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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. Xiaoli Sun & Zhengguo Li & Xiaolin Wang & Chengjiang Li, 2019. "Technology Development of Electric Vehicles: A Review," Energies, MDPI, vol. 13(1), pages 1-29, December.
    2. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    3. Zhang, Xinghui & Li, Zhao & Luo, Lingai & Fan, Yilin & Du, Zhengyu, 2022. "A review on thermal management of lithium-ion batteries for electric vehicles," Energy, Elsevier, vol. 238(PA).
    4. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    5. Burke, Andrew & Miller, Marshall, 2009. "Performance Characteristics of Lithium-ion Batteries of Various Chemistries for Plug-in Hybrid Vehicles," Institute of Transportation Studies, Working Paper Series qt3mc7g3vt, Institute of Transportation Studies, UC Davis.
    6. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    7. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    8. Bishop, Justin D.K. & Martin, Niall P.D. & Boies, Adam M., 2014. "Cost-effectiveness of alternative powertrains for reduced energy use and CO2 emissions in passenger vehicles," Applied Energy, Elsevier, vol. 124(C), pages 44-61.
    9. Tan, P. & Jiang, H.R. & Zhu, X.B. & An, L. & Jung, C.Y. & Wu, M.C. & Shi, L. & Shyy, W. & Zhao, T.S., 2017. "Advances and challenges in lithium-air batteries," Applied Energy, Elsevier, vol. 204(C), pages 780-806.
    10. Wang, Kangkang & Gao, Fei & Zhu, Yanli & Liu, Hao & Qi, Chuang & Yang, Kai & Jiao, Qingjie, 2018. "Internal resistance and heat generation of soft package Li4Ti5O12 battery during charge and discharge," Energy, Elsevier, vol. 149(C), pages 364-374.
    Full references (including those not matched with items on IDEAS)

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    2. 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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