A Physics-Informed Hybrid Data-Driven Approach with Generative Electrode-Level Features for Lithium-Ion Battery Health Prognostics
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION（2025）
Key words
Batteries,Aging,Feature extraction,Estimation,Solids,Data models,Transportation,Predictive models,Integrated circuit modeling,Electrolytes,Battery aging mode,electrochemical-informed data generative model,electrode-level state,physics-informed hybrid neural network (PIHNN),remaining useful life (RUL)
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