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Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism

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  • Wang, Fu-Kwun
  • Amogne, Zemenu Endalamaw
  • Chou, Jia-Hong
  • Tseng, Cheng

Abstract

As battery management systems are widely used in industrial applications, it is important to accurately predict the online remaining useful life (RUL) of batteries. Due to side reactions, the battery will continue to decline in capacity and internal resistance throughout its life cycle. Additionally, battery systems require reliable and accurate battery health diagnostics and timely maintenance and replacement. To obtain accurate RUL prediction, we propose a bidirectional long short-term memory with attention mechanism (Bi-LSTM-AM) model to predict online RUL by continuously updating the model parameters. In this study, normalized capacity was used as state of health (SOH). Multi-step ahead prediction using a sliding window method was used to obtain the SOH estimates. Six cylindrical and prismatic lithium-ion (Li-ion) batteries were used to evaluate the performance of the proposed model. Using our online RUL prediction model, the relative errors for the six Li-ion batteries are 0.57%, 0.54%, 0.56%, 0%, 1.27% and 1.41%, respectively. To evaluate the reliability of the proposed model, the prediction interval for the RUL prediction is also provided using the Monte Carlo dropout approach.

Suggested Citation

  • Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012476
    DOI: 10.1016/j.energy.2022.124344
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    5. Wang, Jiaolong & Zhang, Fode & Zhang, Jianchuan & Liu, Wen & Zhou, Kuang, 2023. "A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    7. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    8. 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).
    9. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).

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