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A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging

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  • Chen, Junxiong
  • Hu, Yuanjiang
  • Zhu, Qiao
  • Rashid, Haroon
  • Li, Hongkun

Abstract

Efficient battery health indicator (HI) extraction and accurate estimation method are two important issues in the study of battery state of health (SOH) estimation. Although machine learning-based methods have been widely applied to the battery SOH estimation in recent years, the battery HI extraction in most studies is too tedious, the estimation method lacks pertinence, and the aging pattern of the battery aging dataset is simple. To solve the above problems, this paper proposes a novel battery HI based on the charging duration of the equal voltage intervals in the constant current charging process, which can effectively characterize the battery aging characteristics by only 10 continuous charging duration counts directly from the battery management system. Considering the difficulty of collecting battery aging data and the high dimensionality of the extracted HI, the least squares support vector regression (LSSVR), which is suitable for small samples and high dimensional data, is used to build the SOH mapping model and the optimal hyperparameters are found with the help of particle swarm optimization (PSO). The satisfactory SOH estimation accuracy of the proposed method is validated on a public LiFePO4 battery aging dataset containing different temperatures, discharge rates, discharge depths and cycle intervals.

Suggested Citation

  • Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s036054422302176x
    DOI: 10.1016/j.energy.2023.128782
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    1. Maosong Fan & Mengmeng Geng & Kai Yang & Mingjie Zhang & Hao Liu, 2023. "State of Health Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(8), pages 1-14, April.
    2. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    3. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    4. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    5. Pei, Pucheng & Zhou, Qibin & Wu, Lei & Wu, Ziyao & Hua, Jianfeng & Fan, Huimin, 2020. "Capacity estimation for lithium-ion battery using experimental feature interval approach," Energy, Elsevier, vol. 203(C).
    6. 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.
    7. 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).
    8. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    9. 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.
    10. Shen, Jiangwei & Ma, Wensai & Shu, Xing & Shen, Shiquan & Chen, Zheng & Liu, Yonggang, 2023. "Accurate state of health estimation for lithium-ion batteries under random charging scenarios," Energy, Elsevier, vol. 279(C).
    11. Tian, Jiaqiang & Liu, Xinghua & Li, Siqi & Wei, Zhongbao & Zhang, Xu & Xiao, Gaoxi & Wang, Peng, 2023. "Lithium-ion battery health estimation with real-world data for electric vehicles," Energy, Elsevier, vol. 270(C).
    12. 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).
    13. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    14. 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).
    15. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    16. 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).
    17. Chenqiang Luo & Zhendong Zhang & Shunliang Zhu & Yongying Li, 2023. "State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-14, April.
    18. Huang, Huanyang & Meng, Jinhao & Wang, Yuhong & Feng, Fei & Cai, Lei & Peng, Jichang & Liu, Tianqi, 2022. "A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve," Applied Energy, Elsevier, vol. 322(C).
    19. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
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