DOI: 10.1002/aenm.202301452 ISSN:

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Shicong Ding, Yiding Li, Haifeng Dai, Li Wang, Xiangming He
  • General Materials Science
  • Renewable Energy, Sustainability and the Environment


Precise prediction of lithium‐ion cell level aging under various operating conditions is an imperative but challenging part of ensuring the quality performance of emerging applications such as electric vehicles and stationary energy storage systems. Accurate and real‐time battery‐aging prediction models, which require an exact understanding of the degradation mechanisms of battery components and materials, could in turn provide new insights for materials and battery basic research. Furthermore, the primary barrier to meaningful artificial intelligence/machine learning for accelerating the prediction period is the exploitation of accurate aging mechanistic descriptors. This review comprehensively summarizes the evolution of deterioration mechanisms at the material and cell level in different environments and usage scenarios, including the intricate relationships between aging mechanisms, degradation modes, and external influences, which are the cornerstones of modeling simulation and machine learning techniques. Recent advances in electrochemical models coupled with internal battery degradation mechanisms as well as identification and tracking of aging parameters are shown, with particular emphasis on electrode balance and the anticipated trend of machine learning‐assisted reliable remaining useful life prediction. Precise simulation prediction of cell level aging will continue to play an essential role in advanced smart battery research and management, enhancing its performance while shortening experimental sequences.

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