SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential to ensure the safe and stable operation of equipment such as electric vehicles. To address the limitations in the accuracy and robustness of existing methods under complex operating conditions, a CNN-BiGRU-KAN (CGKAN) method for SOH estimation based on partial discharge curves is proposed. Firstly, random forest analysis is applied to extract features highly correlated with battery health from the partial discharge curve data. Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. Finally, multiple experiments under different conditions are conducted, and the results demonstrate that the proposed CGKAN method, by integrating the individual advantages of 1D-CNN, BiGRU, and KAN, efficiently captures complex nonlinear patterns in battery health features and maintains stable performance across various operating conditions.