The European Union committed to a 40% reduction in domestic greenhouse gas emissions by 2030 in order to maintain globalwarming between 1.5°C and 2°C during the 21st Conference of the Parties. This pledge involves, among other things, emission reductions in the transportation sector, which accounts for a quarter of Europe’s greenhouse gas emissions. Transportation electrification has been identified as a critical strategy for lowering greenhouse gas emissions. Electric vehicles (EVs) are a critical component in a future of more ecologically friendly transportation. Electric vehicles, on the other hand, are not a panacea for decarbonizing the transportation sector, as they contribute only when powered by renewable energy. Their adoption may cause congestion issues on the electrical grid, but also inconvenient driving experience due to lengthy charging times and restricted driving range. These concerns can be addressed by integrating photovoltaics onto vehicles. Vehicle integrated photovoltaics (VIPV) is a photovoltaic (PV) application that has gained increasing attention in recent years due to the decrease in the cost of solar cells and their increasing efficiency. The purpose of this research is to establish a modeling methodology for estimating the photovoltaic potential of an urban vehicle fleet. The model generates a user defined number of random trajectories that simulate traffic within city limits and calculates the DC energy output of the fleet’s vehicle integrated photovoltaics by taking into account the vehicle’s trajectories, spatial irradiation data along the path and during parking periods, the roof curvature, and the effect of the vehicle’s speed on module temperature. This thesis examined city cars in Eindhoven with a VIPV of 1.34 m2. The base case study investigated a fleet of 1000 cars traveling at an average speed of 30 kilometers per hour, once per day, with VIPV module height of 1.5 m. An automobile in this scenario produces close to 128.5 kWh of DC energy annually in average. The average consumption of the car in this case is almost 131 Wh/km , which means that VIPV can increase the driving range annually by approximately 981 kilometers. The fleet’s annual DC energy output distribution is quite interesting as it is extremely close to the Weibull distribution. The effect of numerous input factors on the DC energy output of the fleet was explored. The larger the fleet size, the better the fit between the fleet’s yearly DC energy distribution and the Weibull distribution. The increased height of the VIPV module enhances DC energy generation, and the influence of car speed on DC power output is significant, owing to the cooling effect of the head-wind on the module’s temperature.