indicate that the rapid development of AR tools has enabled their use across various fields, including energy management and trading. The continuous improvement in AR technology, combined with its ability to provide real-time data visualisation and interaction, positions it as a valuable asset in the energy trading landscape.
Within specific industries, AR has applications in diverse areas, such as port marketing strategies [30], collaborative industrial robots [31] and training for managing industrial equipment and instruments [32]. Markerless AR applications in the e-commerce sector, particularly in face detection, underscore the versatility of AR across sectors [33]. Moreover, AR has emerged as a valuable tool for historical education and building preservation, contributing to the education of individuals about historical structures [34]. Integrating online and offline stores is pivotal for widespread AR adoption, particularly in the marketing industry [35]. AR presents transformative potential across various sectors, including education, fashion, manufacturing and marketing. This demonstrates the versatility of the AR technology across multiple sectors and highlights its potential impact in various real-world contexts. AR’s potential of AR extends to museum visits and enhances visitor experiences by integrating advanced technologies and imaging capabilities [36]. While the referenced studies predominantly focused on AR applications in various fields, including education and engineering, a notable gap exists in studies on AR integration in the electromobility sector. The current drive to reduce carbon footprints in the transportation sector has significantly pioneered the adoption of electromobility with evolving technology and different designs, and AR has gained traction among different manufacturers of EVs, transforming user experiences and increasing the human–machine interface (HMI).
Advancements in electromobility technologies and the widespread adoption of EVs have led to concerns about grid stability challenges [37]. These issues underscore the urgent need for democratised energy-sharing and trading systems, especially with the rise in prosumers. While the authors in [38] explored the integration of AR in the broader electromobility sector, the authors in [39] focused on augmented-reality-heads-up display (AR-HUD) systems specifically designed to assist colour-blind drivers of EVs and [40] implemented an in-vehicle AR-HUD to provide critical driving safety information. However, many existing AR applications lack a user-centred design framework tailored specifically for EV charging, leaving nontechnical users overwhelmed by the complexity of dynamic data displays. This study addresses this research gap by proposing a comprehensive AR design framework outlining specific tools and technologies for developing intuitive, inclusive AR interfaces. These interfaces provide diverse user demographics, simplifying the interpretation of complex energy and charging data to enhance accessibility and user engagement in EV charging applications. In [41], a user-friendly platform for the maintenance and diagnostics of EV-supported training and upskilling initiatives for technicians and other industry players was presented. However, the authors in [41] mentioned the integration of AR software with the existing automation systems. However, it does not delve into the challenges faced during this integration process or how to overcome them, indicating a gap in the understanding of the practical hurdles of AR implementation in industrial settings. This study addresses the research gaps and challenges faced when integrating AR into the electromobility industry.
The contributions of this research survey are multifaceted, advancing the theoretical and practical understanding of AR technologies in electromobility charging and energy trading systems. The key contributions of this study include the following:
- 1.
Theoretical Framework for AR in Electromobility: This study develops a robust theoretical foundation for integrating AR into electromobility charging networks, addressing architectural frameworks, design methodologies and technological infrastructure necessary for building practical systems.
- 2.
Human–Computer Interaction (HCI) and User Experience Optimisation: By emphasising HCI paradigms, this research identifies critical design considerations for AR interfaces to enhance user engagement and improve accessibility in energy democratisation and electromobility applications.
- 3.
Integration of AR with P2P Energy Trading: The study explores emerging innovations in P2P energy trading and sharing (P2P-ETS) mechanisms, analysing how AR can optimise these processes, thereby contributing to a decentralised and efficient energy ecosystem.
- 4.
Analysis of Implementation Challenges: The study provides an in-depth examination of the integration challenges during the implementation of AR with electromobility infrastructure and dynamic energy trading platforms, offering actionable insights to overcome technical and operational barriers.
- 5.
System Integration for Sustainability: This research survey highlights AR’s potential to foster efficient energy transactions among prosumers and enable decentralised energy networks. This research also contributes to the development of sustainable, user-centric electromobility charging networks by optimising energy use and accessibility.
- 6.
Actionable Insights for Stakeholders: The findings provide valuable recommendations for system designers, electricity prosumers and electromobility infrastructure developers, bridging the gap between technical requirements and user needs for AR-enhanced charging systems.
While this section presents the background to the study, Section 2 presents the methodology adopted for this review, detailing the selection process adopted to identify the most pertinent research articles that inform this study. It also justifies the potential impact of this research within sub-Saharan Africa, where unique regional energy challenges amplify the need for advanced energy trading solutions. Section 3 offers a conceptual overview of AR, synthesising definitions from various sources to contextualise AR’s role of AR in enhancing user experience in electromobility and examining recent paradigms in electromobility energy trading for optimising user engagement and operational efficiency. Section 4 addresses the limitations of the existing energy trading models and user experiences within the electromobility sector and identifies these constraints as drivers for innovation in P2P energy trading. Section 5 investigates the practical aspects of AR design in electromobility, focussing on tools such as heads-up displays (HUDs) and windshield AR, including user-centred design elements critical to AR integration into electromobility systems, such as interface considerations for energy trading, data visualisation and privacy and security standards. Section 6 highlights successful case studies of AR integration with other digital technologies in electromobility, demonstrating AR’s adaptability and role of AR in enhancing sector-specific applications. Section 7 illustrates how AR can address the existing challenges in electromobility charging systems while enhancing energy trading platforms. The findings reveal the complexities of implementing AR within the sector, offering insights into the potential impact and resources required for successful deployment. Section 8 outlines future research directions and actionable recommendations for addressing gaps and promoting scalable, user-friendly AR solutions within electromobility energy trading networks. Finally, Section 9 concludes the study.
2. Review Methodology
The process used to select the most pertinent publications that are important to this study is presented in this section. According to the use and integration of AR in energy trading, as it relates to electromobility charging systems, the first representative nations designated as the “essential participants” in AR were chosen from the six continents of the world: Africa, Europe, Australia, North America, South America and Asia. The representative countries and individual contributions to AR in electromobility charging are shown in Figure 1, created using VOSviewer Version 1.6.20; the countries considered for AR and electromobility energy trading based on the countries’ statistics of electromobility charging systems are presented in Figure 1 as obtained from [42]. As shown in Figure 1, the People’s Republic of China has established a dominant position in the global electromobility charging market, accounting for approximately 6.68 million EV sales, representing nearly 67% of global sales in 2023 [43]. The visualisation in Figure 1 effectively shows China, the United States of America and Germany as major hubs in AR development, with extensive collaboration networks and exceptionally high research collaboration between European countries and across the Asia-Pacific region. However, the adoption rates in the African continent remain significantly lower; within African nations, South Africa leads to EV sales, followed by Kenya, although these figures are modest compared to the global scale [44]. The comparatively small data points of African countries in Figure 1 highlight the status of African countries as late adopters of electromobility technologies, including energy trading and AR interfaces [44]. This gap underscores the need for further investment and policy support to facilitate the uptake of advanced technologies within Africa.
[IMAGE OMITTED. SEE PDF]
Web of Science (WoS) and Scopus databases were used to search for credible articles (conference proceedings and journal articles) on both themes of this study: electromobility charging networks, energy (electricity) trading and AR. Search operators were used to determine the exactness and similarity of search keywords. These operators include the AND, OR and “”. In this regard, “Augmented Reality in Energy Trading” and “Augmented Reality in Electromobility charging Networks” were the two keywords used to investigate the trend of AR-based studies in both resources.
The relationship between AR and EV charging infrastructure represents a critical yet underexplored research area, particularly in sub-Saharan Africa. The visualisation in Figure 2 reveals a notable gap between the AR cluster (green) and the EV/power system cluster (red), suggesting limited integration between these technologies despite their potential synergies. This gap is particularly significant for sub-Saharan Africa for several reasons. In sub-Saharan Africa, the rapid growth of EV adoption faces unique challenges related to infrastructure visibility, maintenance and user experience. AR technologies can bridge these challenges by providing innovative solutions, such as real-time visualisation of charging station locations, intuitive navigation to available charging points and interactive maintenance guidance for EV infrastructure. The limited connectivity between AR and EV technologies in the network diagram in Figure 2 highlights the opportunity to develop integrated solutions that can address the specific needs of emerging markets. The potential impact of integrating the AR with EV charging systems in sub-Saharan Africa is substantial. First, it can enhance user adoption by providing intuitive interfaces for new EV users in regions where electric mobility is still emerging. Second, providing technical workers with remote support and visual instruction could increase the maintenance efficiency and alleviate the region’s scarcity of qualified EV technicians. Third, AR could facilitate better grid integration and power management by visualising real-time electricity consumption and grid stability data, which are crucial in regions with developing power infrastructure. The network visualisation in Figure 2 emphasises the research gap, showing established connections within each technological domain but limited cross-domain integration. While AR shows robust connections to medical applications, tourism and education (as shown by the green clusters) and EV technology is well connected to power systems and grid management (red clusters), there is a clear opportunity to develop frameworks that combine these technologies for sustainable transportation infrastructure in developing regions. This gap presents an opportunity for innovative research that could significantly impact the successful deployment of EV charging infrastructure in sub-Saharan Africa through AR-enhanced solutions.
[IMAGE OMITTED. SEE PDF]
3. Definition and Concepts of AR
AR technology enables seamless digital and real environmental integration, enabling instantaneous interaction through computer-generated imagery [1]. It merges the real and virtual worlds, presenting digital elements concurrently with the physical environment [9, 45], and has evolved with diverse instruments and application approaches [11]. Described as a dynamic extension of VR, AR facilitates the integration of 3D digital interfaces into the real world, offering an innovative means of environmental interaction [12]. In the realm of electromobility charging networks, AR has the potential to enhance the user experience and facilitate scientific knowledge visualisation. AR is an emerging technology that has significant potential in various domains. Consequently, numerous researchers have proposed definitions to capture its essence and scope. Table 1 presents a comprehensive compilation of these diverse definitions, offering a holistic view of how AR is conceptualised in academia.
Table 1 Definitions of AR.
| S/N | Definitions of AR | Citations |
| 1 | AR technology incorporates digital data seamlessly into the user’s physical environment, offering an immersive experience that enhances perception and interaction. | [7, 46] |
| 2 | Real-world and digital content combination for additional information with enhanced user experience by projecting digital content in real world | [47] |
| 3 | It is a collaborative setting that blends the real and virtual worlds for instantaneous engagement, enhancing real-world objects with computer-generated data. | [48] |
| 4 | It is a mixture of natural and computer-generated worlds that integrates physical and virtual worlds for interaction and 3D placement. | [49] |
| 5 | AR superimposes virtual objects into a natural environment for enhanced experience. It also combines real and virtual environments for interactive 3D perception. | [50] |
| 6 | AR superimposes 3D digital data onto real-world images, creating an enabled representation and manipulation of 3D chemical structures. | [51] |
| 7 | AR superimposes 3D models on reality, providing an enhanced understanding of educational concepts through visualisation. | [52] |
| 8 | AR uses a three-dimensional model overlayed on reality with an enhanced understanding of educational concepts. | [53] |
| 9 | With AR technology, a composite vision of the natural world is produced by superimposing computer-generated visuals. | [54] |
| 10 | This technology extends reality by adding virtual content to the physical environment. | [55] |
| 11 | AR involves blending virtual objects with the natural world and enabling interaction. | [56] |
| 12 | It combines visual reality with the real world, enhancing the user’s perception by overlaying digital content onto the real-world view. | [57] |
| 13 | AR enhances physical spaces or objects with relevant digital information | [58] |
| 14 | AR involves projecting virtual images onto the physical world, providing visual, auditory and kinaesthetics feedback. | [59] |
| 15 | AR is part of the extended reality (XR) spectrum, encompassing various immersive technologies. | [60] |
| 16 | AR differs from virtual reality (VR) in that AR incorporates virtual aspects into the real world in real time, whereas VR produces a wholly fabricated environment for users to interact. | [61] |
| 17 | AR technology has been used in various industries, including healthcare, education and entertainment, showcasing its versatility and potential impact. | [62] |
3.1. Survey Approach
This review adopts a systematic methodology to synthesise the existing literature on the integration of AR technologies within electromobility charging networks and energy trading platforms. The aim was to ensure transparency, rigour and replicability, while providing a comprehensive overview of the field. A structured search was conducted across IEEE Xplore, Scopus, WoS, ScienceDirect and SpringerLink, covering publications primarily covering 2018 to 2024, to reflect the period in which AR and electromobility research gained prominence. The search strategy combined relevant keywords and Boolean operators, including (“Augmented Reality” OR “AR”) AND (“Electromobility” OR “Electric Vehicle Charging” OR “EV Charging”), (“Energy Trading” OR “Peer-to-Peer Energy” OR “P2P Energy” OR “Blockchain Energy Trading”) AND (“User Experience” OR “UX”) and (“AR Applications” AND “Sustainable Energy” AND “Charging Networks”). In addition to peer-reviewed articles, selected conference proceedings, white papers and technical reports are considered to capture emerging industrial insights. Reference chaining was used to identify relevant studies beyond the initial search results. Articles were included if they were published in English between 2019 and 2024, focused on AR, user experience or digital tools within electromobility charging or energy trading contexts, and either empirically or theoretically addressed innovations, such as P2P energy trading, blockchain integration or AR-enabled visualisation. Studies are required to provide sufficient methodological details or conceptual frameworks relevant to AR’s role of AR in user engagement, energy trading or electromobility. The exclusion criteria included studies not written in English, publications without accessible full texts, articles unrelated to electromobility or energy trading (such as AR in gaming or unrelated medical contexts) and those lacking methodological rigour unless they offered substantial conceptual or industrial contributions, such as selected white papers. The initial search returned a broad pool of articles from which duplicates were removed before screening the titles and abstracts for relevance. Articles meeting the inclusion criteria were subjected to a full-text review, resulting in a final set of critically examined studies. The selection process followed the PRISMA principles as shown in Figure 3 to ensure replicability. For each included study, data on publication year, research objectives, methodological approaches, technological focus (AR, P2P, blockchain and electromobility) and reported user experience outcomes were extracted and thematically synthesised. The synthesis was structured around key themes, including the definitions and concepts of AR, current electromobility charging and energy trading paradigms, AR design principles and tools, challenges and limitations and future research directions.
[IMAGE OMITTED. SEE PDF]
The article selection process followed PRISMA guidelines, beginning with an initial pool of records identified through database searches, from which duplicates were removed before screening by title and abstract. Full-text articles were assessed for eligibility based on inclusion and exclusion criteria, resulting in the final set of studies synthesised in this review.
3.2. Discussion and Findings
AR has received substantial attention in diverse fields, including education and engineering. Studies in engineering education have concentrated on AR applications, aiming to facilitate the teaching of intricate subjects such as power electronics and electronic practice while also investigating the factors influencing AR acceptance among engineering students [63, 64]. The exploration of AR’s applicability extends to various disciplines, such as mathematics and tourism, suggesting its potential to enhance the learning experience across domains [65, 66]. The recognised potential of AR technology in augmenting educational experiences lies in its ability to heighten realism and foster enhanced emotional and cognitive engagement [67]. Its integration into education has introduced novel scenarios supporting teaching and learning processes, with applications extending to diverse contexts, such as tourism, gamification and classroom illustrative learning. These applications illustrate and underscore the versatility of AR and its broad impact [13, 68, 69]. In educational contexts such as orthodontics and physics learning, AR is recognised for its efficacy in integrating formal learning and improving understanding through visual 3D simulations [54, 70]. AR’s application of AR extends to the industrial sector, offering assistance in processes and operations [71] and garnering interest in the forging industry for its relevance in manufacturing and processing [72]. Figure 4 illustrates an overview of the AR applications.
[IMAGE OMITTED. SEE PDF]
3.3. Current State of Electromobility Charging Networks and Energy Trading Discussion and Findings
The burgeoning enthusiasm for electromobility charging networks with electrical power trading stems from the escalating adoption of EVs and the imperative for an efficient charging infrastructure. Challenges such as insufficient network size and geographic coverage impede electromobility promotion, necessitating advancements in EV technology, cost reduction and the implementation of intelligent, renewable energy-based charging solutions [14, 73]. Charging stations are essential for advancing electromobility, particularly across areas with extensive road and maritime transportation infrastructure [74, 75].
The impact of EV charging systems on grid stability and power quality raises concerns, particularly with the existing simulation studies yielding conflicting results. Thus, further investigation is necessary to improve the current state of EV charging infrastructure [76]. The anticipated benefits of electromobility development include improved ai37r quality, economic stimulation and enhanced energy security [77]. As a primary strategy for mitigating rising fuel consumption, electromobility is significant in the automotive industry [78]. The multifaceted nature of electromobility charging networks and energy trading necessitates a comprehensive approach addressing the technological, infrastructural, economic and environmental dimensions. Policymakers targeting green transitions prioritise electromobility promotion with the advancement of EV charging infrastructure and energy trading solutions as critical components to facilitate widespread EV adoption. Energy trading is crucial in smart electromobility charging because it facilitates the efficient management of the electricity flow between EVs and the power grid. By incorporating various energy trading mechanisms, such as P2P trading [79], blockchain-based trading platforms [80–82] and smart contracts [83], EV owners can optimise their charging schedules, reduce costs and contribute to grid stability. These approaches aid in integrating EV charging into intelligent grid systems, ensuring that the charging process is coordinated to prevent grid overload [84]. Energy trading enables the monetisation of excess energy stored in EV batteries through Vehicle-To-Grid (V2G) technologies [85, 86], allowing EVs to serve as a resource for alleviating power rationing in nonresidential buildings. The synergy between energy trading and smart electromobility charging is evident in the development of innovative solutions such as V2G networks [87], cloud computing-enabled virtual power plants [88] and smart city projects [89]. These initiatives underscore the significance of optimising energy management, enhancing grid resilience and promoting sustainable practices in the transportation sector. By leveraging data-driven frameworks [90] and advanced optimisation techniques [91], stakeholders can streamline energy-efficient practices, reduce peak demand and foster a more sustainable energy ecosystem.
3.3.1. Current Energy Trading Paradigms in the Context of Electromobility and AR
In the evolving landscape of energy management and sustainable transportation, the convergence of P2P energy trading, electromobility and AR technologies has driven a paradigm shift in the traditional energy markets. P2P energy trading has emerged as a decentralised approach that enables direct transactions between prosumers without centralised control [92, 93]. This model shows particular promise for distributed renewable energy sources in local networks [94], incorporating interdisciplinary elements, such as game theory for trading strategies [95] and consensus-based approaches for market structures [96]. Concurrently, the electromobility proposition is gaining traction as an effective strategy to reduce carbon emissions in the transportation sector, promising enhanced energy security, economic growth and improved air quality [77, 97]. This aligns with the European Union’s vision of a modern, competitive and carbon-neutral economy [98], with the interplay between the existing infrastructure and the evolving EV market driving significant changes in the automobile industry [99]. AR technology enhances human perception by merging real and virtual spaces [100, 101] and offers innovative approaches for visualising and interacting with complex energy systems. Integrating AR with P2P energy trading in the electromobility context presents numerous opportunities. These include intuitive visualisations of P2P energy networks, interactive trading interfaces, infrastructure planning assistance, real-time market insights, gamification of energy trading experiences and seamless integration with IoT devices and 5 G networks [102]. This integration can potentially create more transparent, efficient and user-friendly energy trading systems that support the transition to sustainable transportation and energy use. The synergy of these technologies represents a frontier in energy management, which requires interdisciplinary collaboration across computer science, renewable energy, psychology and economics. As P2P energy trading, electromobility and AR evolve, they promise to reshape the energy landscape and offer new paradigms for sustainable energy production, distribution and consumption. Future research should focus on optimising the integration of these technologies; addressing challenges in user adoption, data security and regulatory frameworks and quantifying the environmental and economic impacts of these integrated systems. Such efforts will be crucial for realising the full potential of this technological convergence and accelerating the transition to a more sustainable and decentralised energy future.
3.4. Integration of AR in Electromobility and Energy Trading Platforms
Integrating AR into electromobility and energy trading platforms offers significant potential for enhancing user experience and operational efficiency. Applications of AR in BIM, IoT data visualisation and energy monitoring systems [27, 28, 103, 104] suggest promising applications in the electromobility sector. These include intuitive visualisations of energy flows, real-time charging station status updates and interactive interfaces for energy trading. AR can leverage its demonstrated benefits in education and training [105] to improve user understanding of efficient charging practices and energy trading concepts. While current P2P energy trading platforms focus on federated power plants and blockchain-based solutions [106, 107], integrating AR could provide more intuitive representations of P2P networks’ real-time trading interfaces and assist in infrastructure planning. This integration has the potential to accelerate EV adoption and promote more efficient energy trading practices by providing stakeholders with enhanced visualisation tools and engaging in interactions with complex energy systems. The integration of augmented reality Internet of Things (AR-IoT) was introduced for IoT data visualisation, and it further demonstrated AR’s potential of AR in the context of IoT [104]. AR-IoT aligns with the concept of integrating AR-IoT for applications such as smart farming, demonstrating the many conceivable applications of AR in the context of IoT [108].
Energy platforms are essential for aggregating distributed energy resources (DERs), enabling flexibility in grid balancing and facilitating P2P and local energy exchange. Creating value streams for all stakeholders in auction-based local energy trading platforms highlights the possibility of market-based frameworks for demand-side flexibility scheduling and dispatching [109]. Incorporating AR into electromobility energy trading platforms has significant potential for enhancing user experience, facilitating efficient energy trading and contributing to the sustainable utilisation of energy resources. The diverse applications of AR in various domains, coupled with the potential of P2P energy trading platforms and market-based frameworks, provide a strong foundation for integrating AR into electromobility energy trading platforms.
4. Energy Trading in Electromobility Charging: Limitations of User Experience
The user experience of energy trading in electromobility systems faces several challenges and limitations. One significant hurdle involves establishing equitable trading mechanisms in P2P electricity markets, where conflicts of interest among participants impede seamless operation [110]. The complexities arising from large-scale energy mutualisation and precise energy scheduling pose formidable obstacles to further advancement in this domain [111]. The planning objectives of an all-encompassing energy framework necessitate consideration from diverse perspectives, including economic and reliability facets, thereby intricately complicating the operational scheduling process [112]. A comprehensive evaluation of integrated energy systems demands attention to monetary benefits, energy efficiency, user experience and environmental protection [112]. Numerous tactical solutions have been proposed to address these issues. The adoption of transactive energy management systems (TEMS), as illustrated by the authors in Figure 5, empowers end-users to actively engage in energy management, potentially facilitating the user experience [113]. Game-theoretic demand-side management adapts to imperfect consumer behaviour in a smart grid, allowing engaged users to strategically choose the best time to start energy trading to reduce their daily energy expenses [114]. The multifaceted challenges and limitations inherent in the user experience of energy trading in electromobility encompass issues related to fair trading mechanisms, energy mutualisation and intricate planning goals. Nevertheless, strategic approaches such as TEMS and game-theoretic demand-side management offer promising avenues to navigate these challenges, ultimately enhancing user experience in this dynamic landscape.
[IMAGE OMITTED. SEE PDF]
Analysing energy trading within electromobility charging reveals multifaceted challenges and user experience limitations. Successful electromobility implementation depends on a confluence of factors, necessitating the transformation of the energy system, establishment of a dispersed renewable energy network, creation of a robust charging infrastructure and implementation of adequate incentives for EV adoption [98]. The growth of charging infrastructure, market dynamics and legislative incentives are factors that impact electromobility development [115].
The escalating utilisation of portable devices underscores the need for enhanced autonomy, reduced charging time and diminished weight of energy storage components [116]. Advancements in technology should ideally manifest in an expanded EV range, lower production costs and create intelligent and user-friendly charging solutions that employ renewable energy sources [73]. Examining electromobility scenarios in expansive urban settings reveals that unregulated EV charging may strain the overall system, potentially surpassing 17% of the entire generation capacity by 2030 [117]. Concurrently, changes in car engine technologies necessitate parallel developments in the car-charging infrastructure, which is integral to ensuring the success of electromobility initiatives [118]. As electromobility develops, more focus is placed on the accessibility of EVs and charging infrastructures [74]. Notably, the absence of a standardised charging infrastructure is a paramount challenge, underscoring the need to define actors and their interrelations within the context of an electromobility network [119]. The evolution of electromobility confronts challenges related to the charging infrastructure, grid capacity and the imperative for user-friendly and accessible charging solutions. Successful implementation requires comprehensive efforts for energy system transformation, renewable energy development and adequate incentives for EV adoption. The conspicuous absence of a standardised charging infrastructure poses a significant impediment, necessitating focused efforts to facilitate widespread adoption of electromobility.
4.1. Innovations in Electromobility P2P-ETS
P2P energy trading presents an innovative paradigm within electromobility, empowering prosumers to engage in energy transactions without intermediaries [95]. This decentralisation fosters active participation in online trading platforms, fundamentally altering the local green energy market [120, 121]. The electromobility industry prioritises utility maximisation in P2P-ETS markets to optimise energy generation and communication resources. Integrating DERs into the grid requires efficient coordination to prevent instability and ensure reliability. P2P energy trading algorithms enhance energy management by considering fair resource allocation and minimising costs [122]. Employing game-theoretic approaches serves as a motivational strategy within the smart grid context [95], whereas the potential for prosumers to broaden their role in energy activities, including generation and service provision to the grid, is noteworthy [123]. Integrating P2P energy trading with intelligent residential architectures, renewable resources and home energy management systems has been proposed, emphasising profitable power transactions in innovative residential localities [124]. The study of blockchain technology in P2P energy trading systems offers insights into transformative possibilities through various blockchain scaling methods [125]. P2P energy trading transcends the energy sector, aligning with the sharing economy trend across diverse sectors such as transportation, accommodation and energy trading [126]. This expansion underscores the potential of P2P energy trading to play a pivotal role in shaping the future landscape of energy markets. Figure 5 shows the classification of P2P energy trading systems. Blockchain technology has attracted significant interest in P2P energy trading platforms. Blockchain technology has decentralised and secured attributes, positioning it favourably to facilitate direct energy exchanges among end-users [127]. In the electricity sector, blockchain holds promise for optimising established processes, such as metering, billing and grid management, in addition to fostering novel platforms for value exchange, exemplified by P2P energy trading [128].
The complexity of data interaction in decentralised energy entities further accentuates blockchain’s potential as a robust technology supporting P2P energy trading [129]. Numerous scholarly investigations have underscored the importance of blockchain in P2P energy trading. Globally, diverse pilot initiatives have substantiated blockchain’s role in decentralised energy sectors, attesting to its widespread exploration and adoption [130]. The distributive nature of blockchains and their ability to execute intelligent contracts have facilitated P2P energy trading in various applications [131]. Beyond direct trading, blockchain’s utility extends to large-scale energy transactions, project financing, supply chain monitoring and asset management, showcasing its versatility within the energy sector [132]. The inherent characteristics of blockchain render it naturally applicable to energy transactions in energy systems, reinforcing its potential to foster efficient and secure energy trading [133]. Recommendations for implementing blockchains in P2P energy trading platforms emphasise its capacity to establish decentralised and distributed trading systems, ensuring transparency, trustworthiness and security in energy transactions [134]. The features of the technology, including disintermediation, confidentiality and tamper-proof transfers, present valuable advantages for grid operations, economies and customers in the energy sector [135]. The amalgamation of scholarly perspectives accentuates the burgeoning interest and potential of blockchain technology in P2P energy trading systems. The decentralised, secure and transparent nature of blockchains is a promising technology for facilitating direct energy exchanges, optimising transactions and enhancing energy trading overall efficacy and security of energy trading platforms. Trading energy between individuals and infrastructure is beginning to transform the energy industry, allowing prosumers to participate actively and decentralise the energy market. The integration of P2P energy trading with innovative grid technologies, blockchain systems and game-theoretic approaches signifies its potential impact on the future of the electromobility and energy markets.
P2P energy trading platforms have garnered significant scholarly attention for their capacity to incentivise the efficient utilisation of DERs [136, 137]. These platforms empower prosumers to engage in direct energy transactions, obviating the need for intermediaries, particularly in microgrids, energy harvesting networks and V2G configurations [138]. Notwithstanding their potential, extant P2P energy trading platforms exhibit notable gaps that require scholarly scrutiny. A central challenge pertains to scalability, wherein current platforms may encounter limitations in accommodating a burgeoning number of participants and transactions [139, 140]. Furthermore, security concerns, particularly within the Industrial Internet of Things (IIoT), underscore the need to formulate secure energy trading mechanisms [139]. Optimising network charges in P2P energy trading also demands nuanced exploration encompassing mathematical optimisation and Stackelberg competition considerations [140, 141]. Integrating electromobility into P2P energy trading systems constitutes an additional domain that necessitates rigorous investigation, particularly for effectively managing energy consumption and providing market solutions [142]. Beyond this, a systematic classification and identification of gaps in the existing literature on P2P energy trading has emerged as a crucial scholarly pursuit [143]. The conceptualisation of decentralised markets for assimilating prosumer flexibility into power system operations has recently surfaced as a research area, underscoring the need for innovative designs in P2P energy trading to accommodate the evolving market dynamics [144]. The acknowledged capacity of blockchain technology and IoT to enhance the effectiveness of P2P energy trading platforms emphasises the necessity of developing innovative technical solutions in this scholarly field. The lacunae within the current P2P energy trading platforms, including scalability, security, network charge optimisation, integration of electromobility, systematic identification of research gaps, design of decentralised markets and exploration of the potential of blockchain technology, necessitate scholarly attention.
4.2. Blockchain Technology in P2P Systems for Trading Energy
Blockchain technology has garnered substantial attention within P2P energy trading systems. Its decentralised and secure attributes position it favourably to facilitate direct energy exchanges among end-users [127]. Within the electricity sector, blockchain has the potential to optimise established processes such as metering, billing and grid management while concurrently serving as a foundational element for innovative platforms such as P2P energy trading that facilitates value exchange [128]. Blockchain is a viable technology that can accommodate the complexity inherent in P2P energy trading by addressing the complicated data interactions arising from decentralisation [129, 145]. Various studies have underscored the relevance of blockchain in P2P energy trading. As demonstrated by many global pilot initiatives, the deployment of blockchain in P2P energy trading signifies its widespread exploration and application in decentralised energy sectors [130]. The distributive nature and capacity of blockchain to execute intelligent contracts have facilitated P2P energy trading across diverse contexts [131]. Beyond direct trading implications, the potential benefits of blockchain within energy trading systems extend to encompass large-scale energy transactions, project financing, supply chain tracking and asset management, thus showcasing its versatility within the energy sector [132]. The fundamental features of blockchain technology make it suitable for energy transactions in energy systems by default, emphasising its potential role in promoting efficiency and security within energy trading [133]. The integration of blockchain technology into P2P energy trading platforms points to the technology’s capacity to create a distributed, decentralised trading system that offers an environmentally safe, dependable and transparent platform for energy transactions [134]. Blockchain technology offers features such as disintermediation, confidentiality and tamper-proof transfers, thereby presenting valuable attributes for grid operations, economies and customers in the energy sector [135]. The amalgamation of scholarly insights highlights the escalating interest in and potential of blockchain technology in P2P energy trading systems. As a result of its decentralised, transparent and secure features, blockchain technology can be used to optimise energy transactions, increase the security and efficiency of energy trading platforms and allow direct energy trade between end users.
The overarching convergence of blockchain technology, P2P networks, AR and energy trading presents compelling innovation opportunities in the electromobility sector. The inherent security and transparency of blockchain can underpin P2P energy trading platforms [107], while AR can provide an intuitive visual interface for these complex systems. By overlaying real-time blockchain transaction data and P2P network information on the physical world, AR can enable users to visualise energy flows, trading patterns and market dynamics in a more accessible manner. This integration allows prosumers to interact with smart contracts, monitor energy production and consumption and participate in P2P trades through immersive AR experiences [146, 147]. Furthermore, AR can enhance users’ understanding of decentralised energy markets by visualising the impact of their trading decisions on a broader energy ecosystem. This technology synergy has the potential to democratise energy trading, improve market efficiency and accelerate the adoption of sustainable energy practices in the electromobility domain.
5. AR Technologies and Tools for Design in Electromobility
In electromobility charging systems, the authors in [154] emphasised the importance of efficient storage technologies for EVs, indicating the relevance of AR tools in simulating and analysing EV systems. Furthermore, the authors in [155] underscore the significance of electromobility in combating climate change and the need to diversify power generation sources, aligning with the potential of AR technologies to contribute to sustainable urban transport solutions. Moreover, the authors in [156] discuss the development of an AR-based occupational health and safety guidebook in an electricity laboratory, demonstrating the practical applications of AR in ensuring safety and efficiency in electrical environments. This application aligns with the potential use of AR technologies in the electromobility sector to enhance the safety protocols and operational procedures. The potential of integrating AR systems across digital twin (DT)–enabled systems enhances data analytic capabilities for energy trading. Figure 6 shows the digital twin network (DTN) architecture incorporated into electromobility systems for improved energy sharing and trading as shown in Figure 7. Examining the possible uses and effectiveness of AR technology in various contexts is essential for thoroughly reviewing the tools relevant to energy trading platforms. Several studies have explored the usability and effectiveness of the AR technology in different contexts. For instance, the authors in [157] examined how well AR technology can overcome the challenges in electrochemical processes, especially fuel cell energy conversion. The research in [23] highlighted the potential and justification of using AR in trade, marketing and product promotion, indicating its relevance in commercial applications. Furthermore, the authors in [158] emphasised the pedagogical attributes of AR as an instructional tool, suggesting its potential to enhance learning experiences in different subject areas.
[IMAGE OMITTED. SEE PDF]
[IMAGE OMITTED. SEE PDF]
Moreover, the use of AR in workplace environments has been investigated by the authors in [159], who discussed the general requirements for industrial AR applications and emphasised the significance of computer vision and artificial intelligence (AI) in this context. The study by the authors in [20] concentrates on using AR in product packaging, highlighting its potential in marketing and visual presentation. These studies collectively demonstrate the diverse applications of AR technology in different domains including education, marketing and industrial settings. The potential of AR technology to enhance visualisation and 3D modelling has been discussed in the literature. For example, the authors in [159] highlighted interest in AR from scientific and business perspectives, emphasising its role in providing an immersive user experience through 3D model visualisation. The investigation in [20] elucidates developments in visualisation technology, especially in automated image segmentation and augmented and VR-based 3D model visualisation [20].
5.1. Development of Conventional HUDs and Windshield AR for Electromobility
AR-HUDs are a significant advancement in electromobility technologies, offering electromobility users a wealth of interactive information to enhance driving safety and efficiency [161, 162]. By integrating AR into conventional HUDs, these systems can provide drivers with essential information without diverting their attention from the road, ultimately improving the overall driving experience [163]. Integrating immersive technologies such as holography, geometrical imaging and light-field rendering with eye tracking further enhances the capabilities of AR-HUDs, allowing for the implementation of 3D images and personalised displays tailored to the driver’s needs [162, 164]. The development of compact and dual-focal AR-HUDs has generated growing interest in this technology and its potential applications [165]. AR-HUDs can revolutionise how drivers interact with their EVs. By displaying navigation information directly in the driver’s field of view, AR-HUDs can contribute to a seamless driving experience, while promoting safety and efficiency [163]. Incorporating simulation-based optimisation and decision support systems into manufacturing processes demonstrates the versatility of AR technology beyond the automotive sector, highlighting its potential for enhancing various industries [19]. As AR-HUD technology continues to evolve, there is growing emphasis on customisation and user-friendly design. DIY AR-HUDs offer a glimpse into the future of wearable display technology, allowing individual components to be modified and upgraded to meet specific user requirements [166]. Integrating AR technology into windshield HUDs for EVs offers a promising enhancement of the driving experience. AR-HUDs project essential information directly onto the windshield, allowing drivers to access real-time data without diverting EV users’ attention from the road [167]. This approach maximises visual resources and improves driving safety by allowing drivers to focus on the road ahead [168, 169]. The development of AR-HUDs for EVs aligns with the broader trend in the electromobility industry towards enhancing user experience and safety through innovative display systems. Studies have shown that AR-HUDs have the potential to increase user acceptance and trust in fully automated vehicles by augmenting obscured traffic objects/participants, such as in dense fog conditions, and by presenting upcoming driving manoeuvres even when the driver’s orientation is not forward-facing [170].
The seamless uptake of visual information facilitated by HUDs aims to enhance driver interaction with a vehicle while ensuring secure operation [171]. The application of AR technology in windshield HUDs extends beyond the primary information display, offering opportunities for infotainment and navigation features tailored to driver needs. By embedding AR information on significant landmarks, navigational data and local news, AR prototypes for HUDs can transform the driving experience into a more interactive and engaging journey [169]. The potential for AR windshield displays to serve as platforms for infotainment in automated driving scenarios underscores the versatility and future possibilities of this technology [172]. As the automotive industry continues to explore AR-HUDs’ capabilities, research efforts are focused on evaluating driver preferences, assessing the impact on driving performance and determining the optimal glance durations for AR displays [173, 174]. By leveraging emerging technologies such as holography and optics, multiplane AR-HUD systems based on volume and holographic optical elements offer a glimpse into the future of advanced display solutions for EVs. The development of AR windshield HUDs for EVs represents a convergence of cutting-edge technology for electromobility, aimed at enhancing user experience, improving safety and redefining the driving environment of the future. Figure 8 shows a conventional AR-HUD and a windshield AR-HUD.
[IMAGE OMITTED. SEE PDF]
5.2. User-Centric Design Principles for AR in Electromobility
Design heuristics for AR have been developed to enhance practitioners’ use of AR design methods concerning human aspects and user experience [177]. User-centric designs are relevant to this challenge because they emphasise the importance of considering people when developing AR experiences. User-centric designs are particularly crucial when considering user-centred AR concepts for electromobility. The discussion by the authors in [178] translated tourist requirements into MAR application engineering through quality function deployment (QFD). While the reference does not directly address user-centric design principles for AR in electromobility, it highlights the importance of understanding user requirements and translating them into the design process, which is a fundamental aspect of user-centric design principles. User-centric designs emphasise the significance of considering human factors and user experience in developing AR applications, aligning with the task’s focus on human factors influencing the user experience in electromobility. The study in [179] discussed the recognition of advertisement emotions and the efficacy of content-centric convolutional neural network (CNN) features for affect recognition, and the study in [179] investigates user-centric augmentation using EEG responses obtained while seeing ads, showcasing how improved affect predictions enable efficient computational advertising. This work is relevant to this task as it highlights the importance of user-centric design principles in understanding user emotions and experiences, which are crucial in the context of AR in electromobility [179, 180]. The research in [181] explored the critical human factors influencing the user experience in autonomous driving technology. This study emphasises the importance of considering human factors, such as age, focus, multitasking capabilities, intelligence and learning speed, in designing interactions between drivers and automated driving systems to enhance user acceptance and trust. This aligns with user-centric design principles for AR in electromobility, as it underscores the significance of prioritising human factors to ensure good usability and user experience in technology development [181]. The reference provided does not directly speak to the task of “User-Centric Design Principles for AR in Electromobility: Human Factors Influencing User Experience”. The reference discussed the historical perspective and challenges faced in the development of AI, as well as the influence of experience on the quality of datasets. While it provides insights into AI development, it does not address user-centric design principles for AR in electromobility. Therefore, it is not directly relevant to the tasks [182].
According to the authors in [183], the elements impacting the user experience in AR applications include their clear goal, simplicity in usage and learning, seamless functioning, creative information display and interaction. These findings are crucial for understanding the human factors that contribute to a positive user experience in AR, aligning with the task of exploring the human factors influencing user experience in AR. As asserted by the authors in [184], the creation of a Configurable Interaction Questionnaire (CIQ) for assessing interactions with virtual or AR items was presented in [148]. The questionnaire measured five aspects of user satisfaction: comfort, consistency with expectations, quality of sensory improvements, assessment of task performance and interactions. Because it offers a particular tool for assessing the subjective user experience in AR/VR environments, this source helps study the human elements that influence the user experience in AR. According to the authors in [185], the use of MAR for learning has gained attention because of the freedom of movement it offers the users. This study provides insights into the impact of mobile devices on user experience, aligning with the task’s focus on human factors in AR. This reference directly addresses the task by addressing the human factors influencing the user experience in AR through empirical analysis and validation of optimal object placement methods [186]. According to the authors in [187], this study delves into the factors influencing users’ continuous use intention of AR-branded applications. The authors in [187] introduce the variables of “playfulness” and “spatial existence” within the framework of the technology acceptance model (TAM), shedding light on the human factors that impact user experience in AR.
5.3. Evaluation of the Design Considerations for AR Interfaces in Energy Trading
In energy trading, integrating AR interfaces presents a promising avenue for enhancing user experience and facilitating efficient decision-making processes. This section explores the vital design considerations for AR interfaces in the context of energy trading platforms. The significance of a readily accessible interface in VR interaction environments for evaluating digital prototypes during designer–client review sessions was explored in the study of [188], while the focus of the study in [189] was on P2P energy trading in micro/minigrids for local energy communities, which shed light on the importance of interface usability, a critical aspect for designing AR interfaces in energy trading. The contribution of [190] in design considerations for AR interfaces elucidates the usability evaluation of an AR-HUD interface using the analytical hierarchy process-grey relational analysis (AHP-GRA) model. The emphasis on usability evaluation aligns with the need for comprehensive user-centric design considerations in AR interfaces for energy trading platforms. The research in [191] explores the technical aspects of energy trading interfaces and offers guidance on creating and running an open-source P2P energy trading platform based on blockchain technology and the IoT. Their investigation of the machine-to-machine (M2M) Message Queuing Telemetry Transport (MQTT) protocol highlights the significance of comprehending the underlying technology for creating efficient AR interfaces in energy trading. A comprehensive overview of AR-driven human–city interaction was documented in [192], emphasising the importance of interface design and input techniques for AR headsets. Insights into visual and cognitive loads on smart glasses provide valuable considerations for designing AR interfaces tailored to the needs of energy traders. The impact of computational design and advanced visualisation on computer-aided design (CAD), particularly in architecture, was highlighted in [193]. Their discussion on the use of VR and AR interfaces underscores the importance of incorporating advanced visualisation technologies into the design process of AR interfaces for energy trading. In light of the dynamic nature of energy trading markets, the authors in [194, 195] proposed bilateral contract networks for real-time P2P energy trading, as shown in Figure 9. While their focus lies on negotiation mechanisms, their oversight underscores the need for AR interfaces to incorporate real-time optimisation and adaptive decision-making processes.
[IMAGE OMITTED. SEE PDF]
The study in [195] presented an innovative, low-cost, open-source, P2P energy trading system designed for a remote community without electricity and modern amenities. The core of the system includes a Raspberry Pi 4 Model B server hosting the trading system, and a local Ethereum blockchain server to deploy smart contracts. Real-time energy data are collected and transferred by IoT servers running on ESP32 microcontrollers and visualised with a HMI equipped with sensors and actuators. The React.JS library powers a blockchain-enabled user interface for trading. Operating on a secure local Wi-Fi network, the system employs stringent authorisation protocols including login credentials and private keys to ensure data integrity and security. Communication between servers and the user interface is facilitated through the Hypertext Transfer Protocol (HTTP), which emphasises the system’s robust design, comprehensive testing and successful implementation outcomes.
5.4. Enhancing Energy Trading Through AR Visualisation
In the pursuit of optimising energy-trading processes, integrating AR technologies has emerged as a promising avenue for enhancing visualisation and decision-making capabilities. To explore the pertinent research literature and identify key insights into the potential applications of AR in energy trading, the authors in [196] delved into future technologies that could enable 6 G networks, including virtual and virtual augmented reality (VAR). Their discussion underscores the need for advancements in network technologies to support emerging applications, such as AR, which is crucial for enhancing energy trading through visualisation. The authors emphasised the importance of lower latency and higher data rates for successful AR implementation in energy trading. The study of the authors in [197] examines how South Asia’s shift to renewable energy, energy efficiency and environmental sustainability is affected by trade in information and computer technology (ICT). Their work sheds light on the possible effects of technological improvements on energy trading and visualisation, such as AR, although it does not explicitly address energy trading. It focuses on lowering trade barriers for ICT to guarantee environmental sustainability and energy security. In addition, the authors in [101] explored the technical aspects and applications of MAR in the context of 5 G mobile edge computing. Although not directly related to energy trading, their insights provide a foundational understanding of AR technology applicable to visualising and enhancing energy trading processes. This understanding is crucial for exploring AR’s potential of AR in visualising energy trading.
The versatility and applications of AR technologies, including education, exhibition enhancement and virtual museums, were highlighted in [198]. Therefore, the research findings suggest that AR can effectively enhance energy trading through visualisation, offering immersive experiences to facilitate better understanding and decision-making. The effectiveness of AR in online shopping for furniture products was demonstrated in [199], indicating its potential to enhance decision-making processes. While not specific to energy trading, this study provides valuable insights into AR’s potential to enhance user experience, which can be extrapolated to energy trading visualisation. Highlights of the potential of AR in trade, marketing and product promotion were discussed in [22], showcasing its versatility in enhancing various business aspects. Although not directly addressing energy trading, the insights support the exploration of how AR can enhance visualisation in the energy trading sector. However, in the research [35], the user experience of AR was explored in the fashion industry, offering insights into its application and user interactions. Although not directly related to energy trading, their study provides valuable insights into AR’s use of AR in a specific industry, informing potential applications in energy trading. In the business context, the authors in [100] highlighted AR’s successful utilisation in marketing, showcasing its potential to enhance visualisation and user experience. The findings offer valuable insights into AR’s application of AR in a business context, with implications for enhancing visualisation and decision-making processes in energy trading. Integrating AR technologies into energy trading processes holds immense potential for enhancing visualisation and decision-making capabilities. By drawing insights from multidisciplinary research, this review identifies key considerations for leveraging AR in energy trading, thus paving the way for innovative solutions in the energy sector.
5.5. Security and Privacy Implications of AR Integration Into Energy Trading
Security and privacy aspects of decentralised energy trading were explored in [200], offering insights into strategies such as multisignatures and blockchain to address concerns. This research output provides valuable considerations for ensuring the security and privacy of AR integration in energy trading. A comprehensive review of AR technologies [201] underscores the need for distributed trust management in collaborative AR settings. The proposed trusted architecture addresses security concerns and highlights the significance of solid security measures in AR integration for energy trading. The author in [202] discusses the security and privacy risks associated with integrating technologies, such as AR, into the metaverse world, emphasising data breaches and identity theft concerns; thus, the author in [202] underscores the necessity of addressing security and privacy implications in AR integration for energy trading. The integrity of privacy concerns linked to AR was explored in [203], shedding light on the factors influencing user behaviour and potential implications. These insights emphasise the importance of considering privacy implications in AR integration for energy trading. A secured energy trading system to mitigate data leakage and attacks was experimented with in [204], offering a comprehensive approach to security and privacy in energy trading. The findings can inform strategies for ensuring security and privacy in AR integration for energy trading. The integration of blockchain-based authentication in V2G networks to address security concerns in energy trading, offering potential solutions applicable to AR integration, was researched in [205].
The consortium blockchain used for secure energy trading in IoT was implemented in [138], providing insights into addressing security challenges applicable to AR integration in energy trading. The authors of [206] proposed and discussed an architecture for distributed energy trading in blockchain networks, addressing the challenges of trustless trading and ensuring smooth operations pertinent to understanding the security implications of AR integration for energy trading. The privacy-preserving energy trading scheme proposed by the authors in [87] emphasises the protection of sensitive data in transactions, aligning with the need to safeguard privacy in AR integration for energy trading. Postulations of [207] discuss blockchain technology in energy trading within IoT, highlighting fairness, efficiency, security and privacy concerns, and provide insights into solutions for addressing these concerns in AR integration for energy trading. Integrating AR into energy trading requires careful consideration of the security and privacy implications. Insights from these studies inform strategies to ensure secure and private AR integration, advancing energy trading technologies while safeguarding sensitive data and user privacy.
6. Some Success Stories of Integration of AR Into Digital Technologies for Electromobility Applications
A recent study by the authors in [204] focused on integrating blockchain technology in EV energy trading, highlighting its potential for fostering secure and transparent transactions in the EV sector. This study underscores the applicability of blockchain in revolutionising energy trading practices, particularly within the context of EVs. Meanwhile, the authors in [205] present an adoption framework for mobile AR games, drawing insights from the success of Pokémon Go. Although not directly addressing AR integration in energy trading, their work sheds light on the adoption and success factors of AR applications, providing valuable considerations for implementing AR across various sectors including energy trading. Moreover, the authors in [153] investigate the usability of AR in addressing challenges related to fuel cell energy conversion, offering practical insights into AR integration in vocational education for fuel cell technology. The findings provide a glimpse into the successful incorporation of AR into the energy sector, especially in educational settings. The authors in [206] conducted a study on the use of P2P energy trading by local communities, highlighting the contribution of blockchain and IoT technologies to decentralised energy transactions. This analysis supports the emphasis on renewable energy integration in energy trading platforms by guiding the implementation of renewable energy trading. In addition, the authors in [207] assessed sustainable bioenergy technologies, providing insights into the feasibility of integrating AR into energy trading through successful AR implementation in energy conversion technology assessments. Their findings contribute to the understanding of the practicality of AR integration in enhancing the decision-making processes in energy trading. Lastly, the authors in [208] demonstrated the effectiveness of AR in medical procedures, showcasing its potential to improve procedural success and efficiency. Although not directly related to energy trading, their findings suggest the transformative impact of AR in enhancing complex procedures, offering implications for its integration into energy trading to improve decision-making processes. These studies collectively provide valuable insights into the potential integration of emerging technologies such as blockchain and AR into energy trading practices, thereby paving the way for more efficient and sustainable energy systems.
7. Challenges in Implementing AR in Electromobility
AR holds great promise for revolutionising various industries including electromobility. However, its implementation in the electromobility sector faces several challenges that must be addressed to realise its full potential. This section identifies these obstacles and proposes directions for future research. One of the primary challenges in implementing AR in electromobility is the lack of standardised AR hardware and software solutions tailored specifically for the sector. The existing AR technologies are often designed for general use and may not satisfy the unique requirements of electromobility applications. Future research should focus on developing specialised AR solutions that address the specific needs of electromobility, such as real-time vehicle data visualisation and interactive maintenance guides. Another challenge is integrating AR technology with the existing electromobility infrastructure [201]. Electromobility systems such as EV charging stations and battery management systems are often complex and may not easily support AR applications. Future research should explore ways to seamlessly integrate AR technology with electromobility infrastructure to enhance the user experience and efficiency. Data privacy and security are significant concerns when implementing AR for electromobility devices. AR applications require sensitive vehicle and user data access, thereby raising potential privacy concerns [208]. Table 2 presents the recommendations of multiple authors. Based on this review, it is evident that future research should prioritise the development of robust data protection measures to ensure the security and confidentiality of the information accessed and processed by AR systems in electromobility. The high cost of the AR hardware and software is a barrier to the widespread adoption of electromobility. Future research should focus on developing cost-effective AR solutions that offer functionality comparable to expensive alternatives, making AR more accessible to electromobility stakeholders. AR can potentially revolutionise the electromobility industry; however, several issues must be resolved to gain all of its rewards. Therefore, future studies should concentrate on creating customised AR solutions for electromobility, integrating AR technology with the current infrastructure, protecting data security and privacy and reducing the implementation costs of AR. By addressing these challenges, AR can revolutionise electromobility and pave the way for a more sustainable transportation future.
Table 2 Selected research gaps in existing P2P energy trading platforms for electromobility charging.
| Reference | Methods used | Research results and contributions | Research gaps |
| [148] |
|
|
|
| [149] |
|
|
|
| [150] |
|
|
|
| [141] |
|
|
|
| [151] |
|
|
|
| [152] |
|
|
|
| [153] |
|
|
|
8. Future Research Directions in AR-Aided Energy Trading for Electromobility Charging Networks
Integrating AR into energy trading for electromobility charging networks presents a rich landscape for future research, encompassing various interdisciplinary domains. As this field evolves, critical areas for investigation have emerged, focussing on enhancing user experience, optimising data integration and leveraging advanced technologies to improve system efficiency and sustainability. Central to this research is the development of intuitive AR interfaces that can effectively visualise complex energy trading data and charging network information. This necessitates studies on optimising the user experience [209], reducing cognitive load [210] and enhancing decision-making processes for both individual EV users and fleet managers [209]. Concurrently, research should explore methods for seamlessly integrating real-time data from various sources into AR displays and developing efficient algorithms for data processing and visualisation that operate within mobile AR device constraints. Applying machine learning algorithms to predict energy demand, pricing and charging station availability within AR interfaces presents another promising avenue, focussing on developing accurate real-time predictive models visualised in AR environments [210]. The intersection of AR with behavioural economics and gamification offers the potential to encourage efficient energy use and trading behaviours [211]. The design of incentive structures that promote sustainable practices through AR interfaces creates a research gap in electromobility. As these systems handle sensitive data, robust security measures and privacy protection must be developed, including secure AR protocols and examination of potential vulnerabilities in AR-aided trading systems [203]. Integrating blockchain technology and smart contracts with AR interfaces for energy trading warrants exploration, potentially enhancing transparency, and automating transactions within a secure framework [212]. Further research includes investigating AR applications for grid management and load balancing, particularly in scenarios with high EV penetration [213]. Developing cross-platform AR solutions to ensure accessibility and interoperability within the electromobility ecosystem is crucial because it explores AR’s role of AR in facilitating V2G energy trading [214].
The regulatory and policy implications of AR-aided energy trading systems require thorough examination, including how existing energy market regulations may need to evolve. Environmental impact assessment methodologies for AR-aided energy trading decisions could promote more sustainable practices, focussing on creating real-time AR-based environmental impact indicators. Investigating the cognitive load associated with processing AR-presented energy-trading information while operating a vehicle is essential for ensuring safety and effectiveness [210]. These diverse research directions underscore the interdisciplinary nature of AR-aided energy trading in electromobility-charging networks. Future research efforts should prioritise collaborative approaches, combining expertise from computer science, energy systems, HCI, economics and policy, to address these complex challenges. Such interdisciplinary collaboration is crucial in realising the full potential of AR-aided energy trading systems and accelerating the transition to a more sustainable and decentralised energy future in the context of electromobility.
8.1. Actionable Recommendations for Integrating AR in Energy Trading Platforms
Industry stakeholders in the electromobility sector are encouraged to take actionable steps to enhance the integration of AR technologies into energy trading platforms. First, policymakers should allocate resources for this integration effort and collaborate closely with AR developers and experts to ensure seamless compatibility and functionality. Prioritising user-centric design is paramount, necessitating a focus on human factors and conducting thorough user research and usability testing to optimise the AR experience for end users. Addressing security and privacy concerns is crucial for maintaining trust and safeguarding sensitive data within AR-enhanced energy trading platforms. This requires establishing robust security protocols and collaborating with cybersecurity experts to identify and mitigate potential risks and vulnerabilities. Stakeholders must also remain vigilant about regulatory compliance and stay abreast of relevant laws, policies and industry standards governing AR technologies and energy trading practices. Compliance ensures legal adherence and helps mitigate legal risks, thus fostering stakeholder trust. Education and training programs should be provided to industry stakeholders to foster awareness and understanding of AR capabilities, benefits and best practices, facilitating the seamless adoption and utilisation of AR technologies within the sector.
Promoting cooperation and information exchange among research institutes, industry participants and government agencies promotes innovation and accelerates learning and development within the sector. Sharing insights, case studies and lessons learnt facilitates collective progress and growth. Continuous monitoring and evaluation of emerging technologies and trends in AR, blockchain and energy trading enables stakeholders to stay ahead of the curve. Investing in R&D initiatives allows for exploring new applications, improving existing solutions and anticipating future opportunities. Engagement with stakeholders throughout the development and implementation of AR-enhanced energy trading platforms is essential. Soliciting feedback and fostering open communication channels ensures alignment with user and industry requirements. Establishing key performance indicators (KPIs) and metrics to measure the effectiveness and impact of AR integration enables stakeholders to identify areas for improvement and optimisation. Regular assessments and evaluations maximise the benefits for all stakeholders involved. Finally, embracing a culture of adaptability and innovation enables stakeholders to effectively navigate the evolving market dynamics and technological advancements. Continuously iterating and refining AR-enhanced energy trading platforms based on user feedback, market trends and emerging opportunities ensures a competitive edge in the electromobility sector.
9. Conclusion
The integration of AR in energy trading platforms holds significant promise for enhancing the user experience within electromobility charging networks. By leveraging AR technologies, such platforms can address current challenges and limitations, provide real-time data visualisation, improve decision-making processes and foster user-centric design principles. However, the successful implementation of AR in energy trading requires careful consideration of security, privacy implications, regulatory compliance and ethical concerns. Despite the existing obstacles, ongoing research and technological advancements offer exciting opportunities for overcoming challenges and shaping the future of AR-enhanced energy trading. To maximise potential benefits, industry stakeholders should prioritise user adoption strategies, learn from case studies and collaborate on interdisciplinary efforts to drive innovation in this rapidly evolving field. Ultimately, this literature review underscores the importance of embracing AR integration to revolutionise the electromobility sector and pave the way for sustainable energy trading practices.
Although this review provides valuable insights into the integration of AR within electromobility charging networks and energy trading platforms, several limitations must be acknowledged. First, the scope of the review was restricted to publications available in English and indexed in the selected academic databases. This introduces the risk of language and publication bias, potentially excluding relevant studies from non-English or regional sources that could have provided additional perspectives, particularly from emerging markets. Second, although a systematic search strategy and inclusion/exclusion criteria were applied, the final selection of studies relied on the researchers’ interpretation of relevance, introducing the possibility of selection bias. Another limitation arises from the collection of the reviewed literature, which spans multiple domains, including AR, blockchain, peer-to-peer energy trading and electromobility. Variations in study design, methodology and reporting standards limited the ability to perform direct comparisons or meta-analyses, as such, the findings were synthesised thematically rather than quantitatively. This may have affected the generalisability of our conclusions. Given that AR in electromobility charging and energy trading is still an emerging field, much of the available literature is exploratory or conceptual. This reliance on early stage research and limited case studies raises concerns regarding the external validity and transferability of the results to large-scale real-world deployments. While the review sought to highlight user experience as a central theme, empirical studies involving end-user evaluations remain scarce. This scarcity means that conclusions regarding user adoption and interaction with AR systems are based more on projected potential than on robust user-based evidence, creating a validity risk in extrapolating user experience outcomes. Despite these limitations, this review identifies critical gaps and provides a foundation for future empirical and applied research to validate and extend the presented insights.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Idowu Adetona Ayoade: conceptualisation, methodology, investigation, data curation, writing–original draft preparation and visualisation. Omowunmi Mary Longe: conceptualisation, methodology, validation, resources, writing–review and editing, supervision, project administration and funding acquisition.
Funding
This research was funded by the National Research Foundation, South Africa, with the grant number SRUG2205025715.
Acknowledgements
This research was funded by the National Research Foundation, South Africa, with the grant number SRUG2205025715.
1 Grubert J., Langlotz T., Zollmann S., and Regenbrecht H., Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality, IEEE Transactions on Visualization and Computer Graphics. (2017) 23, no. 6, 1706–1724, https://doi.org/10.1109/tvcg.2016.2543720, 2-s2.0-85018861118.
2 Jamali S. S., Shiratuddin M. F., Wong K. W., and Oskam C. L., Utilising Mobile-Augmented Reality for Learning Human Anatomy, Procedia-Social and Behavioral Sciences. (2015) 197, 659–668, https://doi.org/10.1016/j.sbspro.2015.07.054.
3 Wang L., Sukmawarti S., and Suwanto S., The Application of Augmented Reality in Elementary School Education, Research, Society and Development. (2021) https://doi.org/10.33448/rsd-v10i3.12823.
4 Makhat A. B., Akhayeva Z. B., and Alzhanov A. K., Augmented Reality in the Life of a Modern Person, Vestnik of M. Kozybayev North Kazakhstan University. (2021) no. 2 (51), 51–58, https://doi.org/10.54596/2309-6977-2021-2-51-58.
5 Dam A., Siddiqui A., Leclerq C., and Jeon M., Extracting a Definition and Taxonomy for Audio Augmented Reality (AAR) Using Grounded Theory, Proceedings of the Human Factors and Ergonomics Society-Annual Meeting. (2022) 66, no. 1, 1220–1224, https://doi.org/10.1177/1071181322661434.
6 Kyguolienė A. and Braziulytė R., Application of Augmented Reality in Product Packaging: Challenges and Development Opportunities, Management of Organizations: Systematic Research. (2022) 88, no. 1, 85–100, https://doi.org/10.2478/mosr-2022-0014.
7 Santi G. M., Ceruti A., Liverani A., and Osti F., Augmented Reality in Industry 4.0 and Future Innovation Programs, Technologies. (2021) 9, no. 2, https://doi.org/10.3390/technologies9020033.
8 Tenh H. K. and Shiratuddin N., Components of Adaptive Augmented Reality Model for Heritage Mobile Application, International Journal of Interactive Mobile Technologies (iJIM). (2022) 16, no. 02, 17–27, https://doi.org/10.3991/ijim.v16i02.27317.
9 Nurpandi F. and Gumelar A., Augmented Reality Chemical Reaction With User-Centered Design, MATEC Web of Conferences. (2018) 218, https://doi.org/10.1051/matecconf/201821804012, 2-s2.0-85056900369.
10 Pradibta H., Nurhasan U., and Rizaldi M. D. A., Implementation of Multimarker Augmented Reality on Solar System Simulations, Matrix: Jurnal Manajemen Teknologi dan Informatika. (2021) 11, no. 23, 130–139, https://doi.org/10.31940/matrix.v3i11.130-139.
11 Вахрушев М. В., Augmented Reality to Promote and Visualize Scientific Knowledge in the RNPLS& T’s Open Archive, Science & Technology Libraries. (2020) https://doi.org/10.33186/1027-3689-2020-10-51-62.
12 Boşat M., Önder E., and Arcagök U., Augmented Reality Practices in Health Services: Literature Review, Bitlis Eren University Journal of Science and Technology. (2020) 10, no. 2, 67–72, https://doi.org/10.17678/beuscitech.817159.
13 Sun X., Shi Z., Lei G., Guo Y., and Zhu J., Analysis and Design Optimization of a Permanent Magnet Synchronous Motor for a Campus Patrol Electric Vehicle, IEEE Transactions on Vehicular Technology. (2019) 68, no. 11, 10535–10544, https://doi.org/10.1109/tvt.2019.2939794.
14 Sheng K., Dibaj M., and Akrami M., Analysing the Cost-Effectiveness of Charging Stations for Electric Vehicles in the U.K.’s Rural Areas, World Electric Vehicle Journal. (2021) 12, no. 4, https://doi.org/10.3390/wevj12040232.
15 Viziteu A., Furtună D., Robu A. et al., Smart Scheduling of Electric Vehicles Based on Reinforcement Learning, Sensors. (2022) 22, no. 10, https://doi.org/10.3390/s22103718.
16 Qureshi K. N., Alhudhaif A., Anwar R. W., Bhati S. N., and Jeon G., Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks, Big Data. (2021) 9, no. 4, 253–264, https://doi.org/10.1089/big.2020.0282.
17 Рашевська Н. and Соловйов В. М., Augmented Reality and the Prospects for Applying Its in the Training of Future Engineers, Educational Dimension. (2018) 51, 247–254, https://doi.org/10.31812/pedag.v51i0.3672.
18 Carmo M. B., Cláudio A. P., Ferreira A. et al., Augmented Reality for Support Decision on Solar Radiation Harnessing, 2016 23rd Portuguese Meeting on Computer Graphics and Interaction (EPCGI), 2016, IEEE, 1–8.
19 Karlsson I., Bernedixen J., Ng A. H. C., and Pehrsson L., Combining Augmented Reality and Simulation-Based Optimization for Decision Support in Manufacturing, 2017 Winter Simulation Conference (WSC). (2017) https://doi.org/10.1109/wsc.2017.8248108, 2-s2.0-85044511682.
20 González Izard S., Sánchez Torres R., Alonso Plaza Ó., Juanes Méndez J. A., and García-Peñalvo F. J., Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality, Sensors. (2020) 20, no. 10, https://doi.org/10.3390/s20102962.
21 Gronowski A., Arness D. C., Ng J. et al., The Impact of Virtual and Augmented Reality on Presence, User Experience and Performance of Information Visualisation, Virtual Reality. (2024) 28, no. 3, https://doi.org/10.1007/s10055-024-01032-w.
22 Gabajová G., Krajčovič M., Furmannová B., Matys M., Biňasová V., and Stárek M., Augmented Reality as a Powerful Marketing Tool, Proceedings of CBU in Economics and Business. (2021) 2, 41–47, https://doi.org/10.12955/peb.v2.253.
23 Krajcovic M., Matys M., Binasova V., and Stárek M., Augmented Reality as a Powerful Marketing Tool, Proceedings of CBU in Economics and Business. (2021) https://doi.org/10.12955/peb.v2.253.
24 Wang H., Kim B., Xie J., and Han Z., LEAF+ AIO: Edge-Assisted energy-aware Object Detection for Mobile Augmented Reality, IEEE Transactions on Mobile Computing. (2023) 22, no. 10, 5933–5948, https://doi.org/10.1109/tmc.2022.3179943.
25 Challenor J. and Ma M., A Review of Augmented Reality Applications for History Education and Heritage Visualisation, Multimodal Technologies and Interaction. (2019) 3, no. 2, https://doi.org/10.3390/mti3020039.
26 Manurung J. S., Green Tech and Human Dynamics: Transforming Indonesia’s Waste Industry With VR, AR, and Renewable Energy Innovations, International Journal of Energy Economics and Policy. (2024) 14, no. 2, 603–617, https://doi.org/10.32479/ijeep.15650.
27 Dudhee V. and Vukovic V., Integration of Building Information Modelling and Augmented Reality for Building Energy Systems Visualisation, Springer Proceedings in Energy. (2021) 83–89, https://doi.org/10.1007/978-3-030-63916-7_11.
28 Purmaissur J., Towakel P., Guness S. P., Seeam A., and Bellekens X., Augmented-Reality Computer-Vision Assisted Disaggregated Energy Monitoring and IoT Control Platform, 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC). (2018) 1–6, https://doi.org/10.1109/iconic.2018.8601199, 2-s2.0-85061802500.
29 Spinelli F., Bazco Nogueras A., and Mancuso V., Offloading Augmented Reality Tasks With Smart Energy Source-Aware Algorithms at the Edge, Proceedings of the Int’l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, 2023, 73–82, https://doi.org/10.1145/3616388.3617523.
30 Şakar G. D. and Sürücü E., Augmented Reality as Blue Ocean Strategy in Port Industry, Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi. (2018) https://doi.org/10.18613/deudfd.428196.
31 De Pace F., Manuri F., Sanna A., and Fornaro C., A Systematic Review of Augmented Reality Interfaces for Collaborative Industrial Robots, Computers & Industrial Engineering. (2020) 149, https://doi.org/10.1016/j.cie.2020.106806.
32 Chicaiza E. A., la Cruz E. I. D., and Andaluz V. H., Augmented Reality System for Training and Assistance in the Management of Industrial Equipment and Instruments, 2018, https://doi.org/10.1007/978-3-030-03801-4_59, 2-s2.0-85057212828.
33 Hbali Y., Ballihi L., Sadgal M., and Abdelaziz E. F., Face Detection for Augmented Reality Application Using Boosting-Based Techniques, International Journal of Interactive Multimedia and Artificial Intelligence. (2016) 4, no. 2, https://doi.org/10.9781/ijimai.2016.424.
34 Fayiz M., Hilmy N., Darusalam U., and Rubhasy A., Augmented Reality Sebagai Media Edukasi Sejarah Bangunan Peninggalan Kesultanan Utsmaniyah Menggunakan Metode Marker Based Tracking Dan Algoritma Fast Corner Detection, Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). (2020) 4, no. 2, https://doi.org/10.35870/jtik.v4i2.162.
35 Mahmud N. F. and Radzi M. Q. A.-N. A., Preliminary Study of Augment Reality User Experience in Fashion Industry, International Journal of Academic Research in Business and Social Sciences. (2020) 10, no. 6, https://doi.org/10.6007/ijarbss/v10-i6/7466.
36 Masiero A., Tucci G., Conti A., Fiorini L., and Vettore A., Initial Evaluation of the Potential of Smartphone Stereo-Vision in Museum Visits, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. (2019) 837–842, https://doi.org/10.5194/isprs-archives-xlii-2-w11-837-2019, 2-s2.0-85065661519.
37 Sayed M. A., Ghafouri M., Atallah R., Debbabi M., and Assi C., Grid Chaos: An Uncertainty-Conscious Robust Dynamic EV Load-Altering Attack Strategy on Power Grid Stability, Applied Energy. (2024) 363, https://doi.org/10.1016/j.apenergy.2024.122972.
38 Anand P. and Prabhu S., Analysis of Emerging Technologies of Battery Charging, Connected Cars, Autonomous Vehicles, Augmented Reality, and Block-chain in Electric Vehicles, 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference (RESEM), 2023, 1–4, https://doi.org/10.1109/RESEM57584.2023.10236247.
39 He P., Li Y., and Li N., Augmented Reality Head up Display (AR HUD) System for the Electric Vehicles (EV) Color-Blind Drivers, International Journal of Educational Technology. (2022) 53.
40 Park H. S., Park M. W., Won K. H., Kim K.-H., and Jung S. K., In-Vehicle AR-HUD System to Provide Driving-Safety Information, ETRI Journal. (December 2013) 35, no. 6, 1038–1047, https://doi.org/10.4218/etrij.13.2013.0041, 2-s2.0-84891686689.
41 Koteleva N. I., Zhukovskiy Y. L., and Valnev V., Augmented Reality Technology as a Tool to Improve the Efficiency of Maintenance and Analytics of the Operation of Electromechanical Equipment, Journal of Physics: Conference Series. (2021) 1753, no. 1, https://doi.org/10.1088/1742-6596/1753/1/012058.
42 IEA, Trends in Electric Vehicle Charging, 2024, https://www.iea.org/reports/global-ev-outlook-2024.
43 Khaleel M., Nassar Y., El-Khozondar H. J. et al., Electric Vehicles in China, Europe, and the United States: Current Trend and Market Comparison, International Journal of Electrical Engineering and Sustainability. (2024) 1–20.
44 Tongwane M. I. and Moeletsi M. E., Status of Electric Vehicles in South Africa and Their Carbon Mitigation Potential, Scientific African. (2021) 14, https://doi.org/10.1016/j.sciaf.2021.e00999.
45 Pradibta H., Augmented Reality: Daily Prayers for Preschooler Student, International Journal of Interactive Mobile Technologies (iJIM). (2018) 12, no. 1, https://doi.org/10.3991/ijim.v12i1.7269, 2-s2.0-85041344960.
46 Pavlova O., Bashta A., and Kovtoniuk M., Augmented Reality Based Information Technology for Objects 3d Models Visualization, Computer systems and information technologies. (2023) no. 1, 68–74, https://doi.org/10.31891/csit-2023-1-9.
47 Jayashree N., Mohamed Riyas M., Nagulan S., Selva Manoj J., and Vipin K. J., AR (Augmented Reality) Lens, World Journal of Advanced Research and Reviews. (2023) 18, no. 1, 299–303, https://doi.org/10.30574/wjarr.2023.18.1.0473.
48 Phursule R., Sirpor K., Virmalwar P., Zadbuke S., and Avachat P., Augmented Reality Snipping Tool, 2023 4th International Conference for Emerging Technology (INCET), 2023, 1–4, https://doi.org/10.1109/incet57972.2023.10170651.
49 Mujumdar O., Augmented Reality, International Journal for Research in Applied Science and Engineering Technology. (2022) 10, no. 12, 487–495, https://doi.org/10.22214/ijraset.2022.47902.
50 Lv Z., Wang J.-Y., Kumar N., and Lloret J., Augmented Reality, Virtual Reality & Semantic 3D Reconstruction, 2022, MDPI-Multidisciplinary Digital Publishing Institute.
51 Fombona-Pascual A., Fombona J., and Vicente R., Augmented Reality, a Review of a Way to Represent and Manipulate 3D Chemical Structures, Journal of Chemical Information and Modeling. (2022) 62, no. 8, 1863–1872, https://doi.org/10.1021/acs.jcim.1c01255.
52 Pacheco A., Sánchez K., Pariona R., Cuba N., and Larico B., Augmented Reality as an Emerging Technology to Promote Products and Services, Augmented Reality and Its Applications. (2022) .
53 Ponmalar A., Uma M., Kaviyaraj R., and V V. P., Augmented Reality in Education: an Interactive Way to Learn, 2022 1st International Conference on Computational Science and Technology (ICCST), 2022, 872–877, https://doi.org/10.1109/iccst55948.2022.10040368.
54 Dua V., Sikri A., Kaur A., and Sachdeva M., Augmented Reality in Orthodontics: the Way Ahead, International Journal of Oral Health Dentistry. (2021) 7, no. 3, 195–198, https://doi.org/10.18231/j.ijohd.2021.038.
55 Kabanda G., Chipfumbu C. T., and Chingoriwo T., A Cybersecurity Model for a Roblox-Based Metaverse Architecture Framework, British Journal of Multidisciplinary and Advanced Studies. (2022) 3, no. 2, 105–141, https://doi.org/10.37745/bjmas.2022.0048.
56 Yıldız E. P., Augmented Reality Research and Applications in Education, 2022, https://doi.org/10.5772/intechopen.99356.
57 Vaquero-Melchor D., Bernardos A. M., and Bergesio L., SARA: A Microservice-Based Architecture for Cross-Platform Collaborative Augmented Reality, Applied Sciences. (2020) 10, no. 6, https://doi.org/10.3390/app10062074.
58 Miller D. R. and Dousay T. A., Implementing Augmented Reality in the Classroom, Issues and Trends in Educational Technology. (2015) 3, no. 2, https://doi.org/10.2458/azu_itet_v3i2_miller.
59 Mouraux D., Brassinne E., Sobczak S. et al., 3D Augmented Reality Mirror Visual Feedback Therapy Applied to the Treatment of Persistent, Unilateral Upper Extremity Neuropathic Pain: A Preliminary Study, Journal of Manual & Manipulative Therapy. (2016) 25, no. 3, 137–143, https://doi.org/10.1080/10669817.2016.1176726, 2-s2.0-84978518661.
60 Šlosar L., Voelcker-Rehage C., Paravlić A. H., Abazovic E., de Bruin E. D., and Marusic U., Combining Physical and Virtual Worlds for Motor-Cognitive Training Interventions: Position Paper With Guidelines on Technology Classification in Movement-Related Research, Frontiers in Psychology. (2022) 13, https://doi.org/10.3389/fpsyg.2022.1009052.
61 Carroll J., Hopper L., Farrelly A. M., Lombard-Vance R., Bamidis P. D., and Konstantinidis E. I., A Scoping Review of Augmented/Virtual Reality Health and Wellbeing Interventions for Older Adults: Redefining Immersive Virtual Reality, Frontiers in Virtual Reality. (2021) 2, https://doi.org/10.3389/frvir.2021.655338.
62 Smith E. and Banerjee P. P., Optical Image Processing of 2-D and 3-D Objects Using Digital Holography, Practical Holography XXXVII: Displays, Materials, and Applications. (2023) https://doi.org/10.1117/12.2660594.
63 Alvarez-Marin A., Velázquez-Iturbide J. Á., and Castillo-Vergara M., Technology Acceptance of an Interactive Augmented Reality App on Resistive Circuits for Engineering Students, Electronics. (2021) 10, no. 11, https://doi.org/10.3390/electronics10111286.
64 Sandoval Pérez S., Gonzalez Lopez J. M., Villa Barba M. A. et al., On the Use of Augmented Reality to Reinforce the Learning of Power Electronics for Beginners, Electronics. (2022) https://doi.org/10.3390/electronics11030302.
65 Lham T., Jurmey P., and Tshering S., Augmented Reality as a Classroom Teaching and Learning Tool: Teachers’ and Students’ Attitude, Asian Journal of Education and Social Studies. (2020) 27–35, https://doi.org/10.9734/ajess/2020/v12i430318.
66 Saputra H. N., Augmented Reality Dalam Pembelajaran, Idealmathedu: Indonesian Digital Journal of Mathematics and Education. (2020) 7, no. 2, 92–97, https://doi.org/10.53717/idealmathedu.v7i2.228.
67 Balyk N., Grod I. M., Vasylenko Y., Shmyger G., and Oleksiuk V., The Methodology of Using Augmented Reality Technology in the Training of Future Computer Science Teachers, International Journal of Research in E-learning. (2021) 7, no. 1, 1–20, https://doi.org/10.31261/ijrel.2021.7.1.05.
68 Farella M., Arrigo M., Chiazzese G., Tosto C., Seta L., and Taibi D., Integrating xAPI in AR Applications for Positive Behaviour Intervention and Support, 2021 International Conference on Advanced Learning Technologies (ICALT). (2021) 406–408, https://doi.org/10.1109/icalt52272.2021.00129.
69 Fitriani L., Destiani D., and Muhtadillah H., A Tourism Introduction Application Using Augmented Reality, Jurnal Online Informatika. (2022) 7, no. 1, 56–61, https://doi.org/10.15575/join.v7i1.817.
70 Rahmat A. D., Kuswanto H., Wilujeng I., and Perdana R., Implementation of Mobile Augmented Reality on Physics Learning in Junior High School Students, Journal of Education and e-Learning Research. (2023) 10, no. 2, 132–140, https://doi.org/10.20448/jeelr.v10i2.4474.
71 Romero J. A., Quero-Caiza W., Sánchez J. S., and Andaluz V. H., Training Assistant for Industrial Processes Through Augmented Reality, 2019, https://doi.org/10.1145/3369255.3369295.
72 Sitko M., Wesołowski B., Adamus J., Lisiecki Ł., Piotrowska-Madej K., and Madej L., Perceptive Review of Augmented Reality Applications and Their Outlooks in the Forging Industry, Computer Methods in Material Science. (2020) 20, no. 2, https://doi.org/10.7494/cmms.2020.2.0656.
73 Lewicki W. and Drozdz W., Electromobility and Its Development Prospects in the Context of Industry 4.0: A Comparative Study of Poland and the European Union, European Research Studies Journal. (2021) no. 2B, 135–144, https://doi.org/10.35808/ersj/2207.
74 Zema T., Sulich A., and Grzesiak S., Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis, Energies. (2022) 16, no. 1, https://doi.org/10.3390/en16010032.
75 Bartłomiejczyk M., Jarzebowicz L., and Hrbáč R., Application of Traction Supply System for Charging Electric Cars, Energies. (2022) 15, no. 4, https://doi.org/10.3390/en15041448.
76 Ayoade I. A. and Longe O. M., Modelling and Analysis of Onboard Bidirectional Charger for Vehicle-to-Grid and Vehicle-to-Load in Electromobility Energy Systems, Engineering Research Express. (2025) 7, no. 3, https://doi.org/10.1088/2631-8695/adf026.
77 Ślaski P., Eco-Efficiency of the Transportation Process in the Context of Reducing Greenhouse Gas Emissions, 2023, https://doi.org/10.21203/rs.3.rs-2468050/v1.
78 Rata M., Rata G., Filote C. et al., The ElectricalVehicle Simulator for Charging Station in Mode 3 of IEC 61851-1 Standard, Energies. (2019) 13, no. 1, https://doi.org/10.3390/en13010176.
79 Tushar W., Yuen C., Mohsenian-Rad H., Saha T. K., Poor H. V., and Wood K. L., Transforming Energy Networks via Peer-to-Peer Energy Trading: The Potential of Game-Theoretic Approaches, IEEE Signal Processing Magazine. (2018) 35, no. 4, 90–111, https://doi.org/10.1109/msp.2018.2818327, 2-s2.0-85049369934.
80 Debe M., Hasan H. R., Salah K., Yaqoob I., and Jayaraman R., Blockchain-Based Energy Trading in Electric Vehicles Using an Auctioning and Reputation Scheme, IEEE Access. (2021) 9, 165542–165556, https://doi.org/10.1109/access.2021.3133958.
81 Lasla N., Al-Ammari M., Abdallah M., and Younis M., Blockchain Based Trading Platform for Electric Vehicle Charging in Smart Cities, IEEE Open Journal of Intelligent Transportation Systems. (2020) 1, 80–92, https://doi.org/10.1109/ojits.2020.3004870.
82 Silva F. C., A Ahmed M., Martinez J. M., and Kim Y.-C., Design and Implementation of a Blockchain-Based Energy Trading Platform for Electric Vehicles in Smart Campus Parking Lots, Energies. (2019) 12, no. 24, https://doi.org/10.3390/en12244814.
83 Liu H., Zhang Y., Zheng S., and Li Y., Electric Vehicle Power Trading Mechanism Based on Blockchain and Smart Contract in V2G Network, IEEE Access. (2019) 7, 160546–160558, https://doi.org/10.1109/access.2019.2951057.
84 Musleh A. S., Yao G., and Muyeen S. M., Blockchain Applications in Smart Grid–Review and Frameworks, IEEE Access. (2019) 7, 86746–86757, https://doi.org/10.1109/access.2019.2920682, 2-s2.0-85069754734.
85 Zagrajek K., Kłos M., Rasolomampionona D. D., Lewandowski M., and Pawlak K., The Novel Approach of Using Electric Vehicles as a Resource to Mitigate the Negative Effects of Power Rationing on Non-Residential Buildings, Energies. (2023) 17, no. 1, https://doi.org/10.3390/en17010018.
86 Leijon J., Santos Döhler J., Hjalmarsson J., Brandell D., Castellucci V., and Boström C., An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink, World Electric Vehicle Journal. (2024) 15, no. 4, https://doi.org/10.3390/wevj15040153.
87 Shen G. and Zhang X., A Privacy-Preserving Energy Trading Scheme in Blockchain-Based V2G Networks, 2023, https://doi.org/10.1117/12.2668092.
88 Chung H.-M., Maharjan S., Zhang Y., Eliassen F., and Strunz K., Optimal Energy Trading With Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids, IEEE Transactions on Cloud Computing. (2022) 10, no. 1, 17–30, https://doi.org/10.1109/tcc.2021.3118563.
89 Tundys B. and Wiśniewski T., Smart Mobility for Smart Cities—Electromobility Solution Analysis and Development Directions, Energies. (2023) 16, no. 4, https://doi.org/10.3390/en16041958.
90 Petrovic N. and Kocic D., Data-Driven Framework for Energy-Efficient Smart Cities, Serbian Journal of Electrical Engineering. (2020) 17, no. 1, 41–63, https://doi.org/10.2298/sjee2001041p.
91 Zhao X. and Liang G., RETRACTED: Optimizing Electric Vehicle Charging Schedules and Energy Management in Smart Grids Using an Integrated GA-GRU-RL Approach, Frontiers in Energy Research. (2023) 11, https://doi.org/10.3389/fenrg.2023.1268513.
92 Paudel A., Sampath L. P. M. I., Yang J., and Gooi H. B., Peer-to-Peer Energy Trading in Smart Grid Considering Power Losses and Network Fees, IEEE Transactions on Smart Grid. (2020) 11, no. 6, 4727–4737, https://doi.org/10.1109/tsg.2020.2997956.
93 Yap Y. H., Tan W. S., Wong J. et al., A Two‐Stage Multi Microgrids P2p Energy Trading With Motivational Game‐Theory: A Case Study in Malaysia, IET Renewable Power Generation. (2021) 15, no. 12, 2615–2628, https://doi.org/10.1049/rpg2.12205.
94 Meinke R.-J., Sun H., and Jiang J., Optimising Demand and Bid Matching in a Peer-to-Peer Energy Trading Model, ICC 2020-2020 IEEE International Conference on Communications (ICC). (2020) https://doi.org/10.1109/icc40277.2020.9148652.
95 Tushar W., Saha T. K., Yuen C. et al., A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid, 2020, https://doi.org/10.46855/2020.06.30.15.16.551968.
96 Sorin E., Bobo L., and Pinson P., Consensus-Based Approach to Peer-to-Peer Electricity Markets With Product Differentiation, IEEE Transactions on Power Systems. (2019) 34, no. 2, 994–1004, https://doi.org/10.1109/tpwrs.2018.2872880, 2-s2.0-85054010463.
97 Dossow P. and Hampel M., Synergies of Electric Vehicle Multi-Use: Analyzing the Implementation Effort for Use Case Combinations in Smart E-Mobility, Energies. (2023) https://doi.org/10.3390/en16052424.
98 Łuszczyk M., Sulich A., Siuta-Tokarska B., Zema T., and Thier A., The Development of Electromobility in the European Union: Evidence From Poland and Cross-Country Comparisons, Energies. (2021) 14, no. 24, https://doi.org/10.3390/en14248247.
99 Tucki K., Orynycz O., and Dudziak A., The Impact of the Available Infrastructure on the Electric Vehicle Market in Poland and in EU Countries, International Journal of Environmental Research and Public Health. (2022) 19, no. 24, https://doi.org/10.3390/ijerph192416783.
100 Ng C. C. and Ramasamy C., Augmented Reality Marketing in Malaysia—Future Scenarios, Social Sciences. (2018) 7, no. 11, https://doi.org/10.3390/socsci7110224, 2-s2.0-85056133227.
101 Siriwardhana Y., Porambage P., Liyanage M., and Ylianttila M., A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects, IEEE Communications Surveys & Tutorials. (2021) 23, no. 2, 1160–1192, https://doi.org/10.1109/comst.2021.3061981.
102 Jagannath J., Jagannath A., and Kumar P. S. P. V., A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges. (2022) https://doi.org/10.36227/techrxiv.17711444.
103 Morar A., Băluțoiu M.-A., Moldoveanu A., Moldoveanu F., and Butean A., CultReal—A Rapid Development Platform for AR Cultural Spaces, With Fused Localization, Sensors. (2021) 21, no. 19, https://doi.org/10.3390/s21196618.
104 Fuentes D., Correia L., Costa N., Reis A., Barroso J., and Pereira A., SAR.IoT: Secured Augmented Reality for IoT Devices Management, Sensors. (2021) 21, no. 18, https://doi.org/10.3390/s21186001.
105 Morstyn T., Teytelboym A., Hepburn C., and McCulloch M., Integrating P2P Energy Trading With Probabilistic Distribution Locational Marginal Pricing, IEEE Transactions on Smart Grid. (2020) 11, no. 4, 3095–3106, https://doi.org/10.1109/tsg.2019.2963238.
106 Morstyn T., Farrell N., Darby S., and McCulloch M., Using Peer-to-Peer Energy-Trading Platforms to Incentivize Prosumers to Form Federated Power Plants, Nature Energy. (2018) 3, no. 2, 94–101, https://doi.org/10.1038/s41560-017-0075-y, 2-s2.0-85041696096.
107 Zaman I., Hasan M. M., He M., and Giesselmann M., Design of a Peer-to-Peer Energy Trading Platform Using Multilayered Semi-Permissioned Blockchain, International Journal of Communications, Network and System Sciences. (2022) 15, no. 07, 94–110, https://doi.org/10.4236/ijcns.2022.157008.
108 Phupattanasilp P. and Tong S.-R., Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming, Sustainability. (2019) 11, no. 9, https://doi.org/10.3390/su11092658, 2-s2.0-85067007040.
109 Torbaghan S. S., Blaauwbroek N., Kuiken D. et al., A Market-Based Framework for Demand Side Flexibility Scheduling and Dispatching, Sustainable Energy, Grids and Networks. (2018) 14, 47–61, https://doi.org/10.1016/j.segan.2018.03.003, 2-s2.0-85051625272.
110 Zhang J., Hu C., Zheng C., Rui T., Shen W., and Wang B., Distributed Peer-to-Peer Electricity Trading Considering Network Loss in a Distribution System, Energies. (2019) 12, no. 22, https://doi.org/10.3390/en12224318.
111 Wang Y., Yang J., Jiang W., Sui Z., and Chen T., Research on Optimal Scheduling Decision of Multi‐Microgrids Based on Cloud Energy Storage, IET Renewable Power Generation. (2021) 16, no. 3, 581–593, https://doi.org/10.1049/rpg2.12362.
112 Du C., Qin B., Zhang S., Li C., and Guo C., Collaborative Optimal Scheduling of Integrated Energy System Considering Carbon Trading, Journal of Physics: Conference Series. (2023) 2530, no. 1, https://doi.org/10.1088/1742-6596/2530/1/012014.
113 Arun S. L., Bingi K., Vijaya Priya R., Jacob Raglend I., and Hanumantha Rao B., Novel Architecture for Transactive Energy Management Systems With Various Market Clearing Strategies, Mathematical Problems in Engineering. (2023) 2023, no. 1, https://doi.org/10.1155/2023/3979662.
114 Mediwaththe C. P. and Smith D. B., Game-Theoretic Demand-Side Management Robust to Non-Ideal Consumer Behavior in Smart Grid, 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). (2016) 702–707, https://doi.org/10.1109/isie.2016.7744975, 2-s2.0-85001090606.
115 Sendek-Matysiak E. and Łosiewicz Z., Analysis of the Development of the Electromobility Market in Poland in the Context of the Implemented Subsidies, Energies. (2021) 14, no. 1, https://doi.org/10.3390/en14010222.
116 Casteleiro-Roca J.-L., Jove E., Sánchez-Lasheras F., Méndez-Pérez J. A., Calvo-Rolle J. L., and de Cos Juez F. J., Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation, Journal of Sensors. (2017) 2017, 1–10, https://doi.org/10.1155/2017/9640546, 2-s2.0-85029795055.
117 Liberto C., Valenti G., Orchi S., Lelli M., Nigro M., and Ferrara M., The Impact of Electric Mobility Scenarios in Large Urban Areas: The Rome Case Study, IEEE Transactions on Intelligent Transportation Systems. (2018) 19, no. 11, 3540–3549, https://doi.org/10.1109/tits.2018.2832004, 2-s2.0-85047015778.
118 Grzesiak S. and Sulich A., Car Engines Comparative Analysis: Sustainable Approach, Energies. (2022) 15, no. 14, https://doi.org/10.3390/en15145170.
119 Fanti M. P., Pedroncelli G., Roccotelli M., Mininel S., Stecco G., and Ukovich W., Actors Interactions and Needs in the European Electromobility Network, 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). (2017) 162–167, https://doi.org/10.1109/soli.2017.8120988, 2-s2.0-85046301590.
120 Junlakarn S., Kokchang P., and Audomvongseree K., Drivers and Challenges of Peer-to-Peer Energy Trading Development in Thailand, Energies. (2022) 15, no. 3, https://doi.org/10.3390/en15031229.
121 Longe O. M., An Expository Comparison of Electric Vehicles and Internal Combustion Engine Vehicles in Africa-motivations, Challenges and Adoption Strategies, 2022 IEEE PES/IAS PowerAfrica, 2022, IEEE, 1–5.
122 Jogunola O., Adebisi B., Anoh K., Ikpehai A., Hammoudeh M., and Harris G., Multi-Commodity Optimization of Peer-to-Peer Energy Trading Resources in Smart Grid, Journal of Modern Power Systems and Clean Energy. (2022) 10, no. 1, 29–39, https://doi.org/10.35833/mpce.2020.000136.
123 Wilson C., Kerr L., Sprei F., Vrain E., and Wilson M., Potential Climate Benefits of Digital Consumer Innovations, Annual Review of Environment and Resources. (2020) 45, no. 1, 113–144, https://doi.org/10.1146/annurev-environ-012320-082424.
124 Rao B. H., Arun S. L., and Selvan M. P., Framework of Locality Electricity Trading System for Profitable Peer‐to‐Peer Power Transaction in Locality Electricity Market, Iet Smart Grid. (2020) 3, no. 3, 318–330, https://doi.org/10.1049/iet-stg.2019.0131.
125 Coutinho K., Wongthongtham P., Abu-Salih B., Abu Saleh M. A., and Khairwal N. K., Carbon Emission and Cost of Blockchain Mining in a Case of Peer-to-Peer Energy Trading, Frontiers in Built Environment. (2022) 8, https://doi.org/10.3389/fbuil.2022.945944.
126 Abu-Salih B., Wongthongtham P., Morrison G. M., Coutinho K., Al-Okaily M., and Huneiti A., Short-Term Renewable Energy Consumption and Generation Forecasting: A Case Study of Western Australia, Heliyon. (2022) 8, no. 3, https://doi.org/10.1016/j.heliyon.2022.e09152.
127 Yu T., Luo F., Pu C., Zhao Z., and Ranzi G., Dual‐Blockchain‐Based P2P Energy Trading System With an Improved Optimistic Rollup Mechanism, Iet Smart Grid. (2022) 5, no. 4, 246–259, https://doi.org/10.1049/stg2.12074.
128 Nour M., Chaves-Avila J. P., and Sanchez-Miralles A., Review of Blockchain Potential Applications in the Electricity Sector and Challenges for Large Scale Adoption, IEEE Access. (2022) 10, 47384–47418, https://doi.org/10.1109/access.2022.3171227.
129 Teng F., Zhang Q., Wang G., Liu J., and Li H., A Comprehensive Review of Energy Blockchain: Application Scenarios and Development Trends, International Journal of Energy Research. (2021) 45, no. 12, 17515–17531, https://doi.org/10.1002/er.7109.
130 Schneiders A. and Shipworth D., Community Energy Groups: Can They Shield Consumers From the Risks of Using Blockchain for Peer-to-Peer Energy Trading?, Energies. (2021) 14, no. 12, https://doi.org/10.3390/en14123569.
131 Son Y.-B., Im J.-H., Kwon H.-Y., Jeon S.-Y., and Lee M.-K., Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption, Energies. (2020) 13, no. 6, https://doi.org/10.3390/en13061321.
132 Khatoon A., Verma P., Southernwood J., Massey B., and Corcoran P., Blockchain in Energy Efficiency: Potential Applications and Benefits, Energies. (2019) 12, no. 17, https://doi.org/10.3390/en12173317, 2-s2.0-85071541390.
133 Liu J., Sun S., Chang Z. et al., Application of Blockchain in Integrated Energy System Transactions, E3S Web of Conferences. (2020) 165, https://doi.org/10.1051/e3sconf/202016501014.
134 Park L. W., Lee S., and Chang H., A Sustainable Home Energy Prosumer-Chain Methodology With Energy Tags Over the Blockchain, Sustainability. (2018) 10, no. 3, https://doi.org/10.3390/su10030658, 2-s2.0-85042688044.
135 Baashar Y., Alkawsi G., Alkahtani A. A., Hashim W., Razali R. A., and Tiong S. K., Toward Blockchain Technology in the Energy Environment, Sustainability. (2021) 13, no. 16, https://doi.org/10.3390/su13169008.
136 Morstyn T., Teytelboym A., and McCulloch M., Bilateral Contract Networks for Peer-to-Peer Energy Trading, IEEE Transactions on Smart Grid. (2019) 10, no. 2, 2026–2035, https://doi.org/10.1109/tsg.2017.2786668, 2-s2.0-85041694789.
137 Jogunola O., Ajagun A. S., Tushar W. et al., Peer-to-Peer Local Energy Market: Opportunities, Barriers, Security and Implementation Options, IEEE Access. (2024) 12, 37873–37890, https://doi.org/10.1109/access.2024.3375525.
138 Li Z., Kang J., Yu R., Ye D., Deng Q., and Zhang Y., Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things, IEEE Transactions on Industrial Informatics. (2017) https://doi.org/10.1109/tii.2017.2786307, 2-s2.0-85039778045.
139 Yang Y., Chen Y., Hu G., and Spanos C. J., Optimal Network Charge for Peer-to-Peer Energy Trading: A Grid Perspective, IEEE Transactions on Power Systems. (2023) 38, no. 3, 2398–2410, https://doi.org/10.1109/tpwrs.2022.3185585.
140 Xiong Z., Zhang D., and Wang Y., Optimal Operation of Integrated Energy Systems Considering Energy Trading and Integrated Demand Response, Energy Reports. (2024) 11, 3307–3316, https://doi.org/10.1016/j.egyr.2024.03.010.
141 Al-Sorour A., Fazeli M., Monfared M., and Fahmy A., Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System, IEEE Access. (2023) 11, 12489–12503, https://doi.org/10.1109/access.2023.3242052.
142 Tushar W., Saha T. K., Yuen C. et al., A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid, Applied Energy. (2019) 243, 10–20, https://doi.org/10.1016/j.apenergy.2019.03.111, 2-s2.0-85063580611.
143 Morstyn T., Teytelboym A., and McCulloch M., Designing Decentralized Markets for Distribution System Flexibility, IEEE Transactions on Power Systems. (2019) 34, no. 3, 2128–2139, https://doi.org/10.1109/tpwrs.2018.2886244, 2-s2.0-85058673335.
144 Deepa T., Saraswathi N., Hariprasad S., Praveen K. M. S., Elamurugan P., and Dinesh V., Design and Implementation of Blockchain Based Peer to Peer Energy Trading Platform, Journal of Physics: Conference Series. (2022) 2335, no. 1, https://doi.org/10.1088/1742-6596/2335/1/012059.
145 Maine P. K., Leke C. A., and Longe O. M., Blockchain Application in Energy Trading for Grid-Connected Prosumers, E-Prime-Advances in Electrical Engineering, Electronics and Energy. (2024) 8, https://doi.org/10.1016/j.prime.2024.100586.
146 Duy Le H., Tuan Truong V., and Bao Le L., Blockchain-Empowered Metaverse: Decentralized Crowdsourcing and Marketplace for Trading Machine Learning Data and Models, IEEE Access. (2024) 12, 68556–68572, https://doi.org/10.1109/access.2024.3401076.
147 Sharad Mangrulkar R. and Vijay Chavan P., Beyond Blockchain, Blockchain Essentials: Core Concepts and Implementations, 2024, Springer, 229–248.
148 Azim M. I., Alam M. R., Tushar W., Saha T. K., and Yuen C., A Cooperative P2P Trading Framework: Developed and Validated Through Hardware-in-Loop, IEEE Transactions on Smart Grid. (2023) 14, no. 4, 2999–3015, https://doi.org/10.1109/tsg.2022.3225520.
149 Timilsina A. and Silvestri S., P2P Energy Trading Through Prospect Theory, Differential Evolution, and Reinforcement Learning, ACM Transactions on Evolutionary Learning and Optimization. (2023) 3, no. 3, 1–22, https://doi.org/10.1145/3603148.
150 Timilsina A. and Silvestri S., P2p Energy Trading in a Smart Residential Environment With User Behavioral Modeling, 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), 2023, IEEE, 272–273.
151 Chathuranga D. W., Mudiyanselage W., and Hasan K. N., Peer-to-Peer Energy Trading: Existing Algorithms, Applications, Platforms, Challenges and Opportunities, 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022, IEEE, 1–6.
152 Ullo S. L. and Sinha G. R., Advances in Smart Environment Monitoring Systems Using Iot and Sensors, Sensors. (2020) 20, no. 11, https://doi.org/10.3390/s20113113.
153 Li M., Mohammadi J., and Kar S., A Fully Decentralized Tuning-Free Inexact Projection Method for P2P Energy Trading, arXiv Prepr. arXiv2202.06106. (2022) .
154 Saldaña G., Martín J. I., Zamora I., Asensio F. J., and Oñederra O., Analysis of the Current Electric Battery Models for Electric Vehicle Simulation, Energies. (2019) https://doi.org/10.3390/en12142750, 2-s2.0-85069600400.
155 Wołek M., Szmelter-Jarosz A., Koniak M., and Golejewska A., Transformation of Trolleybus Transport in Poland. Does in-Motion Charging (Technology) Matter?, Sustainability. (2020) 12, no. 22, https://doi.org/10.3390/su12229744.
156 Ismara K. I., Surwi F., and Thoyyibah N., Development of Augmented Reality Based Occupational Health and Safety Guidebook in Electricity Basic Laboratory, International Journal of Health Sciences. (2022) 11377–11395, https://doi.org/10.53730/ijhs.v6ns5.11025.
157 Sendari S., Jiono M., Diantoro M., Puspitasari P., Surjanto H., and Nur H., Augmented Reality for Introducing Fuel Cell as Electrochemical Energy Conversion on Vocational School, International Journal of Interactive Mobile Technologies (iJIM). (2020) 14, no. 12, https://doi.org/10.3991/ijim.v14i12.15573.
158 Videnovik M., Trajkovik V., Kiønig L. V., and Vold T., Increasing Quality of Learning Experience Using Augmented Reality Educational Games, Multimedia Tools and Applications. (2020) 79, no. 33-34, 23861–23885, https://doi.org/10.1007/s11042-020-09046-7.
159 Quandt M., Knoke B., Gorldt C., Freitag M., and Thoben K.-D., General Requirements for Industrial Augmented Reality Applications, Procedia CIRP. (2018) 72, 1130–1135, https://doi.org/10.1016/j.procir.2018.03.061, 2-s2.0-85049596566.
160 Ali W. A., Fanti M. P., Roccotelli M., and Ranieri L., A Review of Digital Twin Technology for Electric and Autonomous Vehicles, Applied Sciences. (2023) 13, no. 10, https://doi.org/10.3390/app13105871.
161 Zhou C., Qiao W., Hua J., and Chen L., Automotive Augmented Reality Head-Up Displays, Micromachines. (2024) 15, no. 4, https://doi.org/10.3390/mi15040442.
162 Lee J. H., Yanusik I., Choi Y. et al., Automotive Augmented Reality 3D Head-Up Display Based on Light-Field Rendering With Eye-Tracking, Optics Express. (2020) 28, no. 20, https://doi.org/10.1364/oe.404318.
163 Faria N. de O., Kandil D., and Gabbard J. L., Augmented Reality Head-Up Displays Effect on Drivers’ Spatial Knowledge Acquisition, Proceedings of the Human Factors and Ergonomics Society-Annual Meeting. (2019) 63, no. 1, 1486–1487, https://doi.org/10.1177/1071181319631287.
164 Mu C.-T., Lin W., and Chen C.-H., Zoomable Head-Up Display With the Integration of Holographic and Geometrical Imaging, Optics Express. (2020) 28, no. 24, https://doi.org/10.1364/oe.405789.
165 Liu Y., Dong J., Qiu Y., Yang B.-R., and Qin Z., Compact Dual-Focal Augmented Reality Head-Up Display Using a Single Picture Generation Unit With Polarization Multiplexing, Optics Express. (2023) 31, no. 22, https://doi.org/10.1364/oe.502617.
166 Yoon J. W., Spadola M., Blue R. et al., Do-It-Yourself Augmented Reality Heads-Up Display (DIY AR-HUD): A Technical Note, The International Journal of Spine Surgery. (2021) 15, no. 4, 826–833, https://doi.org/10.14444/8106.
167 Liu Y., Dong J., Shi J., Yang B.-R., and Qin Z., 34‐2: A Dual‐Focal‐Plane Augmented Reality Head‐Up Display Using Polarization Multiplexing, SID Symposium Digest of Technical Papers. (2023) 54, no. 1, 489–492, https://doi.org/10.1002/sdtp.16599.
168 Wintersberger P., Frison A.-K., Riener A., and Sawitzky T. v., Fostering User Acceptance and Trust in Fully Automated Vehicles: Evaluating the Potential of Augmented Reality, PRESENCE: Virtual and Augmented Reality. (2018) 27, no. 1, 46–62, https://doi.org/10.1162/pres_a_00320, 2-s2.0-85064052716.
169 Riegler A., Wintersberger P., Riener A., and Holzmann C., Augmented Reality Windshield Displays and Their Potential to Enhance User Experience in Automated Driving, I-Com. (2019) 18, no. 2, 127–149, https://doi.org/10.1515/icom-2018-0033, 2-s2.0-85070843099.
170 Skirnewskaja J. and Wilkinson T. D., Automotive Holographic Head‐Up Displays, Advanced Materials. (2022) 34, no. 19, https://doi.org/10.1002/adma.202110463.
171 Wang S., Charissis V., and Harisson D. K., Augmented Reality Prototype HUD for Passenger Infotainment in a Vehicular Environment, Advances in Science, Technology and Engineering Systems Journal. (2017) 2, no. 3, 634–641, https://doi.org/10.25046/aj020381, 2-s2.0-85063807635.
172 Li L., Yang Z., Zeng J., and Carlos C. Q. J., Evaluating Driver Preferences for in-Vehicle Displays During Distracted Driving Using Driving Simulators, Electronics. (2024) 13, no. 8, https://doi.org/10.3390/electronics13081428.
173 Faria N. de O. and Gabbard J. L., How Long Can a Driver (Safely) Glance at an Augmented-Reality Head-Up Display?, Proceedings of the Human Factors and Ergonomics Society-Annual Meeting. (2020) 64, no. 1, 42–46, https://doi.org/10.1177/1071181320641014.
174 Lv Z., Liu J., and Xu L., A Multi-Plane Augmented Reality Head-Up Display System Based on Volume Holographic Optical Elements With Large Area, IEEE Photonics Journal. (2021) 13, no. 5, 1–8, https://doi.org/10.1109/jphot.2021.3105670.
175 Bellet T., Paris J.-C., and Marin-Lamellet C., Difficulties Experienced by Older Drivers During Their Regular Driving and Their Expectations Towards Advanced Driving Aid Systems and Vehicle Automation, Transportation Research Part F: Traffic Psychology and Behaviour. (2018) 52, 138–163, https://doi.org/10.1016/j.trf.2017.11.014, 2-s2.0-85037531305.
176 Yamin P. A. R., Park J., and Kim H. K., In-Vehicle Human–Machine Interface Guidelines for Augmented Reality Head-Up Displays: A Review, Guideline Formulation, and Future Research Directions, Transportation Research Part F: Traffic Psychology and Behaviour. (2024) 104, 266–285, https://doi.org/10.1016/j.trf.2024.06.001.
177 Endsley T. C., Sprehn K. A., Brill R. M., Ryan K. J., Vincent E. C., and Martin J. M., Augmented Reality Design Heuristics: Designing for Dynamic Interactions, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2017, Los Angeles, CA, Sage Publications Sage CA, 2100–2104.
178 Han D.-I. D., Jung T., and Tom Dieck M. C., Translating Tourist Requirements into Mobile AR Application Engineering Through QFD, International Journal of Human-Computer Interaction. (2019) 35, no. 19, 1842–1858, https://doi.org/10.1080/10447318.2019.1574099, 2-s2.0-85062444915.
179 Shukla A., Gullapuram S. S., Katti H., Kankanhalli M., Winkler S., and Subramanian R., Recognition of Advertisement Emotions With Application to Computational Advertising, IEEE Transactions on Affective Computing. (2022) 13, no. 2, 781–792, https://doi.org/10.1109/taffc.2020.2964549.
180 Ayoade I. A. and Longe O. M., A Comprehensive Review on Smart Electromobility Charging Infrastructure, World Electric Vehicle Journal. (2024) 15, no. 7, https://doi.org/10.3390/wevj15070286.
181 Cárdenas J. F. S., Shin J. G., and Kim S. H., A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology, Sustainability. (2020) 12, no. 7, https://doi.org/10.3390/su12073030.
182 Jiang Y., Li X., Luo H., Yin S., and Kaynak O., Quo Vadis Artificial Intelligence?, Discover Artificial Intelligence. (2022) 2, no. 1, https://doi.org/10.1007/s44163-022-00022-8.
183 Davidavičienė V., Raudeliūnienė J., and Viršilaitė R., Evaluation of User Experience in Augmented Reality Mobile Applications, Journal of Business Economics and Management. (2020) 22, no. 2, 467–481, https://doi.org/10.3846/jbem.2020.13999.
184 Gao M. and Boehm-Davis D. A., Development of a Customizable Interactions Questionnaire (CIQ) for Evaluating Interactions With Objects in Augmented/Virtual Reality, Virtual Reality. (2023) 27, no. 2, 699–716, https://doi.org/10.1007/s10055-022-00678-8.
185 Hedberg H., Nouri J., Hansen P., and Rahmani R., A Systematic Review of Learning Through Mobile Augmented Reality, International Journal of Interactive Mobile Technologies (iJIM). (2018) 12, no. 3, 75–85, https://doi.org/10.3991/ijim.v12i3.8404.
186 V Alpatova M., Analysis of User Interaction With Virtual Objects in Augmented Reality Applications, E-Management. (2022) .
187 Yang S., Zhao Y., and Wang C., Research on the Influence Mechanism of Users’ Continuous Use Intention of Augmented Reality Branded Application, 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022), 2022, Atlantis Press, 168–176.
188 Wodehouse A., Loudon B., and Urquhart L., The Configuration and Experience Mapping of an Accessible VR Environment for Effective Design Reviews, Artificial Intelligence for Engineering Design, Analysis and Manufacturing. (2020) 34, no. 3, 387–400, https://doi.org/10.1017/s0890060420000293.
189 Shrestha A., Bishwokarma R., Chapagain A. et al., Peer-to-Peer Energy Trading in Micro/Mini-Grids for Local Energy Communities: A Review and Case Study of the Nepalese Electricity System, IEEE Access, 7, 131911–131928.
190 Yunuo C., Xia Z., Min Y., and Liwei T., Usability Evaluation of In-Vehicle AR-HUD Interface Applying AHP-GRA, Human-Centric Intelligent Systems. (2022) 2, no. 3–4, 124–137, https://doi.org/10.1007/s44230-022-00011-1.
191 Baig M. J. A., Iqbal M. T., Jamil M., and Khan J., Design and Implementation of an Open-Source IoT and Blockchain-Based Peer-to-Peer Energy Trading Platform Using ESP32-S2, Node-Red and, MQTT Protocol, Energy Reports. (2021) 7, 5733–5746, https://doi.org/10.1016/j.egyr.2021.08.190.
192 Lee L. H., Lin Z., Hu R. et al., When Creators Meet the Metaverse: A Survey on Computational Arts, arXiv Preprint. (2021) .
193 Urquhart L., Wodehouse A., Loudon B., and Fingland C., The Application of Generative Algorithms in Human-Centered Product Development, Applied Sciences. (2022) 12, no. 7, https://doi.org/10.3390/app12073682.
194 Kim J. C., Laine T. H., and Åhlund C., Multimodal Interaction Systems Based on Internet of Things and Augmented Reality: a Systematic Literature Review, Applied Sciences. (2021) 11, no. 4.
195 Baig M. J. A., Iqbal M. T., Jamil M., and Khan J., A low-Cost, Open-Source Peer-to-Peer Energy Trading System for a Remote Community Using the Internet-of-Things, Blockchain, and Hypertext Transfer Protocol, Energies. (2022) 15, no. 13, https://doi.org/10.3390/en15134862.
196 Tariq F., Khandaker M. R. A., Wong K.-K., Imran M. A., Bennis M., and Debbah M., A Speculative Study on 6G, IEEE Wireless Communications. (2020) 27, no. 4, 118–125, https://doi.org/10.1109/mwc.001.1900488.
197 Murshed M., An Empirical Analysis of the Non-Linear Impacts of ICT-Trade Openness on Renewable Energy Transition, Energy Efficiency, Clean Cooking Fuel Access and Environmental Sustainability in South Asia, Environmental Science & Pollution Research. (2020) 27, no. 29, 36254–36281, https://doi.org/10.1007/s11356-020-09497-3.
198 Bekele M. K., Pierdicca R., Frontoni E., Malinverni E. S., and Gain J., A Survey of Augmented, Virtual, and Mixed Reality for Cultural Heritage, Journal on Computing and Cultural Heritage. (2018) 11, no. 2, 1–36, https://doi.org/10.1145/3145534.
199 Kurniawan B. and Fadryan E., Furniture Online Shopping Using Augmented Reality, Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 2019, Bandung, Indonesia.
200 Aitzhan N. Z. and Svetinovic D., Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams, IEEE Transactions on Dependable and Secure Computing. (2018) 15, no. 5, 840–852, https://doi.org/10.1109/tdsc.2016.2616861.
201 Syed T. A., Siddiqui M. S., Abdullah H. B. et al., In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns, Sensors. (2022) 23, no. 1, https://doi.org/10.3390/s23010146.
202 Abdulsattar Jaber T., Security Risks of the Metaverse World, International Journal of Interactive Mobile Technologies (iJIM). (2022) 16, no. 13, 4–14, https://doi.org/10.3991/ijim.v16i13.33187.
203 Cowan K., Javornik A., and Jiang P., Privacy Concerns when Using Augmented Reality Face Filters? Explaining Why and when Use Avoidance Occurs, Psychology and Marketing. (2021) 38, no. 10, 1799–1813, https://doi.org/10.1002/mar.21576.
204 Samy A., Yu H., Zhang H., and Zhang G., SPETS: Secure and Privacy-Preserving Energy Trading System in Microgrid, Sensors. (2021) 21, no. 23, https://doi.org/10.3390/s21238121.
205 Aggarwal S., Kumar N., Tanwar S., and Alazab M., A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions, IEEE Access. (2021) 9, 116231–116253, https://doi.org/10.1109/access.2021.3104354.
206 Guo J., Ding X., and Wu W., An Architecture for Distributed Energies Trading in Byzantine-Based Blockchains, IEEE Transactions on Green Communications and Networking. (2022) 6, no. 2, 1216–1230, https://doi.org/10.1109/tgcn.2022.3142438.
207 Hu J., Chen Y., Ren X., Yang Y., Qian X., and Yu X., Blockchain-Enhanced Fair and Efficient Energy Trading in Industrial Internet of Things, Mobile Information Systems. (2021) 2021, 1–13, https://doi.org/10.1155/2021/7397926.
208 Gosens J., The Greening of South-South Trade: Levels, Growth, and Specialization of Trade in Clean Energy Technologies Between Countries in the Global South, Renewable Energy. (2020) 160, 931–943, https://doi.org/10.1016/j.renene.2020.06.014.
209 Futalef J.-P., Muñoz-Carpintero D., Rozas H., and Orchard M. E., An Online Decision-Making Strategy for Routing of Electric Vehicle Fleets, Information Scientist. (2023) 625, 715–737.
210 Yu X., Pan D., and Zhou Y., A Stackelberg Game-Based Peer-to-Peer Energy Trading Market With Energy Management and Pricing Mechanism: A Case Study in Guangzhou, Solar Energy. (2024) 270, https://doi.org/10.1016/j.solener.2024.112388.
211 Huseynli B., Gamification in Energy Consumption: A Model for Consumers’ Energy Saving, International Journal of Energy Economics and Policy. (2024) 14, no. 1, 312–320, https://doi.org/10.32479/ijeep.14395.
212 Chin H. H., Varbanov P. S., Wan Alwi S. R., Manan Z. A., and Martincová J. V., Blockchain-Based Concept for Total Site Heat Integration: A Pinch-Based Smart Contract Energy Management in Industrial Symbiosis, Energy. (2024) 305, https://doi.org/10.1016/j.energy.2024.132261.
213 Pan Q., Zhang M., and Zhou H., Application of Augmented Reality (Ar) Technology in Power Grid Emergency Training, Journal of Physics: Conference Series. (2021) 2074, no. 1, https://doi.org/10.1088/1742-6596/2074/1/012095.
214 Kwon D. and Park Y., Design of Secure and Efficient Authentication Protocol for Edge Computing-Based Augmented Reality Environments, Electronics. (2024) 13, no. 3, https://doi.org/10.3390/electronics13030551.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
This study explores the integration of augmented reality (AR) in energy trading platforms, focussing on user experience within electromobility charging networks. It begins by introducing AR concepts and their applications across industries. Integrating AR into energy trading platforms for electromobility charging networks presents innovative potential; however, user experiences and adoption rates remain underexplored. This review seeks to address this gap by analysing the existing AR‐aided platforms, focussing on user engagement, satisfaction and the overall effectiveness of these systems in enhancing the charging experience. The current state of electromobility charging networks is analysed, highlighting the existing energy trading paradigms and user experience challenges. Innovations in peer‐to‐peer energy sharing and trading, particularly blockchain technology, are examined along with the potential advantages of integrating AR. This review also discusses AR technologies and tools applicable to electromobility, user‐centric design principles and the impact of AR visualisation on energy trading decision‐making. This article reviews and evaluates security and privacy implications, case studies, challenges, future directions, regulatory and ethical considerations and user adoption factors, culminating in actionable recommendations for industry stakeholders.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Department of Electrical and Electronic Engineering Science, , University of Johannesburg, , Johannesburg, , , South Africa,