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A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

Author

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  • Shehzar Shahzad Sheikh

    (U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Mahnoor Anjum

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Muhammad Abdullah Khan

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Syed Ali Hassan

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Hassan Abdullah Khalid

    (U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Adel Gastli

    (Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar)

  • Lazhar Ben-Brahim

    (Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar)

Abstract

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

Suggested Citation

  • Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3658-:d:384986
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    References listed on IDEAS

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    Cited by:

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    2. Li, Jinwen & Deng, Zhongwei & Liu, Hongao & Xie, Yi & Liu, Chuan & Lu, Chen, 2022. "Battery capacity trajectory prediction by capturing the correlation between different vehicles," Energy, Elsevier, vol. 260(C).
    3. Zhou, Yong & Dong, Guangzhong & Tan, Qianqian & Han, Xueyuan & Chen, Chunlin & Wei, Jingwen, 2023. "State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    4. Li, Xiaoyu & Lyu, Mohan & Li, Kuo & Gao, Xiao & Liu, Caixia & Zhang, Zhaosheng, 2023. "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning," Energy, Elsevier, vol. 282(C).
    5. Sumukh Surya & Vidya Rao & Sheldon S. Williamson, 2021. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application," Energies, MDPI, vol. 14(15), pages 1-22, July.
    6. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).
    7. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).

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