Advanced Battery Management for Lithium-Ion EVs: Integrating Extended Kalman Filter and Modified Multi-Layer Perceptron for Enhanced State Monitoring

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric Vehicles (EVs). However, many existing studies have concentrated on examining sensor malfunctions in batteries to avert problems such as overcharging and overheating and are lacking in terms of effective handling of non-linear behaviors. To overcome these limitations, the proposed work introduces a hybrid approach for estimating the state of lithium-ion batteries. It employs an Extended Kalman Filter (EKF) for SOC estimation and modified Multi-Layer Perceptron (MLP) for SOH estimation in batteries. It can handle the non-linear characteristics often exhibited by sensor readings and fault behaviors. The EKF algorithm involves initialization, prediction, and correction phases, allowing for accurate state estimation based on measurements. For SOH estimation, the NASA battery dataset, which includes various battery conditions across different temperatures, is analyzed using a modified MLP regression process. This modified MLP employs a gradient shift bias adjustment technique to minimize error rates by refining the gradients and biases introduced during the training process. It also effectively adjusts the model’s weights for better SOH estimation. The results demonstrate improved accuracy in battery performance, as indicated by lower RMSE, MSE, MAE and R2 values. Furthermore, the study highlights the effectiveness of this hybrid method for significant battery management at different temperatures, which emphasizes the potential of this model, with enhanced state estimation for EV applications.