Fusing coupled degradation mechanisms with machine learning: A multi-fidelity framework for lithium-ion battery lifespan prediction

Accurate prediction of battery remaining useful life (RUL) is critical for safety enhancement and service extension. However, conventional model-based methods suffer from computational complexity, while data-driven methods struggle with inadequate mechanistic interpretability and prohibitive data requirements. Moreover, RUL prediction in real-world applications often faces the challenge of data sparsity, where only limited early-cycle measurements are available due to time and cost constraints. To address these limitations, we propose a multi-fidelity framework that synergistically integrates electrochemical degradation mechanisms with sparse early-life data, achieving precise RUL predictions through physics-informed interpretability and reduced experimental data. First, we develop an enhanced pseudo two-dimensional (P2D) model that integrates solid electrolyte interphase (SEI) growth mechanisms with partially reversible lithium plating dynamics. This advancement captures nonlinear capacity degradation patterns frequently overlooked in existing models. Second, through causative-process analysis of battery aging trajectories, three governing parameters—SEI solvent diffusivity, lithium plating kinetic rate constant, and dead Li decay constant are identified. Quantitative correlations between molecular-scale processes and macroscopic degradation patterns are established. Crucially, a multi-fidelity regression architecture is designed to simultaneously predict knee point and RUL by adaptively fusing sparse capacity measurements with physics-based degradation trajectories. Validated across 169 commercial cells under different fast-charging protocols, the proposed framework achieves satisfactory accuracy. It maintains Mean Absolute Percentage Errors (MAPE) of just 5.7% for the prediction of knee point and RUL. These results can serve as valuable metrics for performance evaluation and safety improvement of battery systems. • A modified battery model considering coupled degradation mechanisms is developed. • The effect of mechanistic parameters on capacity decay trajectory is investigated. • A physic-informed multi-fidelity method for battery knee point and RUL prediction. • The proposed method realizes accurate RUL prediction with sparse aging data.