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A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

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  • Shunli Wang

    (College of Electrical Engineering, Sichuan University, Chengdu 610017, China
    School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China)

  • Pu Ren

    (School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China)

  • Paul Takyi-Aninakwa

    (School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China)

  • Siyu Jin

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede, 111 9220 Aalborg, Denmark)

  • Carlos Fernandez

    (School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen AB10-7GJ, UK)

Abstract

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

Suggested Citation

  • Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5053-:d:860144
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    6. 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).
    7. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. Jen-Hao Teng & Rong-Jhang Chen & Ping-Tse Lee & Che-Wei Hsu, 2023. "Accurate and Efficient SOH Estimation for Retired Batteries," Energies, MDPI, vol. 16(3), pages 1-17, January.
    9. Carlo Villante, 2023. "A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts," Energies, MDPI, vol. 16(7), pages 1-25, March.
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