Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review (Adv. Energy Mater. 39/2023)

Cell Aging In article number 2301452, Haifeng Dai, Xiangming He and co-workers review and summarize the material and cell level aging mechanisms in different usage scenarios, the development of coupled electrochemical-aging models, and machine learning-assisted aging prediction, with an emphasis on the electrode balance and mechanistic descriptors to achieve the most precise battery lifetime prediction. A model framework for accurate cell aging prediction is provided to material and battery developers.