New Insights Into Estimating Lithium-Ion Battery Cell Health Leveraging Physics-Informed Machine Learning

The complex degradation kinetics and inconsistency of decay paths in in-service lithium-ion batteries (LIBs) significantly affect the health management capabilities of battery management systems (BMSs). To enhance the performance of state-of-health (SOH) estimation, we propose a physics-informed neural network (PINN) learning framework. A partial differential equation (PDE) is first formulated based on prior knowledge to model battery cell degradation kinetics. When prior knowledge is insufficient, characteristic variables of degradation are extracted from monitoring data to expand the kinetic model into multiple dimensions. To avoid the direct computation of PDEs for multidimensional models, the deep hidden physical modeling (DeepHPM) technique is used to construct a function approximator within the PINN framework. Furthermore, a loss function weight-balancing strategy is introduced during model training. By minimizing the negative log-likelihood estimation, the weights are adaptively updated to ensure optimal task balance. The proposed framework is validated on a public dataset and compared against typical machine learning methods through comprehensive analysis. Experimental results demonstrate that the proposed method achieves higher accuracy and reliability in SOH estimation for practical applications, outperforming existing approaches.