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Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study

Author

Listed:
  • John Guirguis

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Ryan Ahmed

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

The global transportation system’s need for electrification is driving research efforts to overcome the drawbacks of battery electric vehicles (BEVs). The accurate and reliable estimation of the states of charge (SOC) and health (SOH) of Li-Ion batteries (LIBs) is crucial for the widespread adoption of BEVs. Transformers, cutting-edge deep learning (DL) models, are demonstrating promising capabilities in addressing various sequence-processing problems. This manuscript presents a thorough survey study of previous research papers that introduced modifications in the development of Transformer-based architectures for the SOC and SOH estimation of LIBs. This study also highlights approximately 15 different real-world datasets that have been utilized for training and testing these models. A comparison is made between the architectures, addressing each state using the root mean square error (RMSE) and mean absolute error (MAE) metrics.

Suggested Citation

  • John Guirguis & Ryan Ahmed, 2024. "Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study," Energies, MDPI, vol. 17(14), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3502-:d:1436769
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    References listed on IDEAS

    as
    1. Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu, 2010. "Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms," Energies, MDPI, vol. 3(10), pages 1-19, September.
    2. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    3. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    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. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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