State of Health Estimation for Lithium-Ion Batteries Based on Conformer-KAN

The state of health (SOH) estimation of lithium-ion batteries faces significant challenges under complex operating conditions due to transient disturbances and distribution shifts. This paper proposes a deep learning framework named Conformer-KAN, which integrates a convolution-augmented Transformer (Conformer) with a Kolmogorov–Arnold Network (KAN). The method first constructs a unified input representation by fusing multi-view features including voltage, current, temperature, and incremental capacity. It then employs a Conformer encoder that combines gated local convolution units (GLCU) and multi-head self-attention (MHSA) to achieve joint modeling of local and global features. In addition, learnable spline-based activation functions are introduced within the KAN structure to enhance the model’s capacity for capturing complex nonlinear degradation behaviors. Cross-battery and cross-condition evaluations conducted on two public datasets demonstrate that the proposed method achieves root mean square errors (RMSE) of 0.006 ± 0.001 and 0.003 ± 0.001, and coefficients of determination (R2) of 0.987 ± 0.003 and 0.994 ± 0.002, respectively. These results show that Conformer-KAN significantly outperforms existing mainstream approaches in both robustness and generalization performance.