Affiliation:
1. School of Transportation Science and Engineering Beihang University Beijing China
2. Aerospace Science and Technology Defense Technology Research and Experimental Center The Second Academy of China Aerospace Science and Industry Corporation Beijing China
3. Warwick Manufacturing Group University of Warwick Coventry UK
4. Dyson School of Design Engineering Imperial College London, South Kensington Campus London UK
Abstract
AbstractThe issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.
Funder
National Natural Science Foundation of China