Optimizing Solar-Powered Electric Vehicle Management with Distributed Decision Making Under Shading Conditions

Abstract

Solar-powered electric vehicles (SEVs) have gained significant importance in the future of sustainable transportation due to their positive environmental impact and the increasing cost of fuel. However, a major limitation of SEVs is that photovoltaic (PV) panels supply less power than required to propel the vehicle, necessitating the use of energy storage systems. This situation presents several challenges, including shading obstacles in urban areas, driver and road safety concerns, the need to differentiate between shiftable and non-shiftable loads based on current conditions, and maximizing the lifetime of the storage system. To address these issues, this paper proposes a distributed energy management approach that incorporates four parallel functions: frequency separation, reduction, prediction, and shedding. Each function can operate independently, providing greater flexibility. The effectiveness and high performance of this proposed strategy were demonstrated through simulations conducted using Matlab software. To validate the efficacy of the approach, experimental results were achieved using the hardware-in-the-loop testing.