1. Introduction
The European Union (EU) has agreed on a range of policies aiming to reduce greenhouse gas emissions in various sectors of society. Since transport largely contributes to these emissions by a share of 27% of the EU’s total emissions in 2016, these emissions have to be reduced. For the year 2030, this policy implies that in the EU fleet-wide CO2 emissions of passenger cars should be reduced by 37.5% as compared to 1990 levels. For new vans and trucks, the emissions should be reduced by 31% [1]. Therefore, new strict targets require the reduction of average CO2 emissions of new vehicles that will enter the market. Consequently, the year 2020 is widely expected to bring dramatic changes to the automotive market. Due to the aforementioned targets, manufacturers are forced to invest intensively in innovative technologies of sustainable mobility. Therefore, many automotive players focus on battery electric vehicles (BEVs). In recent years, a large number of environmental impact studies were published, analyzing the potential environmental benefits of electric vehicles (EVs). The overall conclusion is that BEVs are preferable over petrol and diesel vehicles, however only if charged by renewable energy [2]. A possible solution is to charge these cars with low-emission renewable energy technologies such as photovoltaic systems. This could be achieved by charging stations which are powered by PV systems or by photovoltaic solar modules which are built in a car’s body parts, also called vehicle integrated photovoltaics (VIPV). Some vehicle manufacturers already aim at integrating PV cells in body parts of their passenger cars. One of the most recent solar powered electric vehicles is the Lightyear One of the Dutch company Lightyear. The vehicle has an integrated silicon PV array of more than 5 m2 with a nominal installed powered of 1250 Wp. Similarly, Munich-based producer Sono Motors is planning on launching their solar electric vehicle, named Sion. The Sion’s PV array has a nominal power of 1200 Wp. Solar charging in summer can add 34 km to the drive range of 255 km. With Audi´s e-tron Quattro with a nominal PV power of 400 Wp and the Toyota Prius P with a PV array of 860 Wp, two of the car industry’s major players recently entered the market as well. Especially for light utility electric vehicles (LUV), VIPV could be an attractive feature due to their predictability of utilization, in particular their moments of use and daily travel distances, and their significantly larger and flat roof surface which, if covered by solar cells, can potentially yield sufficient amounts of solar power. LUVs are usually vehicles with a gross vehicle weight of no more than 3.5 metric tonnes and are optimized to be tough-built, have low operating costs, and to be used in intra-city operations. Though prior studies have often indicated that VIPV will result in lower CO2 emissions, actual life cycle assessments (LCAs) of VIPV are barely available, and most claims until now have not been quantified or validated for the specific situation of VIPV of LUVs [3,4,5]. Thus, the goal of this work is to analyze how PV-powered vehicles can contribute to sustainable mobility. Therefore, an LCA focused on determining the CO2 emissions of a German VIPV LUV called StreetScooter will be conducted. The results of this research could be useful for car manufacturers, to calculate emissions per vehicle, for political institutions to estimate environmental impacts for the transport sector, and for business parties in the solar market to identify further application possibilities and yield useful data to identify critical areas for the improvement of VIPV for LUVs.
This LCA study was executed in the framework of a project called STREET, which was funded by the German Ministry for Economic Affairs and Energy and a German logistics company of Deutsche Post DHL Group named StreetScooter, which is currently working on the integration of PV on electric light utility vehicles (see Figure 1). Forschungszentrum Jülich as an organization for applied research supports the project by equipping the vehicle with PV modules and by analyzing the energy yield of PV modules on this vehicle by the analysis of data measured by radiation sensors on the vehicle under real shading and reflection conditions.
This paper is structured as following: in Section 2 the LCA method will be explained and all input parameters of the LCA will be described. Major assumptions regarding the operation phase and technology choice for the on-board vehicle application are discussed. The results, sensitivity analyses, and limitations of the study are reported in Section 3. Finally, Section 4 summarizes the results, presents the conclusions, and offers recommendations for future studies.
2. Method and Data
This section presents the general methodology used to execute the LCA, defines the efficiency of the VIPV investigated, and quantifies the resulting CO2 emissions. Additionally, key parameters that limit the environmental performance of the electricity produced by the PV system integrated into the vehicle are shown. Assumptions about these critical parameters for the reference case are clarified.
2.1. Life Cycle Assessment Method
LCA is a useful tool to quantify environmental performance, considering a holistic perspective. LCA is generally understood as a compilation and evaluation of the inputs, outputs, and potential environmental impacts of a product system throughout its life cycle [6]. LCA studies always consist of four main phases, which are covered through ISO standards (DIN 14044; ISO 14040:2006). The first step of the LCA is used to define the goal and scope of the study. The second step is a life cycle inventory (LCI) model through which data is collected and organized. The third step is the life cycle impact assessment (LCIA), used to understand the relevance of all the inputs and outputs in an environmental framework. The fourth step is the interpretation, which is a systematic technique to identify, check, and evaluate information resulting from the LCIA (see Figure 2).
The environmental impact assessment for this study is completed at the mid-point level. Midpoints are considered to be connections in the cause–effect chain of different impact categories, also known as the problem-oriented approach or classical impact assessment method. Greenhouse gas emissions (kgCO2-eq) were used as an indicator of climate change contribution. The 100-year global warming potentials based on the latest IPCC 2013 were assumed, according to their radiative, forcing capacity relative to the reference substance CO2. Global warming potential (GWP) during the life cycle stages of a PV system was estimated as an equivalent of CO2 containing all the significant emissions CO2 (GWP = 1), CH4 (GWP = 25), N2O (GWP = 298) and chlorofluorocarbons (GWP = 4750–14,400). The calculations were performed using LCA software GaBi with Ecoinvent v2.2+ as back-ground database. GaBi is a process-oriented software, examining the material and energy flows of each step of the production chain. The applied methodology of this study can be divided into four sections which will be briefly described below.
(A) VIPV Use Case Parameters
In the first section, the system boundaries and the input parameters for the operation in urban delivery were clarified and collected for the Use Case StreetScooter. Promising VIPV configuration was defined as the result.
(B) LCI of VIPV—Manufacturing
The second section was dedicated to compiling an inventory of energy and material inputs and outputs over the life cycle of VIPV. Life-Cycle Inventory was completed based on the data from literature. Based on the Inventory, a GaBi model was developed.
(C) LCI of VIPV—Operation and VIPV Energy flow model
The third part included the simulation of VIPV contribution to charging. To simulate the reduction of grid power demand, an energy flow model for the identified reference case was developed.
(D) Evaluation of environmental impacts
In the last part, potential environmental impacts (GWP) related to identified inputs and releases were evaluated. LCA results were compared to grid charging by means of their environmental impacts. The characterization factors were based on IPCC (2013) and should be incorporated for the impact category of global warming potential, which is tracked in kg CO2 eq.
2.2. Functional Unit, Goal, and Scope
The use case is the light utility battery electric vehicle Work L of StreetScooter. The functional unit for this study is 1 kWh of electricity supplied by the PV system to the battery of the StreetScooter. In comparison to the functional unit of 1 km driven, the emissions of 1 kWh can be calculated more accurately. Furthermore, the chosen functional unit of 1 kWh allows for a direct comparison of effects of charging by PV modules to those due to charging by the grid. Thus, the emissions of VIPV and grid charged BEV can be evaluated more precisely referring to the same functional unit. The operation of the electrical vehicle is set in Cologne, Germany and starts in 2017. Within the scope of this project, the environmental impacts of VIPV are to be studied according to the standard of life cycle assessment ISO 14040:2006. The PV system configuration is based on the first generation of the VIPV panels for the STREET Project with heterojunction silicon PV modules manufactured in China. The analyzed VIPV configuration includes three panels and three control units including the cables mounted on the vehicle roof. The overall capacity of the VIPV system is 930 Wp. The system of the VIPV electricity includes raw material extraction, wafers, crystalline silicon-based heterojunction solar cells and module manufacturing, mounting structures manufacturing, inverters manufacturing, system installation, and the operation.
2.3. Input Parameters for the Life Cycle Inventory
The production process of a typical commercial crystalline silicon solar cell is modelled based on the existing datasets describing the supply chain [7] (see Figure 3). Input parameters of the manufacturing of the PV control unit (PVCU) as well as the vehicle integration process were added based on internal communication in the project STREET. The electricity consumption on all process levels is modelled following specific electricity mixes corresponding to China (CN) or Germany (DE), respectively, based on the Ecoinvent datasets.
All input parameters of the manufacturing and vehicle integration process are described in Table 1. The exact location of manufacturing plants is undocumented and unknown. However, it can be assumed that the location of these plants is somewhere within China. Modelling of the transportation was based on the standard distances as suggested in the Guideline for PC LCA [7]. Metal parts were commonly reported with 200 km train and 100 km truck transportation in China. Additionally, transoceanic transport from China to Belgium was estimated to be 19,994 km based on searates.com data. In Europe, lorry transport from Antwerp (Belgium) to Cologne (Germany), a total of 500 km, was used.
For the solar cells in the VIPV, heterojunction technology (SHJ) was chosen due to the best trade-off between efficiency and costs. Thus, in the LCA, the cell heterojunction process was described by the following process steps taken from [8,10] and shown in Table 2. The metallization of the front side requires a double print of the standard amount of silver paste and sputtered aluminum closed back side. The LCI data on material and energy consumption were added for heterojunction cell processing, referring to [8,9,10].
2.4. Input Parameters for the Energy Flow Model
The main factor for the estimation of PV electricity generation is the effective solar irradiance, which depends on the route and location, season, time, and module configuration and orientation. For the reference case of the LCA, the location for the operation was set in Cologne, Germany. The hourly global horizontal solar irradiance was defined by averaging hourly incident global horizontal radiation data extracted from the PVGIS database. The on-board generation of electricity was simulated based on degradation, system losses, and shadowing factor (see Table 3). A 19.7% module efficiency was assumed [9]. In line with IEA-PVPS methodology guidelines [7], degradation of 0.7% per year was applied. Operation time of the reference case was set to 8 years, based on data of LUVs in delivery services [11].
According to the literature guidelines, efficiency for the VIPV system was estimated. Due to dynamic shading, an average 70% performance compared to residential PV was assumed [3]. Furthermore, generated energy cannot be used directly for traction of the vehicle and must be stored in the battery, where DC-Charging/discharging loss of 2% appears. Additional loss of 5% was considered due to the DC/DC converter. The loss of the MPP tracking additionally limits its efficiency in the model to 95% [3]. A performance loss of 9% due to temperature increase and low irradiance was assumed [5]. The overall average efficiency losses of the VIPV system is to be found in Table 4.
2.5. Input Parameters of the Grid Charge
The grid mix in the location of the charge was analyzed regarding its carbon intensity. The emissions of the grid can vary massively depending on the different power plants. Fossil power plants dominate the power generation in Germany. Acknowledged studies usually consider annual average carbon footprints of the grid power plants caused by the life cycle (construction, fuel production, operation, etc.) [12]. Hourly average emissions of the German electricity mix vary depending on the day and night times. The German electricity mix was modelled using SMARD electricity generation data from 2017 and utilized for the projection of the future scenario [13].
The reference scenario follows the pathway of technological development as far as possible, according to the goals set by politics. The target of the electricity sector in Germany for 2030 is 180–186 Mio t. Until 2028, the annual electricity mix GWP is expected to decrease by 2% per year [1]. Table 5 gives an overview of the emissions of different electricity sources, found in [14].
2.6. Reliability of the Data
The LCI in this study was based on extracting the data from reliable literature. The commercial LCA software GaBi Version 8.7.1.30 was used to model and calculate the LCI and impact assessment results. Essential materials, electricity mixes were calculated based on data represented by the Ecoinvent database unless otherwise noted. The International Energy Agency (IEA) developed guidelines to make the LCAs of PV systems more consistent and to enhance quality and reliability. Data on production is mainly based on these guidelines and LCIs of photovoltaics [7], additionally considering the heterojunction process of [9,10]. Some values for the Vehicle Integration Process and PVCU were adjusted after internal communication in STREET. The reason for adjustment was mainly a lack of access to the supply chain model data. The data used for this LCA varies in quality and reliability. To limit the resulting uncertainty, the differences of the data sources were analyzed and scored referring to the Quality Pedigree Matrix Flow Indicators determined by DIN 14044. Due to the above-mentioned conditions, the scores for each step were evaluated in Table 6. The highest score shows the lowest uncertainty and data scored with 5 shows the highest uncertainty.
3. Results
This section presents the results of the LCA study completed to the mid-point level.
3.1. Manufacturing Process of the VIPV
The results of the analysis of the manufacturing phase [kg CO2eq] demonstrate the impact before the operation starts. The manufacturing process VIPV shows similar results to other PV systems. The most dominant contributor to this phase is the Solar-Grade Process. It is responsible for 444.30 kg CO2 eq, a third of total emissions. The process of integration of the cells into the panel emits 235.24 kg CO2 eq. The calculated total amount of emissions during the manufacturing process is 1143.12 kg CO2 eq (see Figure 4).
3.2. Operation Phase of the VIPV
The on-board generation of electricity was simulated based on the assumptions on degradation, system losses, and shadowing factor, as previously described in Section 2.4. While driving the EV, the batteries will discharge and will recharge again using the on-board PV modules. The degree of VIPV’s impact was expected to vary with the usage patterns: different daily driving distances have different depths of discharge corresponding to daily driving durations. In this study, all incoming irradiance during the day is used, assuming energy is being collected energy and the battery is being charged, even if not driving. The results of the energy flow model are shown in Table 7.
For the reference scenario of 8 years operation and a shadowing factor of 30%, the VIPV contribution is 3738.116 kWh. Prolonged operation of 12 years generates 5526.702 kWh in total.
3.3. Comparison to the Emissions of the Grid Charge
For the same amount of energy, if the grid would be used, 1630 kg CO2-eq for 8 years and 2267 kgCO2-eq for 12 years were calculated. The losses appearing due to grid distribution were not calculated, because the emission factor is already based on an energy consumption perspective.
Main findings of the comparison with grid electricity show: VIPV can improve the carbon footprint for the reference case of an average shadowing factor of 30% and 8 years of operation time. For the functional unit of 1 kWh of on-board generated PV electricity, the emission factor of 0.357 kgCO2-eq/kWh is calculated for the reference case. In comparison, the average grid emissions for the operation time are expected to be 0.435 kgCO2-eq/kWh.
Considering the data quality of the LCA, reduction of emissions of the functional unit for the reference case compared to the grid is about 18%. The holistic view of the results for the reference case shows 3738 kWh VIPV contribution. For the functional unit of 1 kWh of on-board generated PV electricity, the emission factor of 0.357 kgCO2eq/kWh is calculated. In comparison, the average grid emissions for the operation time are expected to be 0.435 kgCO2eq/kWh. Compared to the estimated grid average, about 18% less emissions per kWh are caused by VIPV. Projected contribution of VIPV was replaced by grid charging to find out in which operation year VIPV have fewer emissions than the grid and thus calculate the “ecological break-even point”. In the previously described reference case, this point is achieved in the year 2022. That means that after 6.5 years of operation, the ecological impact of VIPV equals the impact of the grid charge. However, an increasing shadowing factor of mobile application causes a significant growth of emissions per kWh.
3.4. Sensitivity Analysis
The results of the study are wide-ranging. The variations mainly arise from system operating assumptions (e.g., solar irradiation, system lifetime, shadowing factors) and technology improvements (e.g., electricity consumption for manufacturing processes). In this section some adjustments of the reference case (8 years of operation, 0.7% degradation, and 30% shadowing factor) are considered.
PV-generated power is an essential variable for the reduction in emissions. By increasing the shadowing factor, emissions per kWh grow significantly. An emission factor of 0.357 kgCO2-eq/kWh is calculated for the reference case. The increased shadowing factor of 40% results in 0.435 kgCO2-eq/kWh, which equals the average emissions of the future grid electricity. As shown in Figure 5, the ecological benefit over the grid charge disappears completely when the shadowing factor reaches 40%.
Sensitivities show that if the VIPV is used for a prolonged life of 12 years, the emission factor of the produced electricity decreases to 0.221 kgCO2-eq/kWh. A reduction of 38% (0.136 kgCO2-eq/kWh) compared to the reference case of 8 years is noted. The average grid mix emissions of prolonged use decrease to 0.409 kgCO2-eq/kWh. Comparable results can be achieved with a shadow factor of 55% or an average annual VIPV generation of about 260 kWh/a. Lifetime extension of the vehicle operation will automatically result in a reduction of the emissions per produced kWh of the VIPV. Figure 6 demonstrates the potential of the longer operation phase for different shadowing factors.
Based on findings of the sensitivity analyses, the highest potential for emission reduction can be confirmed for a “green” electricity scenario, where renewable electricity is used for the manufacturing process. The emission factor of 0.831 kgCO2/kWh for the electricity mix of China used for the simulation of the reference case is based on the GaBi Education Database from 2017. With the increasing share of renewable energy in the electricity mix, lower GWP impact will arise from the production phase of the VIPV. Using green electricity has the potential to be almost carbon-free, as is the case for today’s hydropower. For “green” electricity, assumptions of hydro plants with average emissions of 0.003 kgCO2-eq/kWh were used to cover the energy need of the manufacturing phase in China [15]. As illustrated in Figure 7, the emissions decrease from 0.357 to 0.230 kgCO2-eq/kWh for the shadowing factor of 30%.
4. Discussion and Conclusions
The study reports the unique observation that placing a PV system on-board of an existing StreetScooter can improve the carbon footprint of the generated electricity for the reference case of an average shadowing factor of 30% and 8 years of operation time. The ecological benefits of PV-powered light utility vehicles are confirmed for the reference case of the StreetScooter. Yet, the results of the LCA show that viability is heavily dependent on the vehicle’s deployment region and usage scenario. Main findings of the comparison to the grid electricity show: VIPV can improve the carbon footprint for the reference case of an average shadowing factor of 30% and 8 years of operation time. For the functional unit of 1 kWh of on-board generated PV electricity, the emission factor of 0.357 kgCO2-eq/kWh is calculated. In comparison, the average grid emissions for the operation time are expected to be 0.435 kgCO2-eq/kWh. Considering the data quality of the LCA, reduction of emissions of the functional unit for the reference case compared to the grid is about 18%. By increasing the shadowing factor, emissions per kWh grow significantly. The ecological benefit to the grid charge disappears completely when the shadowing factor reaches 40%. However, if the operation time is prolonged to 12 years, the shadowing factor can reach 55%, having similar emissions to grid charge. For the reference case with 30% shadowing, a reduction of 38% compared to 8 years in use can be noted. For this case, 0.221 kgCO2-eq/kWh is estimated for the functional unit.
One of the key challenges of this work was finding an appropriate vehicle usage model to reproduce the ratio of using solar power and performance assessment of Maximum Power Point Tracker (MPPT) algorithms for VIPV. Tests with radiation sensors investigating shading and reflection conditions are suggested. Numeric simulation of VIPV output test drives with irradiance profiles of routes should include different vehicle usage times, effects of panel position and movement. Additionally, it is necessary to address the electrical and technical issues.
For the recycling process, no established and reliable routes were found. As to the knowledge of the author, no study provides details on the LCI with the input and output of every process stage. However, if material depletion is considered, recycling is crucial, and further research should include recyclability options. Since second use is an important issue, the mounting structure, removable, and lightweight, must become a priority for research. Furthermore, a scenario of VIPV connection to the public grid while parking during weekends, in which the surplus of unused electricity can be fed into the grid, seems to be realistic. Vehicle2Grid (V2G) concepts can be very profitable, but first, the dependence on the state of charge (SOC) of the battery including an ageing model of the battery with charging and discharging losses should be analyzed. Enhanced communication and cooperation between automotive companies and PV players can contribute to the positive image of vehicle integrated photovoltaic systems in order to achieve the goal to change the image of VIPV. Likewise, international methods for evaluating the reduction of emissions of PV-powered vehicles can help to communicate the created value for the different driving and charging behaviors. To contribute to the growth of the VIPV market, governments willing to achieve emission goals must support the standardization of the technology. To solve this problem, international methods of evaluating added value on the reduction of grid power and ecological benefits are required.
Author Contributions
Conceptualization O.K., K.D.; methodology O.K., J.M.; software, validation, investigation, resources, data curation and writing—original draft preparation O.K.; writing—review and editing A.R., J.M., K.D.; visualization, O.K.; supervision J.M., A.R., project administration K.D. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Federal Ministry for Economic Affairs and Energy in the framework of the STREET project (grant: DB001618).
Acknowledgments
This work is based on the Master’s thesis by Olga Kanz at the Hochschule Köln 2019. The authors appreciate the support of the research group of IEA Photovoltaic Power System Programme Task 17, which focuses on possible contributions of photovoltaic technologies to transport and support from Toshio Hirota and Keichi Komoto. Costs for publishing are covered by Forschungszentrum Jülich GmbH.
Conflicts of Interest
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
Figures and Tables
Enlarge this image.Figure 1. StreetScooter Work L Reprinted from: CC-BY-SA-4.0 (via Wikimedia Commons), Superbass, 2017.
Enlarge this image.Figure 3. Vehicle integrated photovoltaics (VIPV) system value chain: process flow diagram. CN means China, DE Germany.
![View Image - Figure 4. Results of the LCA, the manufacturing phase global warming potential (GWP) = 1143 [kg CO2 eq].](https://media.proquest.com/media/hms/THUM/8/lDIVJ?_a=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%3D%3D&_s=1oPGc9gK8%2FgOh4RB02d2iWekwME%3D "View Image - Figure 4. Results of the LCA, the manufacturing phase global warming potential (GWP) = 1143 [kg CO2 eq].")
Enlarge this image.Figure 4. Results of the LCA, the manufacturing phase global warming potential (GWP) = 1143 [kg CO2 eq].
Input parameters of the manufacturing process and vehicle integration Ppocess.
| Parameter | Based on | Comment | |
|---|---|---|---|
| Wafer | Type: n-type c-Si | (b) | Wafer (Solar Grade) |
| Thickness: 180 nm | (a) | Wafer thickness | |
| Cell | Technology SHJ c-Si | SHJ cell processing adopted due to STREET requirements, μc-SiOx:H | |
| Area: 239 mm2 | (a) | 156 × 156 mm2 | |
| Efficiency: 22.5% | (c) | Describes the efficiency of the solar cells | |
| Panel | Efficiency: 19.7% | (c) | Describes the efficiency of the solar cells |
| Glass thickness: 2 mm | (c) | The thickness of the solar glass used on the front side of the solar cell | |
| Back: EVA Back Foil | (a) | EVA back foil configurations based on the Guideline | |
| Cell number per panel: 72 | (b) | Standard-based on the Guideline | |
| Vehicle Integration | Panel Number: 3 |
(b) | |
| Mounting: on the rooftop Integration with Bosch Profiles |
(a) Guideline (Frischknecht et al. 2015) [7]; (b) STREET Internal Expert Judgement; (c) LCI on SHJ Cells (Louwen et al. 2016; Olson et al. 2013) [8,9].
Table 2Process steps of heterojunction cell.
| Process Step | Material | Description |
|---|---|---|
| Metallization front | Ag print | Screenprint |
| TCO | ITO | Sputtering of indium-tin-oxide |
| Emitter | a-Si: H (p) | ALD—atomic layer deposition |
| Passivation | a-Si:H (i) | Deposited by PECVD |
| BSF | a-Si:H (n) | Back surface field |
| Metallization back | Ag print | Screenprint |
Input parameters for the operation of the VIPV.
| Parameter | Value | Unit |
|---|---|---|
| Capacity | 930 | Wp |
| Efficiency | 19.7 | % |
| Degradation | 0.7 | % |
| Operation lifetime | 8 | a |
| Location | Cologne (50.938, 6.954) | Lat/Lon |
| Database | PVGIS-CMSAF | / |
VIPV system efficiency.
| Loss Coefficient | Changes in Output (%) |
|---|---|
| MPPT loss | −5 |
| Temperature/low irradiance | −9 |
| DC/DC conversions | −5 |
| DC charging/discharging loss | −2 |
| Average shadowing factor | −30 |
Emission factors of electricity sources.
| Electricity Source | (g CO2 eq./kWh) | Reference |
|---|---|---|
| Biomass | 272 | [15] |
| Hydropower | 3 | [12] |
| Pumped hydro | 26 | |
| Wind offshore | 6 | |
| Wind onshore | 11 | |
| Photovoltaics | 67 | |
| Geothermal | 192 | |
| Lignite | 1142 | |
| Coal | 815 | |
| Natural gas | 374 | |
| Nuclear | 32 | [14] |
| German mix average | 486 | [12] |
LCA data quality.
| Process | Data Source | Quality | Comment | Flow Score |
|---|---|---|---|---|
| Feedstock, ingot, wafer production | (Frischknecht et al. 2015) |
Primary data, measured | High variability of process data, low uncertainty, verified data based on measurements with less than 6 years of difference | 2 |
| Wafer cleaning, texturing, PECVD of a-Si layers, TCO deposition, contacting, wiring | (Louwen et al. 2016; Louwen et al. 2012b; Olson et al. 2013) |
Primary data, measured, Ecoinvent processes updated based on updated data | Low variability of process data, low uncertainty, based on measurements with less than 6 years of difference | 1 |
| PVCU | Expert judgement—continental |
Primary data, adjusted and verified by the STREET experts | Low variability of process data, higher uncertainty, verified by STREET Experts, less than 6 years of difference | 2 |
| Module |
(Frischknecht et al. 2015) |
Primary data, measured | Low variability, higher uncertainty, verified precise data based on measurements with less than 6 years of difference | 2 |
| Vehicle |
Expert judgment: StreetScooter |
Estimated data based on metadata, verified by the STREET concept | High variability, high uncertainty, documented estimate, verified by STREET Experts, less than 6 years of difference | 3 |
| Operation | (PV-Powered Vehicle Strategy Committee 2019) |
Estimated data based on metadata, lacking measurements of PV output and maintenance | High uncertainty, high variability, documented estimate, verified by STREET Experts, less than 6 years of difference | 3 |
Results of the energy flow model.
| Parameter | Value | Unit |
|---|---|---|
| Average yield in urban area | 936 | (kWh/kWp) |
| Average annual VIPV electricity production on board | 479 | (kWh/year) |
| Total production VIPV | 3738 | (kWh) |
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Details
; Ding, Kaining 1 1 IEK-5 Photovoltaik, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany;
2 Energy Technology Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;
3 Institute of Electrical Power Engineering (IET), Cologne University of Applied Sciences, 50678 Cologne, Germany;