Optimal battery-swapping mechanism for electric vehicles using hybrid approach

Battery-swapping is a mechanism that involves exchanging discharged batteries for charged ones. Battery-swapping and charging stations (BSCS) enhance operational flexibility and interact with electric vehicle (EVs) batteries. An optimal battery-swapping mechanism is proposed for electric vehicles using a hybrid approach. The proposed intelligent method is a wrapper of the radial basis function neural network (RBFNN) and the war strategy optimization (WSO) algorithm. Hence, it is known as the WSO-RBFNN method. The key objectives of the proposed method are to reduce the total cost by defining an enhanced charging schedule for EV batteries, the number of batteries pulled from inventory to fulfill all incoming EV swap orders, the risk of charging damage while using high-rate chargers, and the cost of electricity at various times of the day. The proposed method minimizes the net costs based on EV energy consumption and travel time. WSO is exploited to attain the optimum control parameters of RBFNN. The performance of the proposed method is measured in MATLAB and compared with existing methods. The simulation outcome shows that the proposed method-related cost is lower than the existing methods. The proposed method provides a low cost of 32.5 $ and a high efficiency of 90% compared with existing methods like differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO).