Neel Patel

We present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions in the Hannover region of Germany. The PV system's nominal power is 2180 W_P with flat silicon modules on the vehicle's roof, rear, left, and right sides and other electronic components needed to charge the vehicle's high‐voltage (HV) battery. The analysis indicated that after 488.92 h of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking at the best possible orientation compared to 15.4, 30.67, and 22.99 kWh for the modules mounted on the rear, left, and right sides, respectively. During the trips, after 31.99 h of operation, 6.12, 0.68, 1.08, and 1.86 kWh of electricity were produced by the modules on the roof, rear, left, and right sides, respectively. The overall system efficiency was in the 60%–65% range. The aggregated usable electricity reaching the HV battery after multiple conversion stages generated by the system at the two parking locations was 129.39 kWh. PV electricity generated at the two parking locations enabled a range extension of approximately 530 km, which is 30% of the total distance driven during the measurement period between April and July 2021.

Nonuniformity of irradiation in photovoltaic (PV) modules causes a current mismatch in the cells, which leads to energy losses. In the context of vehicle‐integrated PV (VIPV), the nonuniformity is typically studied for the self‐shading effect caused by the curvature of modules. This study uncovers the impact of topography on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat‐surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across 17,000 km² in Europe and 59,000 km² in the United States using LIDAR‐derived topography and OpenStreetMap data. Our findings quantify irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle's surface orientation and curvature serving as secondary influencers. The most significant variation occurs on vertical surfaces of the vehicle in residential areas, with the lower parts receiving up to 35% less irradiation than the top part of the car. These insights may be used to improve the design and efficiency of vehicle‐integrated photovoltaic systems, optimizing energy capture in diverse environmental conditions.

This study uncovers the impact of geographic variability on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat-surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across Europe and the USA using LIDAR-derived topography and OpenStreetMap data. Our findings reveal significant irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle’s surface orientation and curvature serving as secondary influencers. These insights hold immense potential for improving the design and efficiency of vehicle-integrated photovoltaic systems, presenting a foundational knowledge base from which to optimize energy capture in diverse environmental conditions.

We assess the accuracy of two steady‐state temperature models, namely, Ross and Faiman, in the context of photovoltaics (PV) systems integrated in vehicles. Therefore, we present an analysis of irradiance and temperature data monitored on a PV system on top of a vehicle. Next, we have modeled PV cell temperatures in this PV system, representing onboard vehicle PV systems using the Ross and Faiman model. These models could predict temperatures with a coefficient of determination (R²) in the range of 0.61–0.88 for the Ross model and 0.63–0.93 for the Faiman model. It was observed that the Ross and Faiman model have high errors when instantaneous data are used but become more accurate when averaged to timesteps of greater than 1000–1500 s. The Faiman model's instantaneous response was independent of the variations in the weather conditions, especially wind speed, due to a lack of thermal capacitance term in the model. This study found that the power and energy yield calculations were minimally affected by the errors in temperature predictions. However, a transient model, which includes the thermal mass of the vehicle and PV modules, is necessary for an accurate instantaneous temperature prediction of PV modules in vehicle‐integrated (VIPV) applications.

We present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions. The PV system’s nominal power is 2180 W _P with flat silicon modules on the vehicle’s roof, rear, left, and right sides, as well as other electronic components needed to charge the vehicle’s high-voltage (HV) battery. The analysis indicated that after 488.92 hours of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking compared to 15.4, 30.67, and 22.99 kWh for the modules mounted on the rear, left, and right sides, respectively. During the trips, after 31.99 hours of operation, 6.12, 0.68, 1.08, and 1.86 kWh of electricity were produced by the modules on the roof, rear, left, and right sides, respectively. The overall system efficiency was in the 60-65 % range. The aggregated usable electricity reaching the HV battery after multiple conversion stages generated by the system at the two parking locations was 129.39 kWh. PV electricity generated at the two parking locations enabled a range extension of approximately 530 km, which is 30% of the total distance driven during the measurement period between April and July 2021.

Photovoltaics (PV) in onboard vehicle applications adds weight to an electric vehicle (EV), increasing the overall energy consumption. Although the added PV system weight is small compared with the vehicle weight, the power generated by PV is also very small compared with the power needed to propel an EV, making the effect of additional PV system weight on energy consumption a nontrivial topic to analyze. We present a method to study the impact of the vehicle onboard PV weight on the energy balance of EVs for different vehicle-added PV and vehicle-integrated PV configurations. The results are expressed through a newly introduced parameter called “onboard PV yield factor,” where positive values indicate a net energy gain and negative values indicate a net energy loss of the onboard PV system. Simulations are carried out to highlight the methodology for the driving phase of a medium and large passenger electric car with onboard PV for a selection of trips. Our method calculates the energy consumption attributable to the added PV system weight and PV energy yield for the selected trips. Our results for these sample simulations for the driving phase show a large range of yield factors, indicating the importance of systematically studying the impact of onboard vehicle PV weight.

Photovoltaics (PV) in onboard vehicle applications adds weight to an electric vehicle (EV), increasing the overall energy consumption. Although the added PV system weight (1.5– 40 kg) is small compared to the vehicle weight (1500–2200 kg), the power generated by PV (55–700 W) is also very small com- pared to the power needed (up to 80–285 kW) to propel an EV, making the effect of additional PV system weight on energy con- sumption a non-trivial topic to analyze. We present a method to study the impact of vehicle onboard PV weight on the energy balance of EVs for different Vehicle-added PV (VAPV) and vehi- cle-integrated PV (VIPV) configurations with eight different PV technologies, using data from vehicle onboard measurement campaigns and simulations. Simulations are carried out for the driving phase of two electric cars (medium and large passenger cars). Our method calculates the energy consumption attributa- ble to the added PV system weight (0.05–1.4 Wh/km) and PV energy yield (0.12–3.12 Wh/km) for a selection of trips. The re- sults of these simulations are expressed through a newly intro- duced parameter called “onboard PV yield factor”, where posi- tive values indicate a net energy gain and negative values indicate a net energy loss of the onboard PV system. Our results show that the onboard PV yield factor for a VAPV configuration can range between -69.1 and 86.9 %, and for a VIPV configuration, between 77.2 and 89.7 %.