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Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

Author

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  • Sahar Khaleghi

    (Department of Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Yousef Firouz

    (Department of Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Maitane Berecibar

    (Department of Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Joeri Van Mierlo

    (Department of Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Peter Van Den Bossche

    (Department of Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

Abstract

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

Suggested Citation

  • Sahar Khaleghi & Yousef Firouz & Maitane Berecibar & Joeri Van Mierlo & Peter Van Den Bossche, 2020. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features," Energies, MDPI, vol. 13(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1262-:d:330238
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    References listed on IDEAS

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    Cited by:

    1. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    2. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    4. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    5. Khaleghi, Sahar & Hosen, Md Sazzad & Van Mierlo, Joeri & Berecibar, Maitane, 2024. "Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).

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