A Multi-Stage Modelling Framework for Accurate Early-Life SOH and EOL Prediction

Abstract

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential to ensure their reliable performance and operational safety. This study integrates functional principal component analysis (FPCA) of incremental capacity (ICA) curves and then propagates the extracted functional features through a two-stage learning architecture, where a linear regression model captures the dominant degradation trend and a Gaussian process regression (GPR) layer adaptively corrects the nonlinear residuals. This multi-stage framework enables highly accurate prediction of both state-of-health (SoH) evolution and end-of-life (EOL) timing from early-life data. Initially, Incremental Capacity Analysis (ICA) is employed to extract characteristic voltage-capacity features that encapsulate the electrochemical degradation behavior of the cells. These features are subsequently transformed through functional decomposition using FPCA, enabling the identification of dominant degradation modes and temporal evolution patterns within the cycling data. The resulting functional components are then mapped to the SOH through a hybrid regression framework, combining the linear interpretability of traditional regression techniques with the nonlinear learning capability of GPR. The proposed approach demonstrates strong predictive performance, achieving a mean absolute percentage error (MAPE) of 2.99% and an average end-of-life (EOL) prediction error of 4.95%, underscoring its effectiveness and robustness in accurately characterizing lithium-ion battery degradation.