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State-of-Health Online Estimation for Li-Ion Battery

Journal Article
14-09-02-0012
ISSN: 2691-3747, e-ISSN: 2691-3755
Published October 10, 2020 by SAE International in United States
State-of-Health Online Estimation for Li-Ion Battery
Sector:
Citation: Fang, L., Xinyi, L., Weixing, S., Hanning, C. et al., "State-of-Health Online Estimation for Li-Ion Battery," SAE Int. J. Elec. Veh. 9(2):185-196, 2020, https://doi.org/10.4271/14-09-02-0012.
Language: English

Abstract:

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.