Thermal and Optical Engineering of Semi-Transparent Photovoltaics for Vehicle Roof Integration under Real-World Driving Conditions

Abstract

Vehicle-integrated photovoltaics (VIPV) have emerged as a promising approach to enhancing energy autonomy in electric vehicles by harnessing available roof surfaces to generate solar energy. However, dynamic factors such as varying incidence angles, shading, temperature fluctuations, and vibration during driving limit energy capture and are not accounted for in static laboratory tests. This study addressed the lack of comparative, on-road, technology-spanning analysis of semi-transparent thin-film photovoltaic absorbers under real automotive conditions. The objective was to evaluate and compare perovskite, CIGS, hydrogenated amorphous silicon, and organic photovoltaics using a combined optical–thermal–mechanical framework. Modules were integrated on a vehicle and tested through drive cycles representing highway, urban, and mixed routes, supported by synchronized telemetry, spectral filtering, goniometric angular analysis, and thermal/vibration stress protocols. Results showed that CIGS produced the highest areal energy output under highway conditions, while OPV led in mass-normalized yield due to its ultralight construction. Perovskite and OPV retained more output at oblique angles than CIGS, and perovskite demonstrated the lowest thermal degradation rate. Shading penalties varied significantly with interconnect geometry, and increasing perovskite segmentation reduced energy loss by 35%. Reinforcement learning–based MPPT improved tracking efficiency by up to 6 percentage points over traditional methods. These findings highlighted the influence of absorber selection, interconnect design, and controller choice on real-world energy yield. The research provided a foundation for engineering decisions in VIPV systems and emphasized the value of co-optimizing material properties with vehicle-specific integration constraints. Future studies could extend this framework to long-term fleet telemetry and predictive maintenance modeling.