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Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network

Author

Listed:
  • Xu, Xiaodong
  • Tang, Shengjin
  • Han, Xuebing
  • Lu, Languang
  • Wu, Yu
  • Yu, Chuanqiang
  • Sun, Xiaoyan
  • Xie, Jian
  • Feng, Xuning
  • Ouyang, Minggao

Abstract

Accurate and robust capacity prediction is significant for battery management system to identify the state of health and life condition for lithium-ion batteries. This paper proposes a fast capacity prediction method by developing a novel deep aging mechanism-informed bidirectional long-short term memory (AM-Bi-LSTM) neural network. Firstly, a physical informed aging mechanism (AM) layer is established with the random charging curve sequences as input to identify the degradation features. Then the deep learning framework with two bidirectional long-short term memory (Bi-LSTM) layers is built to reflect the entire constant current charging curves and predict the battery capacity. In which, the battery aging mechanism is integrated into the artificial intelligence algorithm of capacity prediction for the first time. Several case studies are implemented to verify the effectiveness of developed method, and the influence of voltage window length on capacity prediction is further discussed. The results demonstrate that the charging curves can be accurately and fast captured with a capacity prediction root mean square error of less than 0.49% for 0.74 Ah batteries with 50 mV voltage window charging points collected in only less than 2.09 minutes mean cost time in the whole life cycle. It shows the proposed aging mechanism-informed data-driven prediction method has stronger robustness, faster prediction speed and higher accuracy compared with other data-driven methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s095183202300100x
    DOI: 10.1016/j.ress.2023.109185
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    2. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Xiao, Renxin & Shen, Jiangwei & Liu, Yu & Liu, Yonggang, 2024. "Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    4. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Dudu Guo & Miao Sun & Qingqing Wang & Jinquan Zhang, 2024. "Taxi Demand Method Based on SCSSA-CNN-BiLSTM," Sustainability, MDPI, vol. 16(18), pages 1-21, September.

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