Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Authored by Yang Li, Dulmini Karunathilake, D. Mahinda Vilathgamuwa, Yateendra Mishra, Troy W. Farrell, San Shing Choi, Changfu Zou

Published in IEEE Industrial Electronics Magazine, August 17, 2021

Citation: Y. Li, D. Karunathilake, D. M. Vilathgamuwa, Y. Mishra, T. W. Farrell, S. S. Choi, and C. Zou, "Model order reduction techniques for physics-based lithium-ion battery management: A survey," IEEE Ind. Electron. Mag., vol. 16, no. 3, pp. 36-51, Sep. 2022.

To unlock the promise of electrified transportation and smart grids, emerging advanced battery management systems (BMSs) will play an important role in the health-aware monitoring, diagnosis, and control of lithium-ion (Li-ion) batteries (see “Acronyms Used in This Article”). Sophisticated physics-based battery models incorporated into BMSs can offer valuable internal battery information to achieve improved operational safety, reliability, and efficiency and to extend the battery lifetimes. However, because they are developed from fundamental electrochemical and thermodynamic principles, rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications.

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