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Multivariate stacked bidirectional long short term memory for lithium-ion battery health management

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  • Ardeshiri, Reza Rouhi
  • Liu, Ming
  • Ma, Chengbin

Abstract

Prognostics and health management (PHM) will ensure the safe and reliable operation of the battery systems. The remaining useful life (RUL) prediction as one of the major PHM strategies gives early warning of faults, which can be applied to recognize the necessary battery maintenance and replacement in advance. This study investigates a novel deep learning method for predicting lithium-ion battery RUL, which can learn the long-term dependency of degradation trend of batteries. This is the first time which a stacked bidirectional long short-term memory (SBLSTM) based on extreme gradient boosting (XGBoost) is applied to predict the battery capacity degradation trajectories. Using the XGBoost technique, important time-domain features are selected as multivariate inputs to feed the deep learning model for predicting. To improve the trained model, Bayesian optimization (BO) is also performed to tune the hyper-parameters. The findings show that the SBLSTM model achieves a low root mean square percentage error of 1.94%, which is lower than the state-of-the-art methods due to two-way learning. The suggested model will provide excellent support for the maintenance strategy development and health management of the battery systems.

Suggested Citation

  • Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001430
    DOI: 10.1016/j.ress.2022.108481
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    Cited by:

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    5. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. 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).
    7. 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).
    8. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Meng, Fanbing & Yang, Fangfang & Yang, Jun & Xie, Min, 2023. "A power model considering initial battery state for remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. 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).
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