Modeling and Optimal Capacity Configuration of On-Board Photovoltaic Systems for Electric Vehicles

The on-board photovoltaic system (OBPV) is a key technology for enhancing the driving range of electric vehicles and improving green energy utilization efficiency. However, its capacity configuration must balance multiple constraints, including photovoltaic power generation fluctuations, battery cycle life, and economic viability. This paper proposes a stochastic optimization model using scenario clustering to address multi-objective trade-offs in OBPV design. First, a photovoltaic power generation model and a battery cycle life model are developed. Second, combined with historical solar irradiation data, an improved K-means clustering algorithm is employed to generate typical scenarios and their probabilities, constructing a mixed-integer nonlinear programming model aimed at maximizing the annualized total revenue. The model is solved using a hybrid approach combining an improved particle swarm optimization algorithm with the Gurobi solver. Finally, a case study based on an OBPV prototype installed on Dongfeng eπ007 validates the effectiveness of the proposed model. Results show an $8 \%$ revenue increase over the base model under a $4.6 \%$ solar curtailment rate, with improved battery lifespan estimation for long-term user benefits. This research provides a basis for customizing OBPV configurations for users in different regions, promoting the development of green transportation.