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Lithium-Ion Battery State-of-Charge Estimation from the Voltage Discharge Profile Using Gradient Vector and Support Vector Machine

Author

Listed:
  • Erwin Sutanto

    (Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Kampus C Unair Mulyorejo, Surabaya 60115, Indonesia)

  • Putu Eka Astawa

    (East Java Distribution, Perusahaan Listrik Negara, Surabaya 60271, Indonesia)

  • Fahmi Fahmi

    (Department of Electrical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia)

  • Muhammad Imran Hamid

    (Department of Electrical Engineering, Universitas Andalas, Padang 25163, Indonesia)

  • Muhammad Yazid

    (Biomedical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

  • Wervyan Shalannanda

    (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Muhammad Aziz

    (Institute of Industrial Science, The University of Tokyo, Tokyo 153-8550, Japan)

Abstract

The battery monitoring system (BMoS) is crucial to monitor the condition of the battery in supplying and absorbing the energy when operating and simultaneously determine the optimal limits for achieving long battery life. All of this can be done by measuring the battery parameters and increasing the state of charge (SoC) and the state of health (SoH) of the battery. The battery dataset from NASA is used for evaluation. In this work, the gradient vector is employed to obtain the trend of the energy supply pattern from the battery. In addition, a support vector machine (SVM) is adopted for an accurate battery accuracy index. This is in line with the use of polynomial regression; hence, points V1 and V2 are obtained as the boundaries of the normal-usage phase. Furthermore, testing of the time length distribution is also carried out on the length of time the battery was successfully extracted from the classification. All these stages can be used to calculate the rate of battery degradation during use so that this strategy can be applied in real situations by continuously comparing values. In this case, using the voltage gradient, SVM method, and the suggested polynomial regression, MAPE (%), MAE, and RMSE can be obtained against the battery value graph with values of 0.3%, 0.0106, and 0.0136, respectively. With this error value, the dynamics of the SoC value of the battery can be obtained, and the SoH problem can be resolved with a shorter usage time by avoiding the voltage-drop phase.

Suggested Citation

  • Erwin Sutanto & Putu Eka Astawa & Fahmi Fahmi & Muhammad Imran Hamid & Muhammad Yazid & Wervyan Shalannanda & Muhammad Aziz, 2023. "Lithium-Ion Battery State-of-Charge Estimation from the Voltage Discharge Profile Using Gradient Vector and Support Vector Machine," Energies, MDPI, vol. 16(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1083-:d:1040416
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    References listed on IDEAS

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    1. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    2. Richard Bustos & Stephen Andrew Gadsden & Pawel Malysz & Mohammad Al-Shabi & Shohel Mahmud, 2022. "Health Monitoring of Lithium-Ion Batteries Using Dual Filters," Energies, MDPI, vol. 15(6), pages 1-16, March.
    3. Zhengyi Bao & Jiahao Jiang & Chunxiang Zhu & Mingyu Gao, 2022. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 15(12), pages 1-16, June.
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    Cited by:

    1. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).

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