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State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis

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  • Duan, Linchao
  • Zhang, Xugang
  • Jiang, Zhigang
  • Gong, Qingshan
  • Wang, Yan
  • Ao, Xiuyi

Abstract

Accurate estimation of the state of charge (SOC) of a lithium-ion battery is important to ensure the safe operation of the battery management system. Adaptive extended Kalman filter (AEKF) is used to estimate SOC, which approximates nonlinear function to linear function by first-order Taylor expansion with large truncation error. Therefore, the second-order AEKF is proposed to reduce the truncation error and improve the accuracy of SOC estimation. Since the estimation accuracy of second-order AEKF is also affected by the sliding window length (SWL), correspondence analysis is proposed in this paper to verify the correlation between SWL and algorithm errors and obtain a reasonable SWL parameter value, which helps to ensure that the algorithm has higher accuracy in SOC estimation under the condition that the value of SWL is not changed when the working condition changes. To substantiate the efficacy of the algorithm outlined in this paper, data sets collected from different sources and at various temperatures are employed. The experimental results obtained through meticulous analysis demonstrate that the second-order AEKF proposed in this study excels in terms of estimation accuracy and robustness.

Suggested Citation

  • Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015530
    DOI: 10.1016/j.energy.2023.128159
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    References listed on IDEAS

    as
    1. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    2. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    3. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    4. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).
    5. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    6. Li, Yihuan & Li, Kang & Liu, Xuan & Li, Xiang & Zhang, Li & Rente, Bruno & Sun, Tong & Grattan, Kenneth T.V., 2022. "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements," Applied Energy, Elsevier, vol. 325(C).
    7. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    8. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    9. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    10. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    11. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    12. Bockrath, Steffen & Lorentz, Vincent & Pruckner, Marco, 2023. "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles," Applied Energy, Elsevier, vol. 329(C).
    13. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    14. Zhengxin, Jiang & Qin, Shi & Yujiang, Wei & Hanlin, Wei & Bingzhao, Gao & Lin, He, 2021. "An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery," Energy, Elsevier, vol. 230(C).
    15. Xi, Zhimin & Wang, Rui & Fu, Yuhong & Mi, Chris, 2022. "Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons," Applied Energy, Elsevier, vol. 305(C).
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    3. Chen, Laien & Zeng, Xiaoyong & Xia, Xiangyang & Sun, Yaoke & Yue, Jiahui, 2024. "A modeling and state of charge estimation approach to lithium-ion batteries based on the state-dependent autoregressive model with exogenous inputs," Energy, Elsevier, vol. 300(C).

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