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Enhanced Lithium-ion battery model considering critical surface charge behavior

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  • Xiong, Rui
  • Huang, Jintao
  • Duan, Yanzhou
  • Shen, Weixiang

Abstract

Battery model is the basis of battery efficient and safe management. The widely used equivalent circuit model (ECM) generally shows poor behavior in predicting battery terminal voltage at low sate of charge (SOC), increasing the risk in the urgent use of a battery at low voltage greatly. To model strong nonlinearity of battery open circuit voltage (OCV), a solid-phase diffusion equation based surface SOC is proposed to characterize OCV behavior and establish the new structure of the enhanced ECM to describe low SOC behavior more precisely. Finally, a battery test platform was built to conduct battery tests for model validation. The results show that the root mean square error (RMSE) of the battery terminal voltage obtained from the proposed model at low SOC has been reduced to 8 mV compared with the RMSE of 17 mV from the traditional ECM model. It is expected that the proposed model can be employed in battery management systems to effectively improve the reliability and safety of emergency use of a battery at low SOC.

Suggested Citation

  • Xiong, Rui & Huang, Jintao & Duan, Yanzhou & Shen, Weixiang, 2022. "Enhanced Lithium-ion battery model considering critical surface charge behavior," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003373
    DOI: 10.1016/j.apenergy.2022.118915
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    References listed on IDEAS

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    1. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
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    Citations

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    Cited by:

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    2. Li, Xiaoyu & Chen, Le & Hua, Wen & Yang, Xiaoguang & Tian, Yong & Tian, Jindong & Xiong, Rui, 2024. "Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control," Applied Energy, Elsevier, vol. 367(C).
    3. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    4. Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).
    5. Xiong, Rui & Duan, Yanzhou & Zhang, Kaixuan & Lin, Da & Tian, Jinpeng & Chen, Cheng, 2023. "State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges," Applied Energy, Elsevier, vol. 349(C).

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