Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

Abstract

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency.