Joint prediction of SOH and RUL for Lithium-ion batteries by an enhanced Transformer model with physical information constraints

It is essential to accurately predict the remaining useful life (RUL) and state of health (SOH) for effective health management of lithium-ion batteries (LIBs). Current methods face significant hurdles in practical application because they are heavily reliant on data quantity and quality, and their generalizability and interpretability are often limited. To address these challenges, an enhanced Transformer model with physical information constraints is proposed for the joint prediction of SOH and RUL. Firstly, to effectively capture the complex local and global patterns of LIBs degradation while maintaining computational efficiency, the matrix long short-term memory block is integrated into the encoder in the enhanced Transformer model. Secondly, to extract health features highly correlated with battery degradation, an improved Z-Score wavelet filtering algorithm is designed to process the raw signals. Six key features are selected from the filtered current, voltage, and capacity profiles as model input. Then, to address the insufficient generalization, poor interpretability of pure data-driven models, an implicit partial differential equation describing the LIBs degradation is established. It incorporated into the loss function, effectively constraining model training. Additionally, a model transfer strategy is devised to tackle the insufficient data of specific batteries, the physics-informed component, encoding universal degradation laws, is kept fixed, while the data-driven adaptation component is fine-tuned on limited target data. Finally, the proposed method is validated on four different types of LIBs data. When only one sample is used for training, the proposed model achieves average fitting coefficient exceeding 0.99 for SOH prediction and maintains RUL prediction errors within 5 cycles. The fine-tuning strategy achieved a fitting accuracy of 0.99 using only 30 % of the target domain data. Therefore, the designed model provides a new solution for the high-precision battery health management that possesses both generalization capability and physical interpretability.