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Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles

Author

Listed:
  • Marat Sadykov

    (School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia
    These authors contributed equally to this work.)

  • Sam Haines

    (School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia)

  • Mark Broadmeadow

    (School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia)

  • Geoff Walker

    (School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia)

  • David William Holmes

    (School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia
    These authors contributed equally to this work.)

Abstract

This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources as possible on openly available battery data. The results highlighted an average percentage accuracy of 96.56% for the correct SoC estimation and several drawbacks of the overall implementation, and we propose potential solutions for further improvement. Every implemented model had a similar drawback, which was the poor capturing of the middle area of charge, applying a higher weight to the voltage than the current. The combination of these techniques into a single custom model could result in a better-suited model, further improving the accuracy.

Suggested Citation

  • Marat Sadykov & Sam Haines & Mark Broadmeadow & Geoff Walker & David William Holmes, 2023. "Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-34, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1628-:d:1059789
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    References listed on IDEAS

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    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. Chaoran Li & Fei Xiao & Yaxiang Fan, 2019. "An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit," Energies, MDPI, vol. 12(9), pages 1-22, April.
    3. 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.
    4. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
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