Minimization of Battery Pack Imbalance of Electric Vehicles Using Optimized Balancing Parameters

Battery, one of the key components of developing electric vehicle (EV) technology, has a great significance in the adoption of EVs. The battery packs consist of several cells connected in series and parallel according to the operating voltage and required capacity. Imbalances between serially connected cells are frequently encountered, although cell balancing between parallel connected cells is not a common concern. Batteries are often equipped with balancing systems to prevent the imbalances, which reduces the available capacity and lifetime of the battery. This paper presents a comparative analysis for passive balancing method by considering Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to acquire the optimal timing of enabling/disabling the balancing current to minimize imbalance of cells of the EV battery. In order to perform the balancing operation, the cell voltages are continuously monitored by the controller unit. Cells with lower voltages are detected evaluating the average voltage of the cells, balancing resistor switches of the rest are operated. In the optimal timing of enabling/disabling the balancing current, the minimum imbalance is determined as the cost function for PSO and GA. In the proposed study, a battery pack consisting of 16 series cells is modeled by using MATLAB/Simulink. The nominal capacity and voltage of each cell are 108 Ah and 3.7 V, respectively. The performance of the proposed system is validated.