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Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network

Author

Listed:
  • Fang Guo

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • Guangshan Huang

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • Wencan Zhang

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • An Wen

    (Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Huzhou 313098, China)

  • Taotao Li

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • Hancheng He

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • Haolin Huang

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

  • Shanshan Zhu

    (School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China)

Abstract

Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is restricted. In this regard, this study proposes a method for estimating the SOH of lithium batteries based on sample data generation and a temporal convolutional neural network. First, we analyzed the charge/discharge curves of the batteries, from which we extracted features that were highly correlated with the SOH decay. Then, we used a Variational Auto-Encoder (VAE) to learn the features and distributions of the sample data to generate highly similar data and enrich the number of samples. Finally, a temporal convolutional neural network (TCN) was built to mine the nonlinear relationship between features and SOH by combining the source and extended domain data to realize SOH estimation. The experimental results show that the proposed method in this study has less than 2% error in SOH estimation, which improves the accuracy by 64.9% based on its baseline model. The feasibility of using data-driven models for battery health management in data-constrained application scenarios is demonstrated.

Suggested Citation

  • Fang Guo & Guangshan Huang & Wencan Zhang & An Wen & Taotao Li & Hancheng He & Haolin Huang & Shanshan Zhu, 2023. "Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network," Energies, MDPI, vol. 16(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8010-:d:1298039
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    References listed on IDEAS

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    5. Chunling Wu & Juncheng Fu & Xinrong Huang & Xianfeng Xu & Jinhao Meng, 2023. "Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR," Energies, MDPI, vol. 16(10), pages 1-16, May.
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