## 5 Conclusions  
In this study, the amount of electricity generated by PV-powered EV on a university campus road was estimated by considering the time and driving route. Unlike previous studies that only considered vehicle roof modules, we evaluated the potential of modules attached to the four sides of the vehicle (roof, rear window, left door, and right door). As a result, the PV potential of module B (rear window) was calculated to be approximately 42% of that of module A (roof). Through the validation experiment, we analyzed the variation in PV potential for each module with the slope and azimuth of the driving section and the time of the day. Additionally, the effectiveness of the module attached to the rear window was compared to that of the roof module. The amount of expected power output for a vehicle running for approximately 10 min was 0.0158 kWh, which is low. In future, the power generation can be significantly increased by improving the cell efficiency and increasing the number of vehicles.  
The following should be considered for deriving improved research results. We used TMY data to evaluate the PV potential, which corresponds to the statistical values derived from long-term data. Therefore, it is necessary to take a conservative approach using P90 data to secure the reliability of results in predictions of sub-hours instead of annual or monthly units. Additionally, in the field experiment, the shadows formed by trees did not occur in the modelled shaded area. Herein, DSM could not include some of the trees; however, shadow experimental tools may be used in future. Furthermore, to conceptualize a solar electronic vehicle, an experiment was conducted by attaching a module. However, an experiment using an actual PV-powered EV would be preferable in future.  
The results of this study can be used as the base data for designing third-generation solar vehicles. Furthermore, the development of a navigation service for PV-powered EV maximizing the power output and its installation in driverless EV or self-driving taxis may be beneficial for building smart energy cities and developing a sustainable energy industry.  
[PERSON], [PERSON], [PERSON] and [PERSON] designed, gathered, simulated, and interpreted data; All the authors conducted field experiment; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON] implemented the final work for the paper submission. All authors have read and agreed to the published version of the manuscript.  
This study was supported by (1) Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2021-0-01886, Development of UAV-GIS-IoT-AI-M&S convergence digital twin web application for design, evaluation, operation and maintenance of photovoltaic systems) and (2) 2019 Research Grant from Kangwon National University.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript. DSM: Digital surface model; EU: European Union; EV: Electric vehicle; HV: Hydrogen vehicle; GHI: Global horizontal irradiance; NREL: National Renewable Energy Laboratory; PV: Photovoltaic; SAM: System Advisor Model; TMY: Typical Meteorological Year; USD: US Dollars; VIPV: Vehicle-integrated photovoltaic.