SOH estimation of lithium-ion batteries subject to partly missing data: A Kolmogorov–Arnold–Linformer model

Accurate estimation of the state of health (SOH) is crucial for improving the safety and reliability of lithium-ion batteries. However, sensor measurements inevitably suffer from incomplete data due to sensor failures caused by factors such as component aging in practical applications. To address this issue, a joint estimation Kolmogorov–Arnold–Linformer (KAL) network model is proposed. Specifically, the Kolmogorov–Arnold Network (KAN) module is employed to replace the Multi-layer Perceptrons (MLP) module in the Linformer model, which enhances the representation of nonlinear features and improves the overall accuracy of the model. A dual-training model approach for SOH estimation is designed, which integrates the health feature (HF)-to-capacity model to infer trends in capacity changes using historical data. Based on these inferred trends, the model is trained to achieve accurate SOH estimation in scenarios with partly missing data. Validation on the publicly available Toyota-MIT-Stanford dataset demonstrates that, compared with other common deep learning methods, the KAL network model exhibits superior accuracy and reliability in scenarios with varying rates of partly missing data.