A Physics-Informed Hybrid Multitask Learning for Lithium-Ion Battery Full-Life Aging Estimation at Early Lifetime

Lithium-ion battery health state estimation constitutes an important part of battery management systems, with existing methods either based on mechanistic models or data-driven approaches. This article proposes a physics-informed hybrid multitask learning approach for estimating battery full-life aging states by integrating mechanistic knowledge with data-driven methods at an early lifetime. First, a hybrid aging mode-informed feature is introduced to integrate electrode-level health states with data-driven information. An electrochemical-informed multitask generative model is established to estimate Li$^+$ concentration dynamics in both the solid particle and electrolyte. An electrode-level state-constrained training strategy is implemented to guide the model to respect causality. For validation purposes, three battery datasets are utilized to estimate aging states from the electrochemical to the cell level. Compared with traditional mechanistic and data-driven models, the proposed method demonstrates higher accuracy and real-time performance in battery state estimation.