Lithium-ion battery SOH estimation method based on multi-feature and CNN-KAN

The promotion of electric vehicles brings notable environmental and economic advantages. Precisely estimating the state of health (SOH) of lithium-ion batteries is crucial for maintaining their efficiency and safety. This study introduces an SOH estimation approach for lithium-ion batteries that integrates multi-feature analysis with a convolutional neural network and kolmogorov-arnold network (CNN-KAN). Initially, we measure the charging time, current, and temperature during the constant voltage phase. These include charging duration, the integral of current over time, the chi-square value of current, and the integral of temperature over time, which are combined to create a comprehensive multi-feature set. The CNN’s robust feature extraction is employed to identify crucial features from raw data, while KAN adeptly models the complex nonlinear interactions between these features and SOH, enabling accurate SOH estimation for lithium batteries. Experiments were carried out at four different charging current rates. The findings indicate that despite significant nonlinear declines in the SOH of lithium batteries, this method consistently provides accurate SOH estimations. The root mean square error (RMSE) is below 1%, with an average coefficient of determination ( R 2 ) exceeding 98%. Compared to traditional methods, the proposed method demonstrates significant advantages in handling the nonlinear degradation trends in battery life prediction, enhancing the model’s generalization ability as well as its reliability in practical applications. It holds significant promise for future research in SOH estimation of lithium batteries.