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Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework

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  • Lyu, Zhiqiang
  • Wang, Geng
  • Gao, Renjing

Abstract

This study proposes a hybrid kernel function relevance vector machine (HKRVM) optimized model for battery prognostics and health management. To monitor battery state of health (SOH), two ageing features (AFs) are extracted from the incremental capacity curve to quantify capacity degradation. To further predict remaining useful life (RUL), the AFs are treated with the BOXCOX transformation to enhance the linearity between AFs and cycles. Then, a metabolic extreme learning machine is developed to successionally predict the degradation trends of AFs quickly and accurately. The HKRVM is proposed to capture the underlying relationship between AFs and capacity. To determine the optimal weights and kernel parameters in HKRVM, the biological evolution in the genetic algorithm (GA) is integrated into the grey wolf optimizer (GWO) to further improve the population diversity and optimization performance of the basic GWO.

Suggested Citation

  • Lyu, Zhiqiang & Wang, Geng & Gao, Renjing, 2022. "Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007551
    DOI: 10.1016/j.energy.2022.123852
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    References listed on IDEAS

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    1. Son, Junbo & Zhou, Shiyu & Sankavaram, Chaitanya & Du, Xinyu & Zhang, Yilu, 2016. "Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 38-50.
    2. 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).
    3. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
    4. 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).
    5. 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).
    6. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    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. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    9. 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).
    10. 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.
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    Cited by:

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    2. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    3. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    4. 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).
    5. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    6. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).

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