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Energy State Estimation for Series-Connected Battery Packs Based on Online Curve Construction of Pack Comprehensive OCV

Author

Listed:
  • Lei Pei

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Yuhong Wu

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Xiaoling Shen

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Cheng Yu

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Zhuoran Wen

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Tiansi Wang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Accurate estimation of the state of energy (SOE) in lithium-ion batteries is crucial for determining the output power and driving range of electric vehicles. However, in series-connected battery packs, inconsistencies among individual cells pose significant challenges for precise SOE estimation. This issue is particularly pronounced for lithium iron phosphate (LFP) batteries. Their relatively flat open-circuit voltage (OCV) curve makes the classic method of directly weighting the SOE of representative cells—commonly used for ternary batteries—ineffective. This is because the traditional method relies heavily on a linear relationship between the SOE and the voltage, which is not present in LFP batteries. To address this challenge, a novel SOE estimation approach based on the online construction of the battery pack’s comprehensive OCV curve is proposed in this paper. In this new approach, the weighting of representative cells shifts from a result-oriented mode to a key-parameter-oriented mode. By adopting this mode, the whole pack’s comprehensive OCV can be obtained training free and the pack’s SOE can be estimated online within an equivalent circuit model framework. The experimental results demonstrate that the proposed method effectively controls the SOE estimation error within 3% for series battery packs composed of cells with varying degrees of aging.

Suggested Citation

  • Lei Pei & Yuhong Wu & Xiaoling Shen & Cheng Yu & Zhuoran Wen & Tiansi Wang, 2025. "Energy State Estimation for Series-Connected Battery Packs Based on Online Curve Construction of Pack Comprehensive OCV," Energies, MDPI, vol. 18(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1772-:d:1626186
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    References listed on IDEAS

    as
    1. Zhong, Liang & Zhang, Chenbin & He, Yao & Chen, Zonghai, 2014. "A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis," Applied Energy, Elsevier, vol. 113(C), pages 558-564.
    2. Zhen Zhu & Lingxin Zeng & Long Chen & Rong Zou & Yingfeng Cai, 2022. "Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    3. Zheng, Yuejiu & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Ma, Hongbin & Dollmeyer, Thomas A. & Freyermuth, Vincent, 2013. "Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model," Applied Energy, Elsevier, vol. 111(C), pages 571-580.
    4. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    5. Xiong, Rui & Li, Zhengyang & Li, Hailong & Wang, Jun & Liu, Guofang, 2025. "A novel method for state of charge estimation of lithium-ion batteries at low-temperatures," Applied Energy, Elsevier, vol. 377(PB).
    6. Chen, Lei & Wang, Shunli & Jiang, Hong & Fernandez, Carlos, 2024. "A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range," Applied Energy, Elsevier, vol. 355(C).
    7. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
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