A Comprehensive Overview of the Development and Research of Energy Savings of Electric Wheel Loader

Abstract

Electric wheel loaders (EWLs) have emerged as a pivotal innovation in the 2020s, representing a transformative shift toward high-efficiency, low-emission construction machinery. Despite their growing technological and environmental significance, a systematic synthesis of advancements in EWL design, energy optimization, and intelligent control remains absent in the literature. To bridge this gap, this review critically evaluates over 140 studies for comparative analysis. Building on the authors’ ongoing research, this paper categorizes EWL architectures and examines breakthroughs in hydraulic systems, drivetrain configurations, and bucket dynamics optimization. A dedicated focus is placed on energy-saving strategies, including advancements in battery technology, fast-charging infrastructure, intelligent torque distribution, and data-driven modeling of shoveling and operational resistance. The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. Furthermore, this review advocates for the adoption of distributed electro-hydraulic drive systems to minimize hydraulic losses and enable efficient energy recovery during actuator control. By synthesizing these insights, this work not only highlights current technological frontiers but also proposes actionable research directions to accelerate the commercialization of intelligent, sustainable EWLs.

1. Introduction

A wheel loader (WL) is a versatile construction machinery equipment used for loading, carrying, and unloading heavy objects [1]. It finds widespread applications in various sectors, such as construction sites, ports, storage facilities, mines, and agriculture [2]. The main body of a WL is similar to a road vehicle [3] but the difference is it is typically equipped with a working unit consisting of a bucket, a boom to lift the bucket, and a tilt to rotate the bucket, as shown in Figure 1. The working unit is usually powered by a hydraulic system, and the whole WL is driven by an engine or electric motors or their combination. Therefore, the research on WLs is complex compared to that of road vehicles.

A conventional WL is powered by a diesel engine, which provides the transmission system with torque and offers power for a hydraulic pump. WLs prove suitable for various construction environments, allowing the handling of large material volumes and playing a vital role in construction and civil engineering projects. As the electrification of road vehicles achieves great success around the world on zero exhaust gas, lower noise, and powerful acceleration [4], the electric wheel loader (EWL) has received much more attention from researchers and manufactures wishing to obtain energy savings and other better performance [5]. Electrification is a promising solution for enhancing energy savings in WLs [6,7].

The history of WLs dates back to early loading devices powered by humans or animals, such as log rollers and stone wheels, which were used to move and load lightweight materials [8]. However, these early tools had limited capacity and were not efficient for heavier loads. With the advent of internal combustion engines (ICEs), WLs evolved significantly. In the late 19th and early 20th centuries, the first loaders powered by steam and ICEs were introduced [9]. These advanced machines had higher capacities and were capable of handling more demanding tasks. The mid-20th century saw further advancements with the introduction of hydraulic systems, enhancing the flexibility, precision, and overall performance of WLs [10]. During this time, their stability, capacity, and operational capabilities were also significantly improved. In the 21st century, technological advancements brought about significant improvements in WL design [11]. New powertrains, integrated electronic controls, and automation features have boosted their efficiency, while environmental concerns led to innovations such as low-emission and energy-efficient designs [12]. The latest WLs now incorporate advanced technologies, including sensors, GPS (Global Positioning System), and automated systems, enabling smarter operations and precise load handling [13]. These developments have ensured that WLs remain crucial and indispensable for modern engineering projects.

In June 2024, the New Energy Construction Machinery Conference was held in Huai’an [14], bringing together representatives from production and R&D units of excavators, loaders, and other machinery from across the country. Against the backdrop of China’s goals to achieve “dual carbon” targets and build a new development pattern of dual circulation, both the Ministry of Ecology and Environment and the Ministry of Industry and Information Technology have issued directives emphasizing the acceleration of electrification in construction machinery and the transformation of the industry towards new energy sources. With the gradual maturity of the “three-electric” (electric motor, battery, and control) technology, the manufacturing cost of electric products has steadily decreased [15,16]. As a vital piece of production equipment, EWLs are becoming increasingly economically viable compared to their fuel-powered counterparts, particularly in terms of total lifecycle costs. Furthermore, the electrification of WLs facilitates the decoupling of power between the loader drive, work equipment, and front and rear axles. With the precise control characteristics of electric motors, this technology can significantly reduce parasitic power [17] losses caused by structural limitations in traditional WLs and eliminate unnecessary power losses due to improper operation. However, there is no existing research in the literature that provides a comprehensive overview of the development and research on EWLs. Therefore, it is essential for the industry to construct a holistic framework that covers the classification of EWLs, optimal component design, and the latest advancements in batteries and charging systems, as well as drive control, energy-saving strategies, and assisted operation technologies.

The contribution of this study is a comprehensive analysis and synthesis of energy-saving technologies for EWLs. Based on the reviewed literature, several proposals are made to improve the drivetrain, bucket shoveling, and hydraulic systems, including reducing charging time by using dual charging ports, adopting multi-motor distributed drive under intelligent control strategies, and utilizing distributed electro-hydraulic cylinders to drive the working unit. These findings can serve as a valuable reference for future research in the energy-saving qualities of EWLs. The literature review pertaining to the current research is comprehensively presented and analyzed. This article is organized as follows. Section 2 introduces the typical type of WLs and the research stress of each type. Section 3 summarizes the innovation and application of mechanical components, including the hydraulic system, drivetrain, and bucket of WLs. Section 4 states the improvement of batteries and fast-charging systems. Section 5 delves into torque distribution research relevant to electric vehicles and EWLs, stressing the shoveling characteristics of EWLs. Section 6 discusses the energy-saving control of EWLs on resistance reduction, optimal control, and application of intelligent algorithms. Section 7 focuses on assisted and autonomous driving for EWLs, and Section 8 provides the discussion and recommendations.

2. Types of WLs

A WL, also known as a front-end loader, is a robust machine used primarily for loading materials or equipment with the help of a bucket [18]. It is also called a front loader, bucket loader, tipping bucket loader, or bucket-equipped loader. There are various types of WLs designed for specific tasks, and they are frequently utilized in construction sites and heavy-duty operations.

Currently, WLs operate using three primary driving modes: diesel engines [19], electric motors [20], and hybrid systems that combine both diesel engines and electric motors [21]. Each type has their own characteristics and research stress.

The typical structure of a diesel-engine-powered WL consists of a walking system and a hydraulic system, as illustrated in Figure 2. In this framework, the power of the walking system is supplied by the engine to the drive torque, transmission, final drive, and wheels, while the power of the hydraulic system is offered by an oil pump that is also driven by the diesel engine.

Despite their widespread use, diesel engine WLs face several efficiency challenges. These include excessive resistance due to suboptimal bucket design [22,23,24], limited driving force caused by low engine power [25,26,27], inefficient torque converter (T/C) performance [28,29,30], and hydraulic power losses [31,32,33]. In working sites, engine-powered WLs cannot power off the engine when the vehicle is in a stationary state, which results in excessive consumption of fuel. In addition, the frequent braking and starting operation in working processes increases the power loss. To improve the energy efficiency, the hybrid EWL [31,34,35] and pure EWL [36,37,38,39] are better selections compared to engine-powered WLs.

Another innovative type of WL is the hybrid-type loader, which combines the benefits of engines and electric motors [40]. Hybrid WLs leverage the diesel engine for high power output and long-distance operation, while the electric motor offers high torque at low speeds and energy-efficient, eco-friendly attributes. Hybrid WLs hold immense potential for applications demanding prolonged operation and stringent environmental requirements.

As an alternative type of conventional WL, the hybrid electric prototype takes the advantages of [41,42] larger drive torque, lower emissions, energy recovery in deceleration conditions, and the possibility to power off the engine in idling conditions. Hybrid WLs achieve a balance between operational efficiency and reduced fuel consumption and emissions. A L220F hybrid WL was proposed by Volvo in the year 2008 [43], and three years later, Kawasaki developed a 65Z-2 hybrid WL integrating ultra-capacitors and an electric drive assembly [44], and Hitachi (Chiyoda, Tokyo, Japan) introduced a ZW220HYB-5B series hybrid WL [45]. No matter how the technology improved, hybrid electric WLs typically adopt one of three configurations: series hybrid, parallel hybrid, or series–parallel hybrid, as presented in Figure 3, Figure 4 and Figure 5, respectively.

In the past decade, the research stress of hybrid WLs mainly focused on energy efficiency management [31,46,47]. The optimal control strategies were commonly utilized in the energy management of hybrid WLs. One is global optimization, which is suitable for fixed driving conditions [48,49], and the other is real-time optimization, which involves defining an instantaneous cost function [44,50]. Recently, genetic algorithms [51] and fuzzy algorithms [52] were widely used in hybrid earth-moving machinery.

Moreover, hybrid electric WLs and hybrid WLs are distinct terms. Since WLs use hydraulic systems for their working units, the hydraulic energy can be recovered to power the WL in conjunction with an engine. The hydraulic hybrid structure also includes series, parallel, and power-split configurations. Figure 6 illustrates one type of engine–hydraulic hybrid WL. For hydraulic hybrid WLs, studying the ratio of hybrid powertrains under various control strategies is valuable. For example, Wen et al. [31] introduced a tunable energy management method that allows the hybridization degree to be adjusted without the need for additional hardware. The combination of battery and hydraulic systems can also provide a promising approach for EWLs [53].

However, hybrid WLs suffer from complex structures and intricate control strategies required to coordinate the engine with electric motors. In contrast, a pure electric prototype of a WL, as shown in Figure 7, is simpler, easier to control, and free from exhaust pollutants produced by ICEs [55,56,57]. One key difference between a conventional WL and a pure EWL (EWL refers to pure electric wheel loader in the following text) is the power source for the hydraulic system [58], which includes the bucket, boom, and steering cylinders. In EWLs, these cylinders are powered by an electric motor rather than an ICE.

Dating back to 2010, Jin et al. [36] integrated a permanent magnet synchronous motor (PMSM) into an engine-driven WL and found that the PMSM’s efficiency reached 95% at a speed of 5 km/h under various operating conditions. Pelletier et al. [59] validated that EWLs have simpler transmission systems, resulting in 20–30% lower maintenance costs. Three-phase alternative current (AC) motors have become widely used in electric vehicles (EVs) [60,61] and are increasingly applied in EWLs [53,62].

Podgornyy, A. V. et al. [63] compared the fuel consumption and energy efficiency of diesel and electric WLs. Their findings show that the EWL achieved efficiencies of 77% and 87% under moving and hydraulic working conditions, respectively, whereas the diesel WL achieved only 25% and 28% in the same conditions. In terms of fuel consumption, the diesel WL consumed 12.4 L/h of diesel, while the EWL used 31 kWh electricity per hour. Zhang et al. [64] concluded that a hydraulic hybrid EWL achieved a 6.02% higher energy efficiency that that of an EWL and obtained 12.11% electricity energy savings. Due to the structural changes in EWLs, the research focus is increasingly shifting toward mechanical design, particularly electro-hydraulic systems; electrical improvements, including batteries, motors, and charging infrastructure; working condition characteristics; and intelligent control for enhanced operational performance.

3. Innovation and Application of Mechanical Components

3.1. The Improvement of the Hydraulic System

The hydraulic system is a critical component of a WL, providing power and control for various loader functions [65]. It comprises several key elements, including hydraulic pumps, hydraulic cylinders, hydraulic valves, hydraulic oil tanks, and hydraulic pipelines. Figure 8 illustrates the typical flow of a hydraulic system in an engine-driven WL.

The hydraulic pump converts mechanical energy into hydraulic energy, supplying pressurized oil to other components within the system. The hydraulic cylinder acts as the actuator, transforming hydraulic energy into mechanical energy to perform the loader’s tasks. The hydraulic valves regulate flow, pressure, and direction within the hydraulic system, ensuring smooth and controlled operation, and the hydraulic oil tank stores hydraulic oil and maintains a stable oil level to ensure the system operates efficiently. The hydraulic pipelines transport hydraulic oil and transmit hydraulic energy from the pump to the cylinders and other components.

Significant research has been conducted to enhance the design and performance of hydraulic systems in WLs, focusing on efficiency, precision, and operational safety.

Multi-way valve optimization: Zhang, et al. [65] proposed an optimization method for the multi-way valve structure in a digital hydraulic system using a genetic algorithm. This approach enhances the design of the valve, improving the hydraulic system’s performance and efficiency, which, in turn, boosts the overall working performance and energy efficiency of the loader.

Performance-matching design: Wang, et al. [67] developed a performance-matching optimization method for loader hydraulic systems. By applying a fluid mechanics analysis and a simulated annealing algorithm, this method optimizes the system’s working parameters, achieving better performance matching between the components and improving the overall efficiency.

Load time history analysis: Xu, et al. [68] introduced a technique to determine the load time history of loader hydraulic systems. By collecting and analyzing data under typical working conditions, the load characteristics of the hydraulic system can be understood. This information is valuable for system design, performance evaluation, and providing guidance for loader operation and maintenance.

Sway control for hydraulic loader cranes: Jensen, et al. [69] presented a method for sway control in hydraulic loader cranes with suspended loads. Using a feedback control algorithm, the control system effectively minimizes or eliminates load swing, enhancing the precision and safety of lifting operations.

Optimizing the loader’s hydraulic system enhances its operational efficiency and responsiveness. Hua, et al. [70] utilized a genetic algorithm–backpropagation (GA-BP) method to optimize the loader’s hydraulic system. The genetic algorithm searches the parameter space for the global optimal solution, while the backpropagation algorithm trains and adjusts the neural network model. This combined approach optimized the hydraulic system parameters, resulting in improved performance and higher load capacity. The optimized system demonstrated better work efficiency and enhanced load-bearing capacity.

3.2. The Focus on Drivetrain

For EWLs, the drivetrain is usually composed of electric motors, or the combination of electric motors and a diesel engine, and the transmission system. In hybrid EWLs, Lin et al. [21] integrated three motors into the drivetrain of a series hybrid WL, resulting in a 43.6% reduction in fuel consumption compared to a traditional engine-driven WL. Lin, Z. et al. [71] proposed a series hybrid powertrain for WLs, incorporating an independent electric load-sensing system, which achieved an 11.8% reduction in fuel consumption compared to current hybrid EWL models. To minus the impact of lower energy density of battery technology, Feng et al. [72] applied a mode-driven control strategy on an electric-hydraulic hybrid WL, resulting in a reduction in peak electrical power without sacrificing energy usage or vehicle operation time, surpassing the performance of the power-following strategy. However, the complexity of the hybrid drivetrain confined the promotion and updating of the EWL industry. The total sales of EWLs in China reached 9631 units from January to October in 2024 [73]. However, there is limited reporting on the sales or production of hybrid EWLs. In contrast, pure EWLs are gaining increasing popularity [74].

In full electric drive prototypes of WLs, electric motors with reducers play a unique role for the drivetrain. The use of a single motor in an EWL often prevents it from operating efficiently within the motor’s optimal speed range for extended periods, which negatively impacts the EWL’s overall range. According to the application of electric motors on EVs, the exploration of multi-motor drive systems was becoming increasingly important. Figure 9 illustrates a novel two-motor hybrid drive system [75], which integrates two distinct power sources that allows the torque–speed coupling between the two sources, where, B1 and B2 are two brakes, a is the input sun gear of Motor B, b is the planetary gear, c is the ring gear, d is the planetary gear carrier, e is the input gear of Motor A, and C1 is the clutch Figure 10 presents an architecture with a two-motor, four-speed electric drivetrain [76], which enhances the flexibility of the operational range and optimizing efficiency of electric motors, demonstrating improvements of approximately 9% in both drive efficiency and energy recovery efficiency. Furthermore, as shown in Figure 11, a dual-motor drivetrain consists of two smaller motors connected through a planetary gear mechanism [77], resulting in the double planetary gear configuration achieving 10.9% and 11.1% improvement in drive efficiency and energy recovery efficiency, respectively.

Therefore, the multi-motors applied to EWLs are a must for improving the performance and energy economy. Li et al. [62] implemented dual motors on a ZL50 type of WL and applied a PMSM, a switched reluctance motor (SRM), and an induction motor (IM) to drive the EWL under varying shoveling conditions. The test results indicate that the walking unit achieved optimal performance in SRM-drive mode, while the hydraulic system demonstrated the highest effectiveness in PMSM-drive mode. Wang et al. [78] proposed a torque distribution method based on multi-motor efficiency and achieved a maximum electricity saving of 12%. Fei et al. [79,80] designed a dual-motor drivetrain for an EWL, utilizing a PMSM for the rear drive axle and an SRM for the front axle, with nearly twice the transmission ratio of the rear. They validated that this drivetrain provides sufficient driving force and reduces tire slippage. However, the utilization of motor types and transmission ratios was insufficient in the drivetrain design. In the future, various types of motors and reducers with different transmission ratios can be attempted in a simulation platform first in the development of dual-motor drive EWLs.

3.3. Optimization Research on Buckets

The loader bucket mainly consists of the bucket body, cutting edge, teeth, hydraulic cylinder, and attachment mounting [81]. The bucket body is the primary component, typically designed in a V- or U-shape with a specific volume. The cutting edge, a metal strip at the bottom of the bucket, is used for cutting and digging materials. Some buckets also feature teeth to enhance material grip and improve digging efficiency. Hydraulic cylinders control the bucket’s lifting and tilting movements, while the attachment mounting connects the bucket to the loader’s boom. This mounting usually has an adjustable design to accommodate various loader models and sizes.

Structural optimization aims to enhance the bucket’s performance and durability through improved design and material selection. Sun, et al. [82] optimized the structure of the excavator’s rear digging bucket by applying the principle of compound digging trajectory and limiting digging force. This method enhanced mining efficiency and reduced instability during the mining process. Suryo, et al. [83] employed the finite element method (FEM) to analyze and optimize the structural design and topology of excavator bucket teeth. The analysis identified critical stress and deformation regions, and topology optimization was used to reduce weight while maintaining sufficient strength. The results show that the optimized design offered a better strength-to-weight ratio, enabling it to withstand larger working loads.

Optimizing the design of loader bucket teeth can improve excavation efficiency, extend service life, reduce energy consumption and costs, and enhance operational stability and safety. Shaikh and Mulla [84] proposed a method focusing on material selection, structural design, and weight optimization to improve the teeth for loader buckets. By optimizing these aspects, the performance of the teeth was enhanced, and weight was reduced, improving both efficiency and cost-effectiveness. Similarly, Suryo, et al. [83] used topology optimization and the FEM to refine the design of excavator bucket teeth. Their optimization improved the structural shape and layout, further enhancing performance.

4. Improvement of Batteries and Charging System

Range anxiety has long been a significant barrier to the development of pure electric mobility [85]. The limited driving range is primarily attributed to the energy density of the traction battery pack and the development of charging infrastructure [86,87]. Pure electric construction machinery, including EWLs, faces the same challenge.

According to the literature [63,88], the LiuGong 856HE EWL (Liuzhou, China), equipped with a CATL lithium iron phosphate battery pack, has a rated energy capacity of 350 kWh and a rated voltage of 580 V, with a charging time of under 75 min. There is little discussion in the literature on how to design and apply traction batteries in EWLs. The energy stored in a battery can be calculated by Equation (1), while the charging time of a battery can be expressed by Equation (2), if the charging current is assumed to be constant.

(1)$\mathrm{E}={\displaystyle \frac{U\times C}{1000}}$

(2)$\mathrm{T}={\displaystyle \frac{60C}{I\times \eta }}$

where $\mathrm{E}$ is the energy stored in the battery in kilowatt-hours (kWh), $U$ is the battery’s voltage in volts (V), $C$ is the battery’s capacity in ampere-hours (Ah), $T$ is the charging time in minutes (min), $I$ is the charging current in amperes (A), and $\eta$ is the charging efficiency, typically ranging from 0.85 to 0.95.

Fei [80] utilized a configuration of a battery pack consisting of 9 battery cases in a 3-parallel and 189-series form, using a total of 567 cells of 3.22 V nominal voltage, achieving a nominal voltage of 608 V and an energy capacity of 316 kWh. The prototype battery pack reached a capacity of 519 Ah, enabling faster charging, as indicated by Equations (1) and (2).

Unlike battery electric vehicles, battery electric construction machinery usually works in a particular scenario. It is easier to charge electricity in fixed fast-charging post in DC (direct current) mode; however, the battery charging time depends on the charging current and the capacity of battery pack [89]. Figure 12 shows a dual-port DC charging system for EWLs. This design, in conjunction with a multi-parallel battery pack, theoretically enhances charging efficiency, resulting in a more time-saving charging effect.

Currently, there is limited research on the improvement of batteries and charging systems for EWLs. Innovative designs in multi-port charging methods, battery packing technology, and management systems could serve as key research areas to extend the working hours of EWLs. However, fast charging may lead to cell imbalance and reduced battery lifespan. Therefore, an auxiliary AC charger and battery balance management control are essential to maintain the health of the battery pack.

5. Torque Distribution for EWLs

The primary tasks of a WL include loading and carrying materials, typically performed in a V-pattern work cycle [33] consisting of four stages, of which the shoveling stage is the most complex. Ref. [90] discusses the characteristics of EWL in shoveling conditions; the torque distribution affects the shoveling performance greatly. Torque distribution refers to the process of allocating the torque output from the engine or motor to different drive wheels or components of a vehicle [91]. Optimized torque distribution plays a crucial role in improving traction capacity, enhancing handling performance, ensuring vehicle stability, and optimizing energy utilization. Proper torque allocation improves the driving experience, meets the requirements of various driving conditions, and enhances safety. Due to these benefits, vehicle manufacturers and engineers prioritize the optimization of torque distribution when designing and tuning vehicle powertrains.

5.1. Single-Motor Drive Torque Distribution

Single-motor drive torque distribution involves distributing the output torque of a single motor to different actuators or drive components within a mechanical system [92].

In simple applications, a single motor transmits torque directly to different actuators via a mechanical transmission system. Asama, et al. [93] proposed a single-drive bearingless motor with a novel bearingless design, which simplifies the structure, reduces friction and mechanical losses, and increases rotational speeds and operating efficiency. The study focused on optimizing motor design and performance characteristics, demonstrating its potential for applications requiring high speed and efficiency.

In modern applications, power steering systems distribute the driving torque of a single electric motor to different drive wheels or actuators, ensuring precise steering control. An active torque vectoring control system allows independent torque control for each driving component or actuator based on vehicle requirements. This system continuously monitors real-time vehicle conditions and adjusts the driving torque accordingly to enhance handling, stability, and traction.

Jaafari and Shirazi [94] proposed an active torque vectoring method aimed at improving vehicle handling. By integrating vehicle dynamics models and optimization algorithms, this approach enables precise control of torque output for each drive component or actuator, resulting in enhanced stability and handling performance.

5.2. Dual-Motor Drive Torque Distribution

Dual-motor drive torque distribution is a technology used in some vehicles and mechanical systems to optimize the power and handling of a vehicle or mechanical system by controlling the output torque of two motors [95]. In a dual-motor drive system, each motor typically drives a separate wheel, propeller, or other power transmission.

The simplest approach, fixed distribution, assigns a predetermined torque ratio to each motor, ideal for scenarios requiring consistent power delivery, such as straight-line driving [96]. In contrast, speed-based distribution dynamically adjusts torque based on real-time vehicle speed, often integrated with vehicle stability control systems to enhance traction during acceleration or cornering [97,98]. Alternatively, torque demand allocation responds to instantaneous system requirements, balancing power delivery while minimizing wheel slip through traction control systems [99,100]. A driver-centric approach, input-based allocation, leverages the steering angle and pedal positions to infer driver intent, optimizing torque distribution via driver assistance systems for improved handling and responsiveness [101]. According to the driver’s intention and demand, the system can automatically adjust the output torque of the motor to provide better handling response and driving experience. This approach can be implemented by using a driver assistance system [101]. The most sophisticated method, model predictive control (MPC), employs predictive algorithms and dynamic models to anticipate future states, optimizing torque distribution by incorporating constraints such as terrain conditions, load variations, and energy efficiency targets [102]. These strategies collectively highlight the progression from static, rule-based methods to adaptive, data-driven systems, with MPC representing the forefront of intelligent torque management. By harmonizing stability, efficiency, and adaptability, these methodologies underscore the critical role of advanced control systems in advancing next-generation electric drivetrains.

5.3. Shoveling Features and Torque Distribution for EWLs

For engine-driven WLs, torque distribution refers to allocating torque from the engine to the drive wheels to enhance efficiency and performance [103] Hallowell and Ray [104] proposed an independent torque control system that precisely controls the torque applied to each wheel. Their experiments confirmed significant improvements in vehicle handling, stability, and traction.

The loader’s operational mechanism encompasses a multitude of components, including buckets, bulldozing blades, and related apparatus. Through strategic manipulation of the hydraulic system, diverse torques can be allocated to these components, thereby facilitating the optimization of various operational parameters. This allows for the optimization of loading and bulldozing efficiency, taking into account the distinct working conditions and material properties present in each scenario [27].

Since WL working conditions vary, real-time torque distribution adjustments are necessary [105]. Advanced electronic control systems and sensors allow for real-time monitoring and precise torque adjustments based on the operational status of WLs. Figure 13 illustrates a torque distribution strategy for shoveling operations, addressing key working features and effectively preventing critical tire slippage. This research employs hydraulic sensors to monitor the pressure in the tilt and lift cylinders, which serve as crucial parameters for controlling torque distribution [79,80]. Consequently, precise torque distribution and adjustment can be achieved through the implementation of suitable algorithms and control strategies.

6. Energy-Saving Control of EWLs

Energy-saving control strategies aim to reduce energy consumption and enhance efficiency in WLs [106]. A battery EWL converts electrical energy from the battery into mechanical energy for the walking unit and hydraulic energy for the working unit. Figure 14 illustrates the energy flow of a typical EWL. The mechanical energy is consumed for propelling the vehicle forward or backward, as well as for climbing sloped terrains. The hydraulic energy is used for tasks such as shoveling material piles, dumping materials, and vehicle steering. Additionally, electrical appliances, including the air conditioner and lighting, consume electrical energy. Therefore, achieving energy savings primarily involves reducing resistance forces in both driving and shoveling conditions, enhancing the energy recovery ratio and, more importantly, designing effective strategies and applying intelligent control algorithms.

6.1. Reducing Resistance

According to drive theory and shoveling theory, rolling resistance and shoveling resistance are the primary energy-consuming factors in walking and shoveling conditions, respectively, if acceleration and slope conditions are not considered [90]. During the full cycle of V-type working stages, the resistance between the tires and the ground during steering consumes significant energy in articulated WLs [107], which are commonly used in EWLs. Figure 15 illustrates the forces acting on an articulated WL. Rolling resistance is determined by the vehicle’s total weight and the rolling coefficient, while wind resistance has a negligible effect on EWLs operating at very low speeds [108]. Therefore, an effective approach is to minimize bucket resistance, both in the horizontal direction (${\mathrm{F}}_{\mathrm{S}\mathrm{h}\_\mathrm{x}}$) and the vertical orientation (${\mathrm{F}}_{\mathrm{S}\mathrm{h}\_\mathrm{z}}$), as excessive resistance can lead to wheel slippage and hydraulic energy loss [109].

Horizontal shoveling resistance is regarded as insertion resistance $F_{in}$, which is expressed by Equation (3) and is influenced by the total coefficient $\Lambda$, the width of the bucket, and the depth of insertion into the material pile [110]. The total coefficient $\Lambda$ represents the resultant of multiple interacting factors, including bucket geometry, pile configuration, material type and height, and granular looseness.

(3)$F_{in}=\Lambda ·W_B·D^{1.25}$

In the early 1990s, multi-coefficient calculations were applied to the study of insertion force in China [111], leading to improvements in the equation. Zeng et al. [112] identified five forces acting on the bucket during the shoveling process and analyzed these forces in four stages: the initial horizontal insertion, later horizontal insertion, initial shoveling, and later shoveling stages. Traditional analysis methods relied heavily on extensive field tests, but the advent of simulation software has simplified the analysis of complex force changes. Coetzee et al. [23] applied the discrete element method (DEM) simulation model to predict the shoveling resistance of an excavator bucket. Helgesson [113] used EDEM software to study a bucket design’s impact on resistance. Furthermore, Feng et al. [114] employed the EDEM–RecurDyn bidirectional coupling simulation to study shoveling resistance and confirmed its consistency with test data. Other researchers [115] examined how the shape of the cutting edge influences the stress on the loader bucket. Another effective method for reducing resistance is to disrupt the structure of material blocks, thereby achieving a more loosened state. This can be accomplished by coordinating the hydraulic working system with the loader’s walking system. However, the existing literature has mainly focused on force analysis and structural enhancement measures for the loader bucket, with limited research addressing improvements in driving modes or control strategies.

6.2. Optimized Control Strategies

As an integrated system combining electric power, mechanical movement, and hydraulic transmission, improving the energy efficiency of a WL requires more than just analyzing the driving and shoveling forces. A comprehensive control strategy is necessary to reduce energy consumption and enhance performance. Most of the studies related to reducing insertion resistance were achieved by complex control systems or by attaching multiple components to the working mechanism. Zhang [116] introduced a method to minimize the insertion resistance by adjusting the boom’s motion posture when the loader’s driving force is insufficient to counteract ground resistance. Figure 16 illustrates the control strategy diagram for reducing insertion resistance.

The disadvantage of the study above is that occasional driver misoperation of the accelerator pedal can lead to misjudgment by the control system. To address this, Lu et al. [117] proposed an intelligent control approach that optimizes the hydraulic system to reduce transient shoveling resistance with an additional condition being required: the control system is triggered when the wheel speed difference is maintained for a certain period, as depicted in Figure 17.

The strategies above are based on an electrohydraulic ratio control system and an electric load-sensing hydraulic system for engine-driven WLs. However, they can also be applied to EWLs to enhance energy-saving efficiency, as electric motors deliver high torque at lower speeds.

For hybrid WLs, Wu et al. [118] and Wang et al. [72] adopted a mode-driven control strategy, as shown in Figure 18, to an electric hybrid WL that combines the high energy density of electric drive mode and the high power density of hydraulic drive mode. The result shows a 3% increase in the loader working hours and a 30% reduction in the peak power of the electric drive system. However, the authors have not tested the result in the real world.

Additionally, series hybrid EWLs [119] and parallel EWLs [44] have been studied extensively with various control strategies over long periods. However, for pure EWLs, optimized control strategies have seldom been applied [120]. In the future, research on control strategies for EWLs could focus on electric hydraulic systems powered and controlled by distributed electric hydraulic pumps, rather than multi-way hydraulic valves. The use of distributed electric hydraulic cylinders to power the boom or bucket of an EWL offers the advantage of reduced hydraulic energy loss, including along-travel and overflow losses. Figure 19 shows the diagram of a distributed electric hydraulic cylinder, which can be integrated and installed on the boom frame to drive in the lifting period and generate electricity during the downward motion. For the bucket cylinder, due to the lower energy recovery rate, the recovery configuration of the distributed electric hydraulic cylinder can be removed.

6.3. Intelligent Control Algorithms

With advances in intelligent control technologies, researchers are increasingly focusing on improving control algorithms and applying them to EWLs. A decade ago, researchers [33] utilized a model predictive control (MPC) algorithm combined with a linear quadratic regulator (LQR) for the integration of a driver model and a dynamic model of a WL, revealing that energy consumption in the V-pattern cycle is highly dependent on path planning. Cao at el. [121] constructed a regression prediction model for loader bucket displacement by combining a particle swarm optimization support vector machine (PSO-SVM) algorithm and electro-hydraulic proportional control technology.

In the field of research on torque distribution optimization for EWLs, Yang et al. [103] investigated four optimization algorithms for controlling the torque distribution of a distributed electric wheel loader (DEWL). These algorithms included the quasi-Newton–Lagrangian multiplier method, sequential quadratic programming (SQP), adaptive genetic algorithms, and particle swarm optimization. Among these, the SQP method was found to be the most effective, as demonstrated in DEWL field tests. Gao et al. [122] applied the singular value decomposition unscented Kalman filter (SVD-UKF) to estimate the shoveling load of a DEWL. By incorporating an improved particle swarm optimization (IPSO) algorithm, they achieved a 25–40% increase in longitudinal shoveling force, resulting in an overall efficiency improvement of approximately 40%.

Adaptive control techniques are designed to dynamically adjust controller parameters to accommodate system variations [123]. The adaptive proportional–integral–derivative (PID) algorithm fine-tunes proportional, integral, and derivative gains based on the WL’s operational conditions, ensuring better dynamic response and stability [121]. Liang, et al. [124] introduced a tracking control strategy for precise bulldozing trajectory control. A robust adaptive motion control method for hydraulic cranes was proposed by H. C. Pedersen et al. [125], incorporating system modeling, robust controller design, and adaptive strategies to improve precision. An adaptive torque control algorithm was developed to adjust controller output in response to real-time force or torque variations, enhancing load-handling accuracy and stability [126]. Wang, et al. [127] proposed a nonlinear adaptive control method for electro-hydraulic load systems, increasing efficiency.

Although intelligent control algorithms are widely applied in EVs, PID control remains a fundamental approach for managing WL energy efficiency. Further advancements include fuzzy PID control, which defines fuzzy rules to optimize decision-making in WL operations, and fuzzy neural network control, which enhances adaptive decision-making and improves operational efficiency. Therefore, PID-based control strategies are expected to remain a primary research focus for future EWL developments.

7. Assisted Driving of WL

Assisted-driving technologies utilize advanced technologies to aid operators in performing driving tasks, reducing workload and improving safety, comfort, and efficiency. Research in this field spans sensor technology, computer vision, machine learning, human–machine interaction, and decision-making algorithms. These innovations aim to minimize operator fatigue and reduce accident risks by providing automated support in critical driving scenarios.

7.1. Drive Anti-Slip Control

Drive anti-slip control is a critical safety technology in WLs, designed to enhance traction and stability on low-friction surfaces by dynamically adjusting operational parameters in real time [128]. This system employs wheel speed sensors to monitor individual wheel rotation rates, enabling the control unit to assess vehicle conditions and initiate corrective actions [129]. By integrating real-time data on acceleration, surface friction, and brake pressure, the system modulates braking forces to individual wheels to prevent excessive slip during deceleration [130]. Additionally, it regulates engine power output during acceleration to avoid wheel spin caused by abrupt torque delivery, ensuring optimal torque-to-traction balance [131] Advanced traction control algorithms further optimize tire grip by adjusting torque distribution and hydraulic pressure, thereby improving stability and maneuverability on challenging terrain [132].

Collectively, these functionalities mitigate risks such as skidding, loss of control, and uneven tire wear, while enhancing driver confidence and reducing operational fatigue. As a cornerstone of modern vehicle safety systems, drive anti-slip technology not only safeguards against hazardous conditions but also elevates overall performance and productivity in construction and industrial applications.

7.2. Bucket Assist Control

Bucket assist control for WLs is a sophisticated system designed to enhance the operation of loader buckets [133]. By improving operational efficiency and precision, it simplifies the completion of various loading tasks. The system incorporates sensors and an electronic control unit (ECU) to continuously monitor and regulate crucial parameters, such as bucket position, tilt angle, and movement speed [134]. This real-time information enables the system to dynamically adjust the bucket’s position and movement to align with the driver’s requirements. The automated functionalities of the system assist the driver in accomplishing multiple tasks seamlessly. For instance, when the driver intends to position the bucket precisely, the auxiliary control system automatically governs actions like lifting, lowering, and tilting based on preconfigured parameters and the driver’s input. This precise automation enables operators to handle the bucket with improved accuracy, thereby increasing loading efficiency. Furthermore, the loader bucket assist control system offers additional features and convenience. It can be equipped with a load sensor that accurately detects the weight of the material being loaded, providing the driver with valuable information regarding the load. The system can also utilize visual interfaces or audible signals to relay important details to the driver, including bucket position, movement, and load status.

Abdul Latiff, et al. [135] accomplished the successful design of an efficient and dependable loading bucket trolley through comprehensive consideration of factors like structural design, functional requirements, and performance optimization. Their design approach includes the meticulous selection and optimization of the loading bucket’s shape, size, and material to enhance loading capacity and stability. The study serves as a valuable reference and provides guidance for advancements in the field of loading bucket design. Tiwari, et al. [136] proposed a classification method leveraging machine learning and pattern recognition technology to analyze and classify the motion trajectories of excavator buckets. This approach enables more precise identification and analysis of the bucket’s motion patterns, offering valuable insights and guidance for the operation and control of excavators.

7.3. Autonomous Driving

In the future, autonomous WLs will probably be the trend for saving human labor and reducing working risks at dangerous working sites [137]. In 2009, researchers developed an autonomous WL and successfully tested it across typical working stages [138]. Over the past decade, research has mainly focused on path planning using navigation technology or mode identification [139,140,141], as well as loading operations relying on multi-sensors and neural networks [13,142,143], which are also commonly applied in the unmanned vehicle industry. However, the working characteristics of EWLs differ significantly from those of EVs. As such, the automation of the working unit, combining hydraulic control with electro-hydraulic control, presents a key research focus [66]. Research on autonomous WLs in shoveling operations [144] and assistant driving for EWLs [145] has already been conducted. Moving forward, assistant driving, based on the electrification of the working unit and walking system, will be a critical application for the development of EWLs. Additionally, achieving precise motion control and route planning under complex and extreme shoveling conditions will pose significant challenges, particularly with the heavy reliance on radar, Lidar, cameras, and inertial navigation systems.

8. Discussion and Recommendations

8.1. The Structure Improvement of EWL

From conventional WLs to hybrid WLs, electric or hydraulic drives have served as supplementary power sources to the diesel engine. The benefits of a hybrid configuration can be summarized in three key aspects.

1. The coupling of an electric motor or hydraulic motor with the engine can effectively improve performance at lower speeds, where the engine’s torque is typically lower. This is particularly advantageous because WLs usually operate at lower speeds during work cycles.

2. For engine-only driven WLs, the engine must remain running when the machine is stationary during certain work cycles (e.g., in the dumping material stage). In contrast, with a hybrid drive, the engine can be turned off, as the electric motor or hydraulic accumulator can provide sufficient torque for startup.

3. In hybrid mode, the engine can operate within its most fuel-efficient speed range to either drive or generate power, as long as optimal control strategies are in place.

In the review process of the literature, we also identified research on hybrid electric–hydraulic power systems for EWLs. The integration of such a hybrid powertrain offers significant potential for power assistance and energy recovery. However, the complex structure of hydraulic systems with load-sensing capabilities, along with energy losses from hydraulic overflow and along-travel hydraulic losses, presents a disadvantage when it comes to achieving energy-saving objectives.

Compared to conventional and hybrid WLs, the structure of pure EWLs is much simpler. Thanks to the lower-speed, high-torque characteristics of the electric motor, pure electric drive WLs can achieve sufficient torque and energy efficiency by using a reducer with a suitable transmission ratio, ensuring the motor operates within its most efficient range. However, due to the specific shoveling characteristics of WLs [90], it is advisable to apply two motors, one for the front drivetrain and one for the rear drivetrain, to generate more powerful drive torque. This, however, introduces challenges related to control complexity and torque distribution during working cycles.

For the working unit of EWLs, improvements can be made to both the bucket design and its hydraulic mechanism. The movement trajectory of the bucket tip, in conjunction with the lift and tilt cylinders, can be derived using mathematical formulas. By combining the movement trajectory with the rules governing the direction changes of the shoveling force, a bucket can be designed to ensure that the movement of the bucket tip aligns with the minimum force direction, under the intelligent control of the hydraulic cylinders. Furthermore, distributed electro-hydraulic drive systems can be applied to the boom lift and bucket tilt cylinders, which can save energy by reducing hydraulic losses.

8.2. Energy Efficiency and Working Efficiency

Hybrid EWLs exhibit higher energy efficiency compared to conventional WLs and pure EWLs. This increased efficiency arises from the hybrid drivetrain, which integrates the advantages of an engine, hydraulic pump, and electric motor. Additionally, the energy recovery during working cycles contributes to this enhanced efficiency. However, enthusiasm for hybrid EWLs in the research far exceeds their market popularity. Unlike hybrid electric vehicles (EVs), hybrid EWLs operate under severe conditions; frequent vibrations and heavy loads can lead to failures in mechanical and hydraulic systems. Since WLs are critical production tools, excessive downtime due to malfunctions can significantly inflate operational costs for users. Furthermore, hybrid EWLs are less convenient to repair compared to automobiles, and their maintenance costs remain a major concern for operators. Consequently, hybrid EWLs are less prevalent in the market than diesel-engine-driven WLs or pure EWLs.

While energy recovery in electric drive systems and hybrid systems is feasible, the recovery rate is often suboptimal. One reason for this is that WLs experience very short distances during deceleration, limiting the electricity regenerated by the drive motors. Frequent recharging of the battery pack can also reduce its cycle life. From a long-term perspective, electricity recovery through EWLs is not advisable. In contrast, hydraulic energy recovery in working cylinders, especially in boom lift cylinders, is noteworthy. As illustrated in Figure 19, when a working unit is equipped with distributed electro-hydraulic cylinders and a small displacement accumulator, the gravitational potential energy of the bucket, transitioning from a higher to a lower position, can be converted into hydraulic energy.

For pure EWLs, working efficiency is also influenced by charging time and driving range. Some manufacturers have developed long wired charging systems for the battery pack, connecting one end to an on-board charger and the other to a power source, allowing EWLs to operate at full electricity. However, this design requires a supercapacitor to store and balance the electricity. Given the frequent movements of WLs at working sites, long charging wires are prone to damage, making this charging system unsuitable for multiple EWL operations. We suggest that equipping EWLs with two fast-charging ports could significantly enhance working efficiency by reducing charging time. However, the design of the battery arrangement must be meticulously organized to facilitate the charging current. Additionally, a lack of alternative current charging for batteries can lead to a decrease in battery cycle life.

8.3. Assisted Driving for Future Exploration

The integration of assisted-driving technologies into EWLs represents a critical advancement in enhancing operational efficiency, safety, and energy sustainability. Electrification provides a foundational advantage for realizing assisted- and autonomous-driving capabilities in ELs, as electric motors and actuators enable precise, multifunctional control. When combined with advanced intelligent algorithms, these systems pave the way for progressive automation in EWLs, with assisted and fully autonomous operation becoming increasingly feasible.

Assisted anti-slip control systems prevent wheel slippage during operations, particularly on uneven or slippery terrain. By continuously monitoring wheel speed and torque distribution, these systems autonomously adjust power delivery to maintain optimal traction. Regardless of operator skill level, the technology reduces torque to slipping wheels and redistributes power to those with better grip, significantly reducing parasitic energy losses and minimizing tire wear. Unlike traditional systems reliant on manual operator input, modern anti-slip control employs sensor networks and adaptive algorithms (e.g., fuzzy logic or model predictive control) to autonomously manage slip, thereby improving both safety and productivity.

Bucket-assisted control optimizes hydraulic actuator performance during shoveling tasks, which involve both static and dynamic load conditions. A load-sensing hydraulic system, integrated with feedback mechanisms to the driving and hydraulic control units, is essential for responsive operation. Furthermore, GPS and 3D mapping technologies enable predictive shoveling path planning, which calculates the most efficient trajectory—including optimal bucket angles—to maximize material displacement. While conventional methods depend heavily on operator expertise, assisted systems leverage real-time terrain and load data to enable semi-autonomous navigation, ensuring consistency and reducing operator fatigue. This integration of path planning and bucket control represents a critical research focus for advancing EWL automation.

The efficacy of assisted-driving systems in EWLs relies on robust environmental perception enabled by radar and image recognition technologies. Cameras and radar systems generate real-time 3D maps of the surroundings, enhancing situational awareness for both operators and autonomous control modules. Additionally, intelligent algorithms—such as machine learning, fuzzy PID control, MPC, and hybrid approaches, like PSO-SVM—are pivotal for refining decision-making processes. These algorithms enable adaptive torque management, energy-efficient path optimization, and predictive maintenance, ultimately making EWLs more intelligent, easier to operate, and energy-efficient.

Despite these advancements, challenges persist in computational complexity, sensor calibration, and cost-effectiveness. Future research should prioritize lightweight algorithms for edge computing, standardized sensor fusion protocols, and cost-reduction strategies to accelerate commercial adoption.

EWLs are undergoing continuous technological innovation and commercialization to enhance product development. Table 1 synthesizes the structural characteristics, energy efficiency, and operational challenges of various configurations, providing a valuable reference for researchers, particularly those focused on energy-saving strategies in EWLs. Additionally, this table serves as a practical guide for practitioners in selecting optimal configurations based on specific working conditions.

Author Contributions

Methodology, X.F., X.Z. and M.A.A.; formal analysis, X.F.; writing—original draft, X.F. and M.A.A.; writing—review and editing, S.V.W., Q.S., Q.L. and D.W.; visualization, Z.C., X.Z. and Q.S.; supervision, S.V.W.; project administration, S.V.W.; funding acquisition, X.F. and Z.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

We extend our heartfelt appreciation to the Sudian Yingcai Engineering Project from the Jiangsu Vocational College of Electronics and Information. This is also one of the phased achievements from the industry practice program for teachers at Jiangsu Vocational College of Electronics and Information.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AC	Alternative current
AMT	Automatic manual transmission
DC	Direct current
DCT	Dual clutch transmission
DEM	Discrete element method
DEWL	Distributed electric wheel loader
ECU	Electronic control unit
EV	Electric vehicle
EWL	Electric wheel loader
FEM	Finite element method
GA-BP	Genetic algorithm–backpropagation
GPS	Global Positioning System
HMPRT	Hydraulic mechanical power reflux transmission
HV	High-voltage
ICE	Internal combustion engine
IM	Induction motor
IPSO	Improved particle swarm optimization
LQR	Linear quadratic regulator
MPC	Model predictive control
PID	Proportional–integral–derivative
PMSM	Permanent magnet synchronous motor
PSO-SVM	Particle swarm optimization support vector machine
SQP	Sequential quadratic programming
SRM	Switched reluctance motor
SVD-UKF	Singular value decomposition unscented Kalman filter
T/C	Torque converter
WL	Wheel loader

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. The working unit of a WL.

Figure 2. Structure of a diesel engine WL.

Figure 3. Series hybrid composition of WL [5].

Figure 4. Parallel hybrid composition of WL [5].

Figure 5. Series–parallel hybrid composition of WL [5].

Figure 6. A type of hydraulic hybrid WL diagram [54].

Figure 7. Typical structure of pure EWL.

Figure 8. Schematics of the power flow in an engine drive WL [66].

Figure 9. Structure of a dual-motor hybrid drivetrain.

Figure 10. Structure of two-motor, four-speed drivetrain.

Figure 11. Planetary gear drivetrain allows a single-planet set or a double-planet set.

Figure 12. A dual DC charging port design for EWLs in high-voltage circuit.

Figure 13. A control strategy of motor torque allocation of EWLs based on wheel load [80].

Figure 14. Diagram of energy flow of a battery EWL.

Figure 15. The dynamics of an articulated EWL in a three-coordinate system.

Figure 16. The strategy of insertion resistance reduction.

Figure 17. The intelligent control strategy to reduce transient insertion resistance.

Figure 18. The graphic of mode-driven control strategy for electric–hydraulic hybrid WL.

Figure 19. An electric pump-controlled hydraulic circuit structure with energy feedback function.

Table 1

A synthetic comparison of energy-saving technologies for EWLs.

Category	Advantages	Disadvantages	Key Technologies	Applications	Capabilities
Hybrid EWLs	1. Improved low-speed torque; 2. Engine-off operation during idle periods; 3. Operates within fuel-efficient range; 4. Potential for energy recovery.	1. Complex hydraulic structure with load-sensing; 2. Energy losses from hydraulic overflow; 3. Higher maintenance and repair costs; 4. Less prevalent in the market due to operational risks.	1. Hybrid electric–hydraulic powertrain; 2. Load-sensing hydraulic system; 3. Energy recovery mechanisms.	1. Work cycles with frequent stops and starts; 2. Operations requiring optimized fuel efficiency.	1. Enhances fuel efficiency and torque delivery; 2. Reduces idle fuel consumption; 3. Increases energy recovery potential.
Pure EWLs	1. Simple drivetrain structure; 2. High torque at low speeds; 3. More energy-efficient than diesel WLs; 4. Optimized torque distribution with dual-motor system.	1. Control complexity for torque distribution; 2. Limited battery life and energy recovery; 3. Charging time and infrastructure constraints.	1. Dual-motor drive system;2. Optimized transmission ratios;3. Fast-charging and supercapacitor systems	1. Heavy-load material handling; 2. High-energy-efficiency operations.	1. Achieves efficient torque control; 2. Reduces operational emissions; 3. Supports intelligent drive configurations.
Bucket and Hydraulic System Optimization	1. Enhanced energy efficiency through reduced hydraulic losses; 2. Intelligent bucket movement optimization; 3. Electro-hydraulic actuation reduces power wastage.	1. Requires precise control algorithms; 2. Increased computational demands for real-time adjustments.	1. Distributed electro-hydraulic drive; 2. Intelligent hydraulic cylinder control; 3. Optimized bucket trajectory algorithms.	1. Material handling; 2. Energy-efficient shoveling operations.	1. Improves operational efficiency; 2. Reduces hydraulic energy loss; 3. Enables precise control of bucket movement.
Energy Recovery in EWLs	1. Hydraulic energy recovery is effective in boom lift cylinders; 2. Potential for reducing energy waste.	1. Electric energy recovery is suboptimal due to short deceleration distances; 2. Battery recharge cycles limit long-term recovery.	1. Hydraulic accumulator systems; 2. Regenerative braking and energy storage.	Lifting operations with frequent load changes.	1. Converts gravitational potential energy into reusable power; 2. Reduces fuel/electricity consumption.
Charging and Power Management	1. Fast-charging ports reduce downtime; 2. Onboard wired charging enables continuous operation.	1. Long wired charging systems are prone to damage; 2. Poor alternative current charging can reduce battery life.	1. Supercapacitor-assisted fast charging; 2. Optimized battery arrangement for high current intake.	1. Continuous industrial operations; 2. Large-scale mining and construction.	Extends operational time without prolonged charging breaks.
Assisted-Driving Technologies	1. Enhanced safety and operational efficiency; 2. Reduces energy loss from unnecessary wheel slip; 3. Reduces operator fatigue through automation.	1. High computational complexity; 2. High cost of sensors and automation infrastructure.	1. Anti-slip control with sensor networks; 2. Load-sensing bucket control; 3. GPS and 3D mapping for predictive shoveling; 4. Machine-learning-based control algorithms.	1. Autonomous and semi-autonomous EWL operation; 2 Harsh terrain and precision-required tasks.	1. Improves traction and stability; 2. Enables intelligent bucket control for optimized loading; 3. Enhances real-time decision-making with AI.
Environmental Perception and Smart Control	1. Real-time situational awareness enhances safety and automation; 2. AI-driven decision-making improves efficiency.	1. High sensor calibration requirements; 2. Edge computing challenges for real-time processing.	1. Radar and image recognition; 2. AI-based decision algorithms (MPC, fuzzy PID, PSO-SVM); 3. Predictive maintenance.	Automated and AI-assisted operations.	1. Enhances safety through 3D mapping; 2. Enables adaptive energy management; 3. Supports predictive maintenance strategies.

References

1. Koszałka, G.; Dębski, H.; Dziurka, M.; Kaczor, M. Design of a frame to a semi low-loader. J. KONES; 2011; 18, pp. 215-223.

2. Wang, S.; Yin, Y.; Wu, Y.; Hou, L. Modeling and Verification of an Acquisition Strategy for Wheel Loader’s Working Trajectories and Resistance. Sensors; 2022; 22, 5993. [DOI: https://dx.doi.org/10.3390/s22165993] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36015754]

3. Parekh, D.; Poddar, N.; Rajpurkar, A.; Chahal, M.; Kumar, N.; Joshi, G.P.; Cho, W. A review on autonomous vehicles: Progress, methods and challenges. Electronics; 2022; 11, 2162. [DOI: https://dx.doi.org/10.3390/electronics11142162]

4. Ehsani, M.; Singh, K.V.; Bansal, H.O.; Mehrjardi, R.T. State of the art and trends in electric and hybrid electric vehicles. Proc. IEEE; 2021; 109, pp. 967-984. [DOI: https://dx.doi.org/10.1109/JPROC.2021.3072788]

5. Fei, X.; Han, Y.; Wong, S.V. An Overview of and Prospects for Research on Energy Savings in Wheel Loaders. Automot. Exp.; 2023; 6, pp. 133-148. [DOI: https://dx.doi.org/10.31603/ae.8759]

6. Swedes, T.A. Electrification of Diesel-Based Powertrains for Heavy Vehicles. Master’s Thesis; Purdue University Graduate School: West Lafayette, IN, USA, 2021.

7. Zhang, H.; Wang, F.; Xu, B.; Fiebig, W. Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain. Energy; 2022; 261, 125190. [DOI: https://dx.doi.org/10.1016/j.energy.2022.125190]

8. Haddock, K. Giant Earthmovers: An Illustrated History; MotorBooks International: Osceola, WI, USA, 1998.

9. Haycraft, W.R. History of construction equipment. J. Constr. Eng. Manag.; 2011; 137, pp. 720-723. [DOI: https://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000374]

10. Holian, M.; Pyeon, J.-H. Analyzing the Potential of Hybrid and Electric Off-Road Equipment in Reducing Carbon Emissions from Construction Industries. Mineta Transportation Institute. 2017: Available online: https://transweb.sjsu.edu/research/Analyzing-Potential-Hybrid-and-Electric-Road-Equipment-Reducing-Carbon-Emissions-Construction-Industries (accessed on 11 January 2025).

11. He, X.; Jiang, Y. Review of hybrid electric systems for construction machinery. Autom. Constr.; 2018; 92, pp. 286-296. [DOI: https://dx.doi.org/10.1016/j.autcon.2018.04.005]

12. Hou, L.; Lin, H.; Wang, S.; Chen, Y.; Su, D. Feature-Based sensor configuration and working-stage recognition of wheel loader. Autom. Constr.; 2022; 141, 104401. [DOI: https://dx.doi.org/10.1016/j.autcon.2022.104401]

13. Dadhich, S.; Sandin, F.; Bodin, U.; Andersson, U.; Martinsson, T. Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders. Autom. Constr.; 2019; 97, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.autcon.2018.10.013]

14. Li, J. Reviewing 2024: Electrification, Green Transformation, and Intelligentization Become the Key Development Directions of the Industry. Available online: https://www.ccm-1.com/news/6417.html (accessed on 16 November 2024).

15. Lin, T.; Lin, Y.; Ren, H.; Chen, H.; Chen, Q.; Li, Z. Development and key technologies of pure electric construction machinery. Renew. Sustain. Energy Rev.; 2020; 132, 110080. [DOI: https://dx.doi.org/10.1016/j.rser.2020.110080]

16. Chen, Y.; Ni, L.; Liu, K. Innovation efficiency and technology heterogeneity within China’s new energy vehicle industry: A two-stage NSBM approach embedded in a three-hierarchy meta-frontier framework. Energy Policy; 2022; 161, 112708. [DOI: https://dx.doi.org/10.1016/j.enpol.2021.112708]

17. Yang, Z.; Wang, J.; Han, Y. A novel real-time center of gravity estimation method for wheel loaders with front/rear-axle-independent electric driving. J. Control Sci. Eng.; 2021; 2021, 6621060. [DOI: https://dx.doi.org/10.1155/2021/6621060]

18. Madau, R.; Colombara, D.; Alexander, A.; Vacca, A.; Mazza, L. An online estimation algorithm to predict external forces acting on a front-end loader. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng.; 2021; 235, pp. 1678-1697. [DOI: https://dx.doi.org/10.1177/09596518211005583]

19. Oh, K.; Yun, S.; Ko, K.; Ha, S.; Kim, P.; Seo, J.; Yi, K. Gear ratio and shift schedule optimization of wheel loader transmission for performance and energy efficiency. Autom. Constr.; 2016; 69, pp. 89-101. [DOI: https://dx.doi.org/10.1016/j.autcon.2016.06.004]

20. Zhang, H.; Wang, F.; Lin, Z.; Xu, B. Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension. Appl. Energy; 2023; 351, 121865. [DOI: https://dx.doi.org/10.1016/j.apenergy.2023.121865]

21. Lin, Z.; Wang, F.; Xu, B. Improving Wheel Loader Energy Efficiency With a Series Electric Hybrid Powertrain. Proceedings of the BATH/ASME 2022 Symposium on Fluid Power and Motion Control; Bath, UK, 14–16 September 2022; American Society of Mechanical Engineers: New York City, NY, USA, 2022; pp. 14-16. [DOI: https://dx.doi.org/10.1115/FPMC2022-89099]

22. Yue, N.; Liu, X.H. Research on the Resistance Acting on the Bucket during Shovelling. Adv. Mater. Res.; 2013; 787, pp. 778-781. [DOI: https://dx.doi.org/10.4028/www.scientific.net/AMR.787.778]

23. Coetzee, C.J.; Els, D.N.J. The numerical modelling of excavator bucket filling using DEM. J. Terramechanics; 2009; 46, pp. 217-227. [DOI: https://dx.doi.org/10.1016/j.jterra.2009.05.003]

24. Segla, S. Kinematic analysis and optimization of a wheel loader mechanism. Manuf. Technol.; 2018; 18, pp. 309-314. [DOI: https://dx.doi.org/10.21062/ujep/97.2018/a/1213-2489/MT/18/2/309]

25. Mustayen, A.; Rasul, M.; Wang, X.; Negnevitsky, M.; Hamilton, J. Remote areas and islands power generation: A review on diesel engine performance and emission improvement techniques. Energy Convers. Manag.; 2022; 260, 115614. [DOI: https://dx.doi.org/10.1016/j.enconman.2022.115614]

26. Tan, D.; Tan, J.; Peng, D.; Fu, M.; Zhang, H.; Yin, H.; Ding, Y. Study on real-world power-based emission factors from typical construction machinery. Sci. Total Environ.; 2021; 799, 149436. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.149436] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34365269]

27. Xu, L.; Hou, X. Power matching on loader engine and hydraulic torque converter based on typical operating conditions. Trans. Chin. Soc. Agric. Eng.; 2015; 31, pp. 80-84.

28. Huang, H.; Zou, N. Test and Research on Transmission Efficiency of Torque Convertor for Loaders. Constr. Mach. Equip.; 2015; 46, pp. 7, 8 and 28–32.

29. Li, W.; Wang, A.; Li, X.; Zhang, Q. Performance optimization of the design space of torque converter’s blade angle under the condition of driving cycle. J. Harbin Eng. Univ.; 2017; 38, pp. 1781-1785.

30. Zhongping, Q. Analysis and Improvement of Bulldozing Efficiency of Wheel Loader. Equip. Manuf. Technol.; 2020; 2020, pp. 115–117+154.

31. Wen, Q.; Wang, F.; Xu, B.; Sun, Z. Improving the Fuel Efficiency of Compact Wheel Loader with a Series Hydraulic Hybrid Powertrain. IEEE Trans. Veh. Technol.; 2020; 69, pp. 10700-10709. [DOI: https://dx.doi.org/10.1109/TVT.2020.3006155]

32. Kan, Y.; Sun, D.; Luo, Y.; Ma, K.; Shi, J. Optimal design of power matching for wheel loader based on power reflux hydraulic transmission system. Mech. Mach. Theory; 2019; 137, pp. 67-82. [DOI: https://dx.doi.org/10.1016/j.mechmachtheory.2019.03.020]

33. Oh, K.; Kim, H.; Ko, K.; Kim, P.; Yi, K. Integrated wheel loader simulation model for improving performance and energy flow. Autom. Constr.; 2015; 58, pp. 129-143. [DOI: https://dx.doi.org/10.1016/j.autcon.2015.07.021]

34. Frank, B.; Pohl, J.; Palmberg, J.O. Estimation of the potential in predictive control in a hybrid wheel loader. Proceedings of the 11th Scandinavian International Conference on Fluid Power; Linköping, Sweden, 2–4 June 2009.

35. Masami, O.; Shohei, R. Hybrid in construction machinery. Proc. JFPS Int. Symp. Fluid Power; 2008; 2008, pp. 41-44. [DOI: https://dx.doi.org/10.5739/isfp.2008.41]

36. Xiaolin, J.; Laide, S.; Yongming, B. Design and research on wheel-driven systems of electric loaders. Chin. J. Constr. Mach.; 2010; 8, pp. 62–65+71. [DOI: https://dx.doi.org/10.15999/j.cnki.311926.2010.01.017]

37. Nilsson, T. Optimal Predictive Control of Wheel Loader Transmissions; Linköping University Electronic Press: Linköping, Sweden, 2015.

38. Zhou, H. Electric Wheel Loader Study on Composite Power Energy Management Strategy. Master’s Thesis; Chang’an University: Xi’an, China, 2018.

39. Pyrhönen, L. Modeling of Permanent Magnet Electric Drive in Full Electric Wheel Loader. Master’s Thesis; Aalto University: Espoo, Finland, 2020.

40. Zhang, Q.; Wang, F.; Xu, B.; Sun, Z. Online optimization of Pontryagin’s minimum principle for a series hydraulic hybrid wheel loader. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.; 2022; 236, pp. 1487-1499. [DOI: https://dx.doi.org/10.1177/09544070211039819]

41. Wang, Y.; Biswas, A.; Rodriguez, R.; Keshavarz-Motamed, Z.; Emadi, A. Hybrid electric vehicle specific engines: State-of-the-art review. Energy Rep.; 2022; 8, pp. 832-851. [DOI: https://dx.doi.org/10.1016/j.egyr.2021.11.265]

42. Liu, J.; Liang, Y.; Chen, Z.; Chen, W. Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect. Energies; 2023; 16, 3018. [DOI: https://dx.doi.org/10.3390/en16073018]

43. Filla, R. Hybrid Power Systems for Construction Machinery: Aspects of System Design and Operability of Wheel Loaders. Proceedings of the ASME 2009 International Mechanical Engineering Congress and Exposition; Lake Buena Vista, FL, USA, 13–19 November 2009; American Society of Mechanical Engineers: New York City, NY, USA, 2009; Volume 13, pp. 611-620. [DOI: https://dx.doi.org/10.1115/IMECE2009-10458]

44. Zeng, X.; Yang, N.; Peng, Y.; Zhang, Y.; Wang, J. Research on energy saving control strategy of parallel hybrid loader. Autom. Constr.; 2014; 38, pp. 100-108. [DOI: https://dx.doi.org/10.1016/j.autcon.2013.11.007]

45. Li, T.; Liu, H.; Ding, D. Predictive energy management of fuel cell supercapacitor hybrid construction equipment. Energy; 2018; 149, pp. 718-729. [DOI: https://dx.doi.org/10.1016/j.energy.2018.02.101]

46. Wang, F.; Mohd Zulkefli, M.A.; Sun, Z.; Stelson, K.A. Energy management strategy for a power-split hydraulic hybrid wheel loader. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.; 2016; 230, pp. 1105-1120. [DOI: https://dx.doi.org/10.1177/0954407015600899]

47. Shafikhani, I.; Å slund, J. Energy management of hybrid electric vehicles with battery aging considerations: Wheel loader case study. Control Eng. Pract.; 2021; 110, 104759. [DOI: https://dx.doi.org/10.1016/j.conengprac.2021.104759]

48. Karbowski, D.; Rousseau, A.; Pagerit, S.; Sharer, P. Plug-in vehicle control strategy: From global optimization to real time application. Proceedings of the 22th International Electric Vehicle Symposium (EVS22); Yokohama, Japan, 23–28 October 2006.

49. Wang, F.; Mohd Zulkefli, M.A.; Sun, Z.; Stelson, K.A. Investigation on the Energy Management Strategy for Hydraulic Hybrid Wheel Loaders. Proceedings of the ASME 2013 Dynamic Systems and Control Conference; Palo Alto, CA, USA, 21–23 October 2013; American Society of Mechanical Engineers: New York City, NY, USA, 2013; Volume 1, V001T11A005. [DOI: https://dx.doi.org/10.1115/DSCC2013-3949]

50. Salmasi, F.R. Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends. IEEE Trans. Veh. Technol.; 2007; 56, pp. 2393-2404. [DOI: https://dx.doi.org/10.1109/TVT.2007.899933]

51. Li, J.; Shu, H.; Xu, Z.; Huang, W. Control strategy of genetic algorithm for a hybrid electric container loader. Int. J. Veh. Perform.; 2021; 7, pp. 324-340. [DOI: https://dx.doi.org/10.1504/IJVP.2021.116062]

52. Lin, M.; Yu, Z.; Zhao, L.; Chen, Y. Working cycle identification–based braking control strategy and its application for hydraulic hybrid loader. Adv. Mech. Eng.; 2018; 10, pp. 1-12. [DOI: https://dx.doi.org/10.1177/1687814018773160]

53. Wang, F.; Zhang, Q.; Wen, Q.; Xu, B. Improving productivity of a battery powered electric wheel loader with electric-hydraulic hybrid drive solution. J. Clean. Prod.; 2024; 440, 140776. [DOI: https://dx.doi.org/10.1016/j.jclepro.2024.140776]

54. Wen, Q.; Wang, F.; Xu, B. Modeling and design of a series hydraulic hybrid powertrain for compact wheel loaders. Proceedings of the Fluid Power Systems Technology; Milwaukee, WI, USA, 14–16 May 2018; Volume 51968, V001T001A056.

55. Jacobs, W.; Hodkiewicz, M.R.; Bräunl, T. A cost–benefit analysis of electric loaders to reduce diesel emissions in underground hard rock mines. IEEE Trans. Ind. Appl.; 2014; 51, pp. 2565-2573. [DOI: https://dx.doi.org/10.1109/TIA.2014.2372046]

56. Silvaş, E.; Hofman, T.; Steinbuch, M. Review of optimal design strategies for hybrid electric vehicles. IFAC Proc. Vol.; 2012; 45, pp. 57-64. [DOI: https://dx.doi.org/10.3182/20121023-3-FR-4025.00054]

57. Cai, S.; Chen, Q.; Lin, T.; Xu, M.; Ren, H. Automatic shift control of an electric motor direct drive for an electric loader. Machines; 2022; 10, 403. [DOI: https://dx.doi.org/10.3390/machines10050403]

58. Dasgupta, K.; Murrenhoff, H. Modelling and dynamics of a servo-valve controlled hydraulic motor by bondgraph. Mech. Mach. Theory; 2011; 46, pp. 1016-1035. [DOI: https://dx.doi.org/10.1016/j.mechmachtheory.2010.11.006]

59. Pelletier, S.; Jabali, O.; Laporte, G. Battery Electric Vehicles for Goods Distribution: A Survey of Vehicle Technology, Market Penetration, Incentives and Practices. 2014; Available online: https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2014-43.pdf (accessed on 19 May 2016).

60. Loncarski, J.; Leijon, M.; Srndovic, M.; Rossi, C.; Grandi, G. Comparison of output current ripple in single and dual three-phase inverters for electric vehicle motor drives. Energies; 2015; 8, pp. 3832-3848. [DOI: https://dx.doi.org/10.3390/en8053832]

61. Mehbodniya, A.; Kumar, P.; Changqing, X.; Webber, J.L.; Mamodiya, U.; Halifa, A.; Srinivasulu, C. Hybrid Optimization Approach for Energy Control in Electric Vehicle Controller for Regulation of Three-Phase Induction Motors. Math. Probl. Eng.; 2022; 2022, 6096983. [DOI: https://dx.doi.org/10.1155/2022/6096983]

62. Li, X.; Duan, C.; Bai, K.; Yao, Z. Operating Performance of Pure Electric Loaders with Different Types of Motors Based on Simulation Analysis. Energies; 2021; 14, 617. [DOI: https://dx.doi.org/10.3390/en14030617]

63. Podgornyy, A.; Filatov, V.; Subbotin, B.; Smirnov, P. Comparison of the Efficiency and Energy Consumption of an Electric Wheel Loader and a Diesel Loader. Proceedings of the 2024 Systems of Signals Generating and Processing in the Field of on Board Communications; Moscow, Russia, 12–14 March 2024; pp. 1-6.

64. Zhang, Q.; Wang, F.; Cheng, M.; Xu, B. Energy efficiency improvement of battery powered wheel loader with an electric-hydrostatic hybrid powertrain. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.; 2023; 237, pp. 75-88. [DOI: https://dx.doi.org/10.1177/09544070221076887]

65. Zhang, W.; Zhou, J.; Zhang, Y.; Huang, P.; Deng, J. Energy-Saving analysis in the hydraulic system of loader working device. Int. J. Comput. Appl. Technol.; 2012; 45, pp. 106-114. [DOI: https://dx.doi.org/10.1504/IJCAT.2012.050699]

66. Yu, S.; Zhao, G.; Song, X.; Sun, Z. Control oriented model order reduction for the hydraulic system of an autonomous wheel loader. Control Eng. Pract.; 2023; 139, 105628. [DOI: https://dx.doi.org/10.1016/j.conengprac.2023.105628]

67. Wang, H.; Yang, S.; Lu, T. Performance-Matching Optimization Design of Loader-Hydraulic System Based on Hydrodynamics Analysis. Processes; 2022; 10, 1524. [DOI: https://dx.doi.org/10.3390/pr10081524]

68. Xu, L.; Ge, R.; Chang, L. Acquisition method of load time course of loader hydraulic system under typical operating conditions. Trans. Chin. Soc. Agric. Eng.; 2012; 28, pp. 57-62.

69. Jensen, K.J.; Ebbesen, M.K.; Hansen, M.R. Anti-Swing control of a hydraulic loader crane with a hanging load. Mechatronics; 2021; 77, 102599. [DOI: https://dx.doi.org/10.1016/j.mechatronics.2021.102599]

70. Hua, Z.; Lei, Z.; Hua, C. Research on Hydraulic System Optimization of Loader Based on GA-BP. Manuf. Technol.; 2019; 19, pp. 952-958. [DOI: https://dx.doi.org/10.21062/ujep/402.2019/a/1213-2489/MT/19/6/952]

71. Lin, Z.; Lin, Z.; Wang, F.; Xu, B. A series electric hybrid wheel loader powertrain with independent electric load-sensing system. Energy; 2024; 286, 129497. [DOI: https://dx.doi.org/10.1016/j.energy.2023.129497]

72. Wang, F.; Wu, Z.; Xu, B.; Fiebig, W. A mode-driven control strategy to reduce electric drive peak power of hybrid wheel loader propulsion system. IEEE Trans. Veh. Technol.; 2023; 72, pp. 5948-5961. [DOI: https://dx.doi.org/10.1109/TVT.2023.3235810]

73. Peng, B. Electric Loaders Continue to Go up 269.3% Year-over-Year in 2024. Available online: https://www.ccm-1.com/news/6274.html (accessed on 18 November 2024).

74. Downs, J. Case Study: Diesel Powered Loaders Compared to Electric Loaders in Heavy Civil Construction. Constr. Manag.; 2024; Available online: https://digitalcommons.calpoly.edu/cmsp/742/ (accessed on 11 November 2024).

75. Wang, Y.; Sun, D. Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm. Discret. Dyn. Nat. Soc.; 2014; 2014, 956521. [DOI: https://dx.doi.org/10.1155/2014/956521]

76. Holdstock, T.; Sorniotti, A.; Everitt, M.; Fracchia, M.; Bologna, S.; Bertolotto, S. Energy consumption analysis of a novel four-speed dual motor drivetrain for electric vehicles. Proceedings of the 2012 IEEE Vehicle Power and Propulsion Conference; Seoul, Republic of Korea, 9–12 October 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 295-300.

77. Xu, S.; Wei, L.; Zhang, X.; Bai, Z.; Jiao, Y. Research on Multi-Mode Drive Optimization Control Strategy of Four-Wheel-Drive Electric Vehicles with Multiple Motors. Sustainability; 2022; 14, 7378. [DOI: https://dx.doi.org/10.3390/su14127378]

78. Wang, Y.Y.; Hu, Y.; Ming, Q. Torque Distribution Control Strategy of Electric Wheel Loader with Multiple Drive Motors Based on Optimal Motor Efficiency. SAE Int. J. Commer. Veh.; 2023; 16, pp. 257-269. [DOI: https://dx.doi.org/10.4271/02-16-03-0017]

79. Fei, X.; Wong, S.V.; Azman, M.A.; Liu, P.; Han, Y. Investigation of Drive Performance of Motors in Electric Loaders with Unequal Transmission Ratios—A Case Study. World Electr. Veh. J.; 2024; 15, 459. [DOI: https://dx.doi.org/10.3390/wevj15100459]

80. Fei, X.; Han, Y.; Wong, S.V.; Azman, M.A.; Shen, W. Design and Testing of Innovative Type of Dual-Motor Drive Electric Wheel Loader. Energies; 2024; 17, 1542. [DOI: https://dx.doi.org/10.3390/en17071542]

81. Alli, O.A. Dynamic Analysis of Soil-Tool Interaction in Hydraulic Excavation. Ph.D. Thesis; University of Lagos: Lagos, Nigeria, 2011.

82. Sun, H.; Ren, Z.; Li, J.; Li, G.; Liu, W. Bucket structure optimization of backhoe hydraulic excavator based on compound digging trajectory and limit digging force. Adv. Mech. Eng.; 2022; 14, 16878132221092988. [DOI: https://dx.doi.org/10.1177/16878132221092988]

83. Suryo, S.H.; Fawwaz, W.M.; Wijaya, Y.A.; Saputro, E.W.; Harto, H. Analysis and topology optimization structural design excavator bucket tooth using finite element method. AIP Conf. Proc.; 2020; 2217, 030086.

84. Shaikh, B.P.; Mulla, A.M. Analysis of bucket teeth of backhoe excavator loader and its weight optimization. Int. J. Eng. Res. Technol.; 2015; 4, pp. 289-295.

85. Pevec, D.; Babic, J.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Ketter, W.; Podobnik, V. A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. J. Clean. Prod.; 2020; 276, 122779. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122779]

86. Song, Y.; Sun, X.; Lou, S.; Sun, F.; Wang, J. Alleviating range anxiety: Solid-state batteries and extreme fast charging. Prog. Mater. Sci.; 2024; 147, 101339. [DOI: https://dx.doi.org/10.1016/j.pmatsci.2024.101339]

87. Chakraborty, P.; Parker, R.; Hoque, T.; Cruz, J.; Du, L.; Wang, S.; Bhunia, S. Addressing the range anxiety of battery electric vehicles with charging en route. Sci. Rep.; 2022; 12, 5588. [DOI: https://dx.doi.org/10.1038/s41598-022-08942-2]

88. Karelina, M.Y.; Subbotin, B.S.; Smirnov, P.I.; Karelina, E.A. Comparative Analysis of the Efficiency of Using a Diesel and Electric Loader Taking into Account the Resource Limitations of the Battery. Proceedings of the 2024 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED); Moscow, Russia, 13–15 November 2024; IEEE: Piscataway, NJ, USA, 2024; pp. 1-5.

89. Hemavathi, S.; Shinisha, A. A study on trends and developments in electric vehicle charging technologies. J. Energy Storage; 2022; 52, 105013. [DOI: https://dx.doi.org/10.1016/j.est.2022.105013]

90. Fei, X.; Shenrui, H.; Voon, W.S.; Azman, M.A.; Yunwu, H. Mechanical Characteristics of Distributed Electric Wheel Loader in Shoveling Condition. Automot. Exp.; 2023; 6, pp. 336-358. [DOI: https://dx.doi.org/10.31603/ae.9024]

91. Yuan, X.; Wang, J. Torque distribution strategy for a front-and rear-wheel-driven electric vehicle. IEEE Trans. Veh. Technol.; 2012; 61, pp. 3365-3374. [DOI: https://dx.doi.org/10.1109/TVT.2012.2213282]

92. Dizqah, A.M.; Lenzo, B.; Sorniotti, A.; Gruber, P.; Fallah, S.; De Smet, J. A fast and parametric torque distribution strategy for four-wheel-drive energy-efficient electric vehicles. IEEE Trans. Ind. Electron.; 2016; 63, pp. 4367-4376. [DOI: https://dx.doi.org/10.1109/TIE.2016.2540584]

93. Asama, J.; Hamasaki, Y.; Oiwa, T.; Chiba, A. Proposal and analysis of a novel single-drive bearingless motor. IEEE Trans. Ind. Electron.; 2012; 60, pp. 129-138. [DOI: https://dx.doi.org/10.1109/TIE.2012.2183840]

94. Jaafari, S.M.M.; Shirazi, K.H. Integrated vehicle dynamics control via torque vectoring differential and electronic stability control to improve vehicle handling and stability performance. J. Dyn. Syst. Meas. Control; 2018; 140, 071003. [DOI: https://dx.doi.org/10.1115/1.4038657]

95. He, Q.; Yang, Y.; Luo, C.; Zhai, J.; Luo, R.; Fu, C. Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery. Energy; 2022; 248, 123543. [DOI: https://dx.doi.org/10.1016/j.energy.2022.123543]

96. Park, J.; Jeong, H.; Jang, I.G.; Hwang, S.-H. Torque distribution algorithm for an independently driven electric vehicle using a fuzzy control method. Energies; 2015; 8, pp. 8537-8561. [DOI: https://dx.doi.org/10.3390/en8088537]

97. Zhao, X.; Guo, G. Braking torque distribution for hybrid electric vehicles based on nonlinear disturbance observer. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.; 2019; 233, pp. 3327-3341. [DOI: https://dx.doi.org/10.1177/0954407018823944]

98. Wang, J.; Wang, Q.; Jin, L.; Song, C.-X. Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering. Mechatronics; 2011; 21, pp. 63-76. [DOI: https://dx.doi.org/10.1016/j.mechatronics.2010.08.005]

99. Zhang, B.; Yu, Q.; Wu, Y.; Wang, C.; Ma, S. ISIGHT-Based Optimal Analysis of Torque Distribution in the Four-wheel Drive System of Electric Vehicles. Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC); Chongqing, China, 4–6 March 2022; IEEE: Piscataway, NJ, USA, 2022; Volume 6, pp. 2031-2035.

100. Li, B.; Goodarzi, A.; Khajepour, A.; Chen, S.-k.; Litkouhi, B. An optimal torque distribution control strategy for four-independent wheel drive electric vehicles. Veh. Syst. Dyn.; 2015; 53, pp. 1172-1189. [DOI: https://dx.doi.org/10.1080/00423114.2015.1028414]

101. Asiabar, A.N.; Kazemi, R. A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control. Proc. Inst. Mech. Eng. Part K J. Multi-Body Dyn.; 2019; 233, pp. 549-567. [DOI: https://dx.doi.org/10.1177/1464419318807700]

102. Shim, T.; Adireddy, G.; Yuan, H. Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.; 2012; 226, pp. 767-778. [DOI: https://dx.doi.org/10.1177/0954407011430275]

103. Yang, Z.; Wang, J.; Gao, G.; Shi, X. Research on optimized torque-distribution control method for front/rear axle electric wheel loader. Math. Probl. Eng.; 2017; 2017, 7076583. [DOI: https://dx.doi.org/10.1155/2017/7076583]

104. Hallowell, S.J.; Ray, L.R. All-wheel driving using independent torque control of each wheel. Proceedings of the 2003 American Control Conference; Denver, CO, USA, 4–6 June 2003; IEEE: Piscataway, NJ, USA, 2003; Volume 3, pp. 2590-2595.

105. Moon, Y.; Hong, J.; Jin, S.; Bae, J.; Seo, T. Real-time UVMS torque distribution algorithm based on weighting matrix. PLoS ONE; 2021; 16, e0253771. [DOI: https://dx.doi.org/10.1371/journal.pone.0253771]

106. Madau, R.; Vacca, A.; Pintore, F. Energy Saving on a Full-Size Wheel Loader Through Variable Load Sense Margin Control. J. Dyn. Syst. Meas. Control; 2022; 144, 031003. [DOI: https://dx.doi.org/10.1115/1.4052821]

107. Li, X.; Wang, G.; Yao, Z.; Yang, Y. Research on lateral stability and rollover mechanism of articulated wheel loader. Math. Comput. Model. Dyn. Syst.; 2014; 20, pp. 248-263. [DOI: https://dx.doi.org/10.1080/13873954.2013.825871]

108. Fei, X.; Han, Y.; Wong, S.V.; Azman, M.A. Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes. World Electr. Veh. J.; 2023; 14, 298. [DOI: https://dx.doi.org/10.3390/wevj14100298]

109. Chai, S. Effect of Road Conditions on Reactive Power of Double-Axle Drive Vehicles. Trans. Chin. Soc. Agric. Eng.; 1997; 3, pp. 146-149.

110. Yang, Z.; Wang, Z.; Zhang, C. Wheel Loader; Chemical Industry Press: Beijing, China, 2005.

111. Yao, J.; Li, Z.; Peng, C. Shoveling behavior and resistance of loader bucket. Constr. Mach. Equip.; 1993; 3, pp. 9-48.

112. Zeng, Q.; Qin, S.; Zhao, T.; Wang, X. Force Analysis to Digging Procedure of a Loader Bucket. Constr. Mach. Equip.; 2011; 42, pp. 18–21+101–102.

113. Helgesson, J. Optimization of Bucket Design for Underground Loaders. Master’s Thesis; Chalmers University of Technology: Göteborg, Sweden, 2010.

114. Feng, Y.; Wu, J.; Guo, C.; Lin, B. Numerical simulation and experiment on excavating resistance of an electric cable shovel based on EDEM-RecurDyn bidirectional coupling. Machines; 2022; 10, 1203. [DOI: https://dx.doi.org/10.3390/machines10121203]

115. Lale, T.; Sarma, N.K. Analytical Study on Influence of Shape of Bucket Cutting Edge of a Wheel Loader on Load Transfer; SAE Technical Paper SAE International: Warrendale, PA, USA, 2021; pp. 0148-7191.

116. Jinglun, Z. Research on Loader Drag Reduction Strategy Based on Electro-Hydraulic Proportional Control. Master’s Thesis; Jilin University: Changchun, China, 2021.

117. Yiming, L.; Xinhui, L.; Cao, B.-w.; Wei, C.; Haomin, L. Control Strategy and Experiment on Loader Digging and Loading with Intelligent-resistance Reduction. Chin. Hydraul. Pneum.; 2022; 46, pp. 169-175.

118. Wu, Z.; Wang, F.; Xu, B.; Fiebig, W. An Electric-Hydraulic Hybrid Wheel Loader With Mode-Driven Control Strategy. American Society of Mechanical Engineers. Fluid Power Syst. Technol.; 2022; 86335, V001T001A047. [DOI: https://dx.doi.org/10.1115/FPMC2022-90715]

119. Gao, R.; Zhou, G.; Wang, Q. Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC. Energy; 2024; 295, 131011. [DOI: https://dx.doi.org/10.1016/j.energy.2024.131011]

120. Shen, W.; Han, Y.; Fei, X.; Ji, C. Energy-Saving Impact and Optimized Control Scheme of Vertical Load on Distributed Electric Wheel Loader. World Electr. Veh. J.; 2024; 15, 141. [DOI: https://dx.doi.org/10.3390/wevj15040141]

121. Cao, B.; Liu, X.; Chen, W.; Yang, K.; Tan, P. Skid-Proof operation of wheel loader based on model prediction and electro-hydraulic proportional control technology. IEEE Access; 2019; 8, pp. 81-92. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2961364]

122. Gao, G.; Wang, J.; Ma, T.; Han, Y.; Yang, X.; Li, X. Optimisation strategy of torque distribution for the distributed drive electric wheel loader based on the estimated shovelling load. Veh. Syst. Dyn.; 2022; 60, pp. 2036-2054. [DOI: https://dx.doi.org/10.1080/00423114.2021.1890153]

123. Landau, I.D.; Lozano, R.; M’Saad, M.; Karimi, A. Adaptive Control: Algorithms, Analysis and Applications; Springer Science & Business Media: Berlin, Germany, 2011.

124. Liang, G.; Liu, L.; Meng, Y.; Bai, G. Shoveling Trajectory Tracking Control of Loader Working Mechanism. Proceedings of the 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM); Ma’anshan, China, 18–20 November 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 668-673.

125. Pedersen, H.C.; Nielsen, B.; Andersen, T.O.; Hansen, M.R. Robust and Adaptive Resolved Motion Control of a Hydraulic Loader Crane. ASME Int. Mech. Eng. Congr. Expo.; 2003; 37130, pp. 505-513.

126. Johnson, K.E. Adaptive Torque Control of Variable Speed Wind Turbines; University of Colorado at Boulder: Boulder, CO, USA, 2004.

127. Wang, C.; Jiao, Z.; Wu, S.; Shang, Y. Nonlinear adaptive torque control of electro-hydraulic load system with external active motion disturbance. Mechatronics; 2014; 24, pp. 32-40. [DOI: https://dx.doi.org/10.1016/j.mechatronics.2013.11.005]

128. Foito, D.; Guerreiro, M.; Cordeiro, A. Anti-slip wheel controller drive for EV using speed and torque observers. Proceedings of the 2008 18th International Conference on Electrical Machines; Vilamoura, Portugal, 6–9 September 2008; IEEE: Piscataway, NJ, USA, 2008; pp. 1-5.

129. Gao, J.; Petovello, M.; Cannon, M. Development of precise GPS/INS/wheel speed sensor/yaw rate sensor integrated vehicular positioning system. Proceedings of the 2006 National Technical Meeting of the Institute of Navigation; Monterey, CA, USA, 18–20 January 2006; pp. 780-792.

130. Castillo, J.J.; Cabrera, J.A.; Guerra, A.J.; Simon, A. A novel electrohydraulic brake system with tire–road friction estimation and continuous brake pressure control. IEEE Trans. Ind. Electron.; 2015; 63, pp. 1863-1875. [DOI: https://dx.doi.org/10.1109/TIE.2015.2494041]

131. Fu, Q.; Zhao, L.; Sun, X.; Cheng, M.; Cai, M. Study on anti-slip regulation of quarter automobile based on PID control. Proceedings of the 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet); Yichang, China, 21–23 April 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 2111-2114.

132. Nakakuki, T.; Shen, T.; Tamura, K. Adaptive control approach to uncertain longitudinal tire slip in traction control of vehicles. Asian J. Control; 2008; 10, pp. 67-73. [DOI: https://dx.doi.org/10.1002/asjc.7]

133. Dadhich, S.; Sandin, F.; Bodin, U. Predicting bucket-filling control actions of a wheel-loader operator using a neural network ensemble. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN); Rio de Janeiro, Brazil, 8–13 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1-6.

134. Hemami, A.; Goulet, S.; Aubertin, M. Resistance of particulate media to excavation: Application to bucket loading. Int. J. Surf. Min. Reclam.; 1994; 8, pp. 125-129. [DOI: https://dx.doi.org/10.1080/09208119408964772]

135. Abdul Latiff, Z.; Saad, F.; W. Maidin, W.F.; Mazlan, M.S.F. Design and Development of a Loader Bucket Wheelbarrow. Progress in Engineering Technology III; Springer: Berlin/Heidelberg, Germany, 2021; pp. 357-369.

136. Tiwari, R.; Knowles, J.; Danko, G. Bucket trajectory classification of mining excavators. Autom. Constr.; 2013; 31, pp. 128-139. [DOI: https://dx.doi.org/10.1016/j.autcon.2012.11.006]

137. Naghshbandi, S.N.; Varga, L.; Hu, Y. Technologies for safe and resilient earthmoving operations: A systematic literature review. Autom. Constr.; 2021; 125, 103632. [DOI: https://dx.doi.org/10.1016/j.autcon.2021.103632]

138. Koyachi, N.; Sarata, S. Unmanned loading operation by autonomous wheel loader. Proceedings of the 2009 ICCAS-SICE; Fukuoka, Japan, 18–21 August 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 2221-2225.

139. Gu, R.; Marinescu, R.; Seceleanu, C.; Lundqvist, K. Formal verification of an autonomous wheel loader by model checking. Proceedings of the 6th Conference on Formal Methods in Software Engineering; Gothenburg, Sweden, 2 June 2018; pp. 74-83.

140. Shi, J.; Sun, D.; Qin, D.; Hu, M.; Kan, Y.; Ma, K.; Chen, R. Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control. Robot. Auton. Syst.; 2020; 131, 103570. [DOI: https://dx.doi.org/10.1016/j.robot.2020.103570]

141. Kim, J.-C.; Pae, D.-S.; Lim, M.-T. Obstacle avoidance path planning based on output constrained model predictive control. Int. J. Control Autom. Syst.; 2019; 17, pp. 2850-2861. [DOI: https://dx.doi.org/10.1007/s12555-019-9091-y]

142. Murayama, H.; Kawabe, T.; Inagawa, M.; Yoshizawa, K.; Ishibashi, M.; Takei, T.; Nagatani, K. Demonstration of snow removal work by wheel loader in an environment surrounded by obstacles. J. Robot. Mechatron.; 2023; 35, pp. 1251-1266. [DOI: https://dx.doi.org/10.20965/jrm.2023.p1251]

143. Backman, S.; Lindmark, D.; Bodin, K.; Servin, M.; Mörk, J.; Löfgren, H. Continuous Control of an Underground Loader Using Deep Reinforcement Learning. Machines; 2021; 9, 216. [DOI: https://dx.doi.org/10.3390/machines9100216]

144. Zhang, H.; Jin, S.; Li, B.; Xu, B.; Xiao, Y.; Zhou, W. Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles. Appl. Sci.; 2024; 14, 11036. [DOI: https://dx.doi.org/10.3390/app142311036]

145. Karahan, M. Autopilot and operator assistance systems for wheel loaders. Eurasia Proc. Sci. Technol. Eng. Math.; 2024; 32, pp. 433-438. [DOI: https://dx.doi.org/10.55549/epstem.1604702]