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Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

Author

Listed:
  • Sadiqa Jafari

    (Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
    These authors contributed equally to this work.
    Department of Electronic Engineering, Institute of Information Science Technology, Jeju National University, Jeju 63243, Korea.)

  • Zeinab Shahbazi

    (Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
    These authors contributed equally to this work.)

  • Yung-Cheol Byun

    (Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea)

  • Sang-Joon Lee

    (Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea)

Abstract

The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

Suggested Citation

  • Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:888-:d:768530
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    References listed on IDEAS

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    Cited by:

    1. Xin Zhang & Jiawei Hou & Zekun Wang & Yueqiu Jiang, 2022. "Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Yaoyidi Wang & Niansheng Chen & Guangyu Fan & Dingyu Yang & Lei Rao & Songlin Cheng & Xiaoyong Song, 2023. "DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches," Mathematics, MDPI, vol. 11(22), pages 1-21, November.
    3. Taysa Millena Banik Marques & João Lucas Ferreira dos Santos & Diego Solak Castanho & Mariane Bigarelli Ferreira & Sergio L. Stevan & Carlos Henrique Illa Font & Thiago Antonini Alves & Cassiano Moro , 2023. "An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles," Energies, MDPI, vol. 16(13), pages 1-18, June.
    4. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections," Mathematics, MDPI, vol. 10(16), pages 1-16, August.

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