Optimizing solar vehicle performance in urban shading conditions with enhanced control strategies

Solar-powered electric vehicles play a pivotal role in the forthcoming era of eco-friendly transportation, offering significant ecological advantages and addressing challenges posed by escalating fuel costs. Despite these advantages, these vehicles often encounter a disparity between available photovoltaic power and the required load power, necessitating reliance on energy storage systems. This situation gives rise to several challenges, including maximizing the lifespan of storage systems, identifying shiftable and non-shiftable secondary systems in real-time scenarios, ensuring road and driver safety, and navigating the urban environment with obstacles causing shading. In response to these challenges, this paper presents pioneering solutions aimed at pushing the boundaries of solar-powered electric vehicle technology. First, a novel approach to PV power converter control is introduced, leveraging an adaptive control strategy within the maximum power point tracking algorithm. This innovative technique dynamically adjusts the algorithm’s step size, particularly crucial when traversing shaded areas during vehicle movement, thus maximizing energy capture efficiency. Complementing this breakthrough, the paper proposes a cutting-edge decentralized energy management strategy. This strategy is characterized by its versatility and autonomy, featuring four parallel functions designed to optimize signal frequency allocation to each storage component, determine shedding percentages for secondary systems based on PV and battery power availability, identify optimal secondary systems for shedding, and manage their activation and deactivation seamlessly. To validate the performance and efficacy of these groundbreaking methodologies, extensive simulations were conducted using Matlab software, supplemented by real-time validation on the OPAL-RT platform within a hardware-in-the-loop application. The results obtained from both simulation and real-time testing provide compelling empirical evidence of the superior effectiveness and high-performance capabilities of the proposed solutions.