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State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression

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  • Li, Fang
  • Min, Yongjun
  • Zhang, Ying
  • Zhang, Yong
  • Zuo, Hongfu
  • Bai, Fang

Abstract

Gaussian process regression (GPR) is extensively employed in lithium-ion battery state-of-health (SOH) estimation, which ensures the safe, reliable operation of electric vehicles (EVs). However, a single GPR can produce performance discrepancies across different fast-charge batteries, as well as high time consumption. Therefore, we propose an SOH estimation method for fast-charging batteries based on stacking ensemble sparse Gaussian process regression (SGPR). First, health factors are extracted in partial discharge fragments to reflect battery degradation. Then, SGPRs based on the fully independent training condition (FITC) are developed with different kernel functions as level-1 learners, and a genetic algorithm (GA) is used to optimize the parameters of the kernel function. Further, the level-2 learner integrates the features produced by the level-1 learner based on cross validation. Finally, the accuracy, robustness, and reliability of the proposed method were evaluated under various fast-charging experiments. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation were within 1.0852% and 1.2123%, respectively, and that the average relative time consumption was reduced by 85.68% compared with stacking ensemble GPR. Thus, the proposed method has broad application prospects in processing vast datasets from numerous batteries in monitoring platforms or cloud data centers.

Suggested Citation

  • Li, Fang & Min, Yongjun & Zhang, Ying & Zhang, Yong & Zuo, Hongfu & Bai, Fang, 2024. "State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023007019
    DOI: 10.1016/j.ress.2023.109787
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    References listed on IDEAS

    as
    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. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    4. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. 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.
    7. 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).
    8. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    11. 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.
    12. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    13. 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).
    14. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    15. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    17. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    18. Hou, WanJun & Peng, Yizhen, 2023. "Adaptive ensemble gaussian process regression-driven degradation prognosis with applications to bearing degradation," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    19. Gandoman, Foad H. & Jaguemont, Joris & Goutam, Shovon & Gopalakrishnan, Rahul & Firouz, Yousef & Kalogiannis, Theodoros & Omar, Noshin & Van Mierlo, Joeri, 2019. "Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: Basics, progress, and challenges," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. 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).
    21. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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