SOC Estimation of Lithium-Ion Batteries Based on Multi-Frequency Impedance Feature Point Extraction and Whale-Optimized Backpropagation Neural Network

Abstract

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is essential for energy management and safety control in battery management systems (BMSs). Conventional methods, such as Coulomb counting and open-circuit voltage methods, are constrained by error accumulation and slow response. This study proposes a data-driven SOC estimation method based on multi-frequency electrochemical impedance spectroscopy (EIS) feature-point extraction. Based on an EIS-SOC dataset, representative impedance frequency feature points are identified through Pearson correlation analysis, redundancy screening, and validation-based sequential forward selection. The corresponding real and imaginary impedance components are extracted to construct the input feature vector. The whale optimization algorithm (WOA) then optimizes the initial weights and biases of a backpropagation (BP) neural network, establishing a nonlinear mapping between EIS features and SOC. Experimental results on the predefined test cells show that, averaged over three runs, the proposed WOA-BP model with extracted multi-frequency impedance features achieves a root mean square error (RMSE) of 2.02%, a mean absolute error (MAE) of 1.41%, a maximum absolute error (MaxAE) of 5.84%, and a coefficient of determination (R2) of 0.9937. In comparison to traditional BP, PSO-BP, and LightGBM, the proposed method exhibits commendable overall estimation accuracy, which suggests the potential for EIS-based SOC estimation of lithium-ion batteries.