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Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model

Author

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  • Bing Long

    (School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China)

  • Xiangnan Li

    (School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China)

  • Xiaoyu Gao

    (School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China)

  • Zhen Liu

    (School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China)

Abstract

Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.

Suggested Citation

  • Bing Long & Xiangnan Li & Xiaoyu Gao & Zhen Liu, 2019. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model," Energies, MDPI, vol. 12(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3271-:d:260842
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    References listed on IDEAS

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    1. Xiaoqiong Pang & Rui Huang & Jie Wen & Yuanhao Shi & Jianfang Jia & Jianchao Zeng, 2019. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon," Energies, MDPI, vol. 12(12), pages 1-14, June.
    2. Bowen Jia & Yong Guan & Lifeng Wu, 2019. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis," Energies, MDPI, vol. 12(13), pages 1-14, June.
    3. Nicholas Williard & Wei He & Christopher Hendricks & Michael Pecht, 2013. "Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability," Energies, MDPI, vol. 6(9), pages 1-14, September.
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    Cited by:

    1. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Yong Li & Jue Yang & Wei Long Liu & Cheng Lin Liao, 2020. "Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery," Energies, MDPI, vol. 13(15), pages 1-23, July.
    3. Dawei Song & Shiqian Wang & Li Di & Weijian Zhang & Qian Wang & Jing V. Wang, 2023. "Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions," Energies, MDPI, vol. 16(2), pages 1-13, January.
    4. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    5. Konstantin Zadiran & Maxim Shcherbakov, 2023. "New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment," Energies, MDPI, vol. 16(2), pages 1-21, January.
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    7. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 12(19), pages 1-22, September.

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