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Abstract

Electric Vehicle (EV) charging use is going up solar installations on rooftops and on a large scale in power plants are increasing consequently. Therefore, a Battery Management System (BMS), which is the system for managing battery storage, was used to control the solar energy generation to coordinate charging. Such a connection gives the BMS the opportunity to modify charging/discharging patterns with the help of solar energy predictions and prices of electricity from the grid. Moreover, the battery is being protected from aging with the BMS to monitor the state of charge/health of the battery. The BMS protects the Li-ion batteries against overcharging, discharging, heating and managing the used energy. In this paper, a comprehensive review of solar-powered EV-based BMS methods is presented. moreover, different BMS methods are applied to the Li-ion batteries, including classical methods such as Coulomb Counting (CC) andopen-circuit voltage, improved Kalman Filter (KF) methods, and Artificial Intelligence (AI) methods. Moreover, estimation of the battery’s SOC is very important to protect the battery cells from overcharging/discharging, and Implementing this can enhance the battery’s performance and extend its lifetime. The most used methods in the literature for estimation SOC are the KF and extended KF methods. Although these methods have more advantages, such as simplicity and low-cost implementation, they suffer from low response and the computationally intensive. Unlike the BMS-based AI methods, it provides more benefits in managing the Li-ion batteries of EVs, including high degree of accuracy, establishing the discharge voltage limit, ensuring the protection of battery cells, and minimizing the time required. As for future scope, there is a possibility of developing the coordination of vehicle to grid or grid to vehicle (V2G/V2G) operation using adaptive BMS and AI strategies to improve the performance and enhance the reliability.

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