Enhanced lithium‐ion battery state‐of‐charge estimation for Electric Vehicles using the AOA‐DNN approach

Electric vehicles (EVs) battery management systems (BMSs) rely on exact state of charge (SoC) estimations to guarantee efficient and safe operation. Lithium‐ion batteries (LIBs) are favored for EVs due to their extended lifespan, high energy density, and minimal self‐discharge and high voltage. To address these issues, this research propose a LIB SoC prediction based on an actual BMS in EVs. The main objective is improving SoC of LIB. The proposed hybrid strategy is the combined performance of both the dynamic neural networks (DNN) and arithmetic optimization algorithm (AOA). Commonly it is named as DNN‐AOA technique. The SoC of Lithium‐ion batteries are predicted using the DNN approach. The proposed AOA is used to optimize the weight parameter of DNN to enhance prediction accuracy and reliability. By then, the operational MATLAB platform has adopted the proposed framework, and existing procedures are used to compute its execution. The proposed method demonstrates superior existing like Bayesian network (DBN), random vector functional link neural network (RVFLNN) and Gaussian progress regression (GPR). The proposed method yields a lower error value of 0.1 and a higher accuracy value of 98% compared with other existing methods.