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A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles

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  • Chen, Xiaokai
  • Lei, Hao
  • Xiong, Rui
  • Shen, Weixiang
  • Yang, Ruixin

Abstract

Open circuit voltage (OCV) has a considerable influence on the accuracy of battery state of charge (SOC) estimation. Three efforts have been made to reconstruct OCV for SOC estimation of lithium ion batteries in this study: (1) A new parameter backtracking strategy is proposed for online parameter identification using the recursive least square (RLS) algorithm to obtain stable OCV, which significantly reduces the jitters occurring in OCV identification results. (2) Historical experimental data of lithium ion batteries are used to derive baseline OCV curve and determine constraint boundaries, then an extended Kalman filter (EKF) is employed as a state observer to estimate the SOC for the same types of the batteries that have not been tested. (3) The OCV-SOC curve is reconstructed based on the accumulated online parameter identification and SOC estimation results. The OCV curve can be locally reconstructed even when the accumulated data only cover a partial range of SOC, which is suitable for electric vehicle (EV) operation conditions. Once the OCV curve is reconstructed, the response surface model of OCV-SOC-Capacity is applied to update battery capacity. In this way, the OCV curve can be gradually reconstructed from high SOC to low SOC during battery discharging process. The use of the reconstructed OCV curve to estimate SOC significantly improves the SOC estimation accuracy with the maximum error less than 3% for EV operation conditions.

Suggested Citation

  • Chen, Xiaokai & Lei, Hao & Xiong, Rui & Shen, Weixiang & Yang, Ruixin, 2019. "A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s030626191931445x
    DOI: 10.1016/j.apenergy.2019.113758
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    Citations

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    Cited by:

    1. Jiang, Li & Li, Yong & Huang, Yuduo & Yu, Jiaqi & Qiao, Xuebo & Wang, Yixiao & Huang, Chun & Cao, Yijia, 2020. "Optimization of multi-stage constant current charging pattern based on Taguchi method for Li-Ion battery," Applied Energy, Elsevier, vol. 259(C).
    2. Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    3. Aihua Wu & Yan Zhou & Jingfeng Mao & Xudong Zhang & Junqiang Zheng, 2023. "An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(16), pages 1-24, August.
    4. Chun Wang & Chaocheng Fang & Aihua Tang & Bo Huang & Zhigang Zhang, 2022. "A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty," Energies, MDPI, vol. 15(12), pages 1-16, June.
    5. Filip Maletić & Mario Hrgetić & Joško Deur, 2020. "Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack," Energies, MDPI, vol. 13(3), pages 1-16, January.
    6. Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    7. Yongcun Fan & Haotian Shi & Shunli Wang & Carlos Fernandez & Wen Cao & Junhan Huang, 2021. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation," Energies, MDPI, vol. 14(8), pages 1-18, April.
    8. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    9. Xin Qiao & Zhixue Wang & Enguang Hou & Guangmin Liu & Yinghao Cai, 2022. "Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion," Energies, MDPI, vol. 15(12), pages 1-22, June.
    10. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    11. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    12. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    13. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    14. Wang, Xiaofei & Sun, Quan & Kou, Xiao & Ma, Wentao & Zhang, Hong & Liu, Rui, 2022. "Noise immune state of charge estimation of li-ion battery via the extreme learning machine with mixture generalized maximum correntropy criterion," Energy, Elsevier, vol. 239(PD).

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