Solar electric vehicle PV potential under roadside shading considering tree transmittance

Solar electric vehicles represent an extension of electric vehicle technology and have the potential to accelerate decarbonization in the transportation sector by meeting part of the energy demand with electricity generated from vehicle-integrated photovoltaics. However, photovoltaic generation by solar electric vehicle is governed by spatiotemporally varying shading conditions along travel routes. The partial shading effect of roadside trees remains underrepresented in existing models. This study derives monthly tree transmittance coefficients through image-based canopy gap detection and integrates them into a street-view image-based shadow analysis and photovoltaic generation framework. Deep-learning semantic segmentation classifies each image into sky, tree, and artificial-structure regions, with monthly transmittance coefficients applied to tree pixels at each measurement location. The image-derived transmittance coefficients showed a mean absolute error of 5.1%p to field measurements. Incorporating monthly transmittance reduced the relative photovoltaic generation estimation error by 70% compared to fully blocked or open-sky assumptions. Differences in photovoltaic generation potential between the simplified assumptions and the transmittance-aware estimate varied by season, time of day, and route characteristics. The proposed image-processing-based methodology enables application across extensive road networks without site-specific field measurements, thereby providing a reliable and extensible foundation for solar mobility research that addresses both urban transport electrification and renewable energy integration.