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State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter

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  • Bi, Jun
  • Zhang, Ting
  • Yu, Haiyang
  • Kang, Yanqiong

Abstract

Power battery packs are the energy source of battery electric vehicles (BEVs). A precise state-of-health (SOH) estimation for batteries is crucial to ensure the operational security and stability of BEVs. This paper employs an equivalent circuit model of battery pack in SOH estimation. Since a battery pack is a complex and nonlinear system, the equivalent circuit model of battery pack is always complicated. To balance estimation accuracy and computational complexity, the equivalent circuit model of battery pack should be simplified. However, much noise is produced in the simplified model. In addition, the errors during SOH estimation are from various sources so that SOH estimation is a non-Gaussian problem. Given the genetic resampling particle filter (GPF) performs efficiently in solving non-Gaussian problems, this paper proposes a new GPF-based method for battery SOH dynamic estimation when accuracy of the equivalent circuit model is not high. First, a second-order equivalent circuit model of Resistance–Capacitance (RC) circuit for the battery pack is developed. The unknown parameters are identified using the recursive least-squares method with forgetting factor. Second, a state-space model of the GPF is developed based on the equivalent circuit model. Finally, a case study is conducted using real data collected from operating electric taxis in Beijing to investigate the estimation performance of the proposed model. Estimation results show that the proposed GPF model outperforms the particle filter method in the SOH estimation problem.

Suggested Citation

  • Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
    • Handle: RePEc:eee:appene:v:182:y:2016:i:c:p:558-568
    DOI: 10.1016/j.apenergy.2016.08.138
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    References listed on IDEAS

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    Cited by:

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    5. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
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    8. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
    9. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
    10. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    11. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
    12. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
    13. Shuo Sun & Junzhong Sun & Zongliang Wang & Zhiyong Zhou & Wei Cai, 2022. "Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism," Energies, MDPI, vol. 15(12), pages 1-17, June.
    14. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    15. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    16. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    17. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    18. Rahbari, Omid & Omar, Noshin & Firouz, Yousef & Rosen, Marc A. & Goutam, Shovon & Van Den Bossche, Peter & Van Mierlo, Joeri, 2018. "A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm," Energy, Elsevier, vol. 155(C), pages 1047-1058.
    19. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    20. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    21. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
    22. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).

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