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State of health estimation for lithium-ion battery based on energy features

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  • Gong, Dongliang
  • Gao, Ying
  • Kou, Yalin
  • Wang, Yurang

Abstract

There is a recognized need to forecast lithium-ion batteries' state of health (SOH) to guarantee their safety and reliability. However, the selected health indicators highly influence the prognostics accuracy of SOH. This paper's primary purpose is to assess the applicability and prediction accuracy of the proposed energy features-based SOH estimation model for different lithium-ion batteries under varied charging and discharging scenarios. These health indicators are energy in the constant current (CC) charging phase, constant voltage (CV) charging stage, and energy in the equal discharge voltage interval (EDVI). The proposed SOH estimation model employs a machine learning algorithm based on Gaussian process regression (GPR). The validation scheme utilizes two data training modes. In addition, data sets from MIT, CALCE, NASA, and Oxford containing different charge and discharge conditions and lithium-ion battery types are adopted. The experimental results reveal that the prediction errors are less than 0.5% for both training modes, while the coefficient of determination (R2) is more than 97%. In addition, 95% of tested cells had an R2 value of more than 98%. This research suggests that the proposed energy feature-based SOH estimation model has high prediction accuracy and excellent generalization ability.

Suggested Citation

  • Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222017157
    DOI: 10.1016/j.energy.2022.124812
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    References listed on IDEAS

    as
    1. Wang, Limei & Cheng, Yong & Zhao, Xiuliang, 2015. "A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles," Applied Energy, Elsevier, vol. 142(C), pages 293-302.
    2. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    3. Tang, Xiaopeng & Zou, Changfu & Yao, Ke & Lu, Jingyi & Xia, Yongxiao & Gao, Furong, 2019. "Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method," Applied Energy, Elsevier, vol. 254(C).
    4. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    6. Hong-jie Xing & Ming-hu Ha & Bao-gang Hu & Da-zeng Tian, 2009. "Linear feature-weighted support vector machine," Fuzzy Information and Engineering, Springer, vol. 1(3), pages 289-305, September.
    7. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    8. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2009. "Knowledge based proximal support vector machines," European Journal of Operational Research, Elsevier, vol. 195(3), pages 914-923, June.
    9. 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).
    10. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    11. Lin, Mingqiang & Yan, Chenhao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2022. "Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression," Energy, Elsevier, vol. 250(C).
    12. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    13. Hong-jie Xing & Ming-hu Ha & Bao-gang Hu & Da-zeng Tian, 2009. "Linear Feature-weighted Support Vector Machine," Fuzzy Information and Engineering, Taylor & Francis Journals, vol. 1(3), pages 289-305, September.
    14. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    15. 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).
    16. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    17. Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
    18. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    19. 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).
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    2. Xinfeng Zhang & Xiangjun Li & Kaikai Yang & Zhongyi Wang, 2023. "Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus," Mathematics, MDPI, vol. 11(15), pages 1-15, August.
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    4. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).

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