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Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Author

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  • Sadiqa Jafari

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

  • Zeinab Shahbazi

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

Abstract

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

Suggested Citation

  • Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach," Energies, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4753-:d:850945
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    References listed on IDEAS

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    1. 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.
    2. Liu, Donglei & Wang, Shunli & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2024. "An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures," Energy, Elsevier, vol. 304(C).
    3. Milosz Smolarczyk & Jakub Pawluk & Alicja Kotyla & Sebastian Plamowski & Katarzyna Kaminska & Krzysztof Szczypiorski, 2023. "Machine Learning Algorithms for Identifying Dependencies in OT Protocols," Energies, MDPI, vol. 16(10), pages 1-24, May.

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