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An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

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  • Zhang, Jiusi
  • Jiang, Yuchen
  • Li, Xiang
  • Huo, Mingyi
  • Luo, Hao
  • Yin, Shen

Abstract

Accurate prediction of the remaining useful life (RUL) of lithium-ion battery is of great significance for the reliability of electronic equipment. In the conventional approaches, there are notable challenges in the RUL prediction for a single battery lacking historical data. To predict the battery’s RUL under the condition of unlabeled small sample data and to describe the uncertainty of the parameter estimation in the degradation model, a novel adaptive approach based on Kalman filter and expectation maximum with Rauch–Tung–Striebel (KF-EM-RTS) is proposed to predict the battery’s RUL. Specifically, without RUL labels and offline training, an online KF adaptive-update model based on the Wiener process is proposed for a single battery, in which the uncertainty of parameter estimation is described. Furthermore, the unknown model parameters can be adaptively estimated using EM-RTS to overcome the constraints of strong Markov characteristics, the convergence of which is proved. The real-world battery dataset provided by NASA Ames research center is applied to verify the proposed RUL prediction approach. Experimental results show that the proposed approach outperforms the existing conventional data-driven approaches for predicting the battery’s RUL.

Suggested Citation

  • Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000369
    DOI: 10.1016/j.ress.2022.108357
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    1. 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.
    2. Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Lee, Dooyoul & Choi, Dongsu, 2020. "Analysis of the reliability of a starter-generator using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Zhou, Di & Zheng, Wenbin & Chen, Shaohui & Fu, Ping & Zhu, Hongyu & Song, Bai & Qu, Xisong & Wang, Tiancheng, 2021. "Research on state of health prediction model for lithium batteries based on actual diverse data," Energy, Elsevier, vol. 230(C).
    6. Mishra, Madhav & Martinsson, Jesper & Rantatalo, Matti & Goebel, Kai, 2018. "Bayesian hierarchical model-based prognostics for lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 25-35.
    7. 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).
    8. Li, Sai & Fang, Huajing & Shi, Bing, 2021. "Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    9. 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.
    10. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
    11. Lorenzo, Charles & Bouquain, David & Hibon, Samuel & Hissel, Daniel, 2021. "Synthesis of degradation mechanisms and of their impacts on degradation rates on proton-exchange membrane fuel cells and lithium-ion nickel–manganese–cobalt batteries in hybrid transport applicati," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    12. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    Cited by:

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    3. Wang, Yu & Liu, Qiufa & Lu, Wenjian & Peng, Yizhen, 2023. "A general time-varying Wiener process for degradation modeling and RUL estimation under three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Lin, Wenyi & Chai, Yi & Fan, Linchuan & Zhang, Ke, 2024. "Remaining useful life prediction using nonlinear multi-phase Wiener process and variational Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. 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).
    10. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    11. 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).
    12. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    13. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Shao, Xiaoyan & Cai, Baoping & Gao, Lei & Zhang, Yanping & Yang, Chao & Gao, Chuntan, 2024. "Data-model-linked remaining useful life prediction method with small sample data: A case of subsea valve," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    15. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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