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Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

Author

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  • Iacopo Marri

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Emil Petkovski

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Loredana Cristaldi

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Marco Faifer

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

Abstract

Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset.

Suggested Citation

  • Iacopo Marri & Emil Petkovski & Loredana Cristaldi & Marco Faifer, 2023. "Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach," Energies, MDPI, vol. 16(11), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4423-:d:1159892
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    References listed on IDEAS

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    1. Li, Xiaoyu & Fan, Guodong & Rizzoni, Giorgio & Canova, Marcello & Zhu, Chunbo & Wei, Guo, 2016. "A simplified multi-particle model for lithium ion batteries via a predictor-corrector strategy and quasi-linearization," Energy, Elsevier, vol. 116(P1), pages 154-169.
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    1. Emil Petkovski & Iacopo Marri & Loredana Cristaldi & Marco Faifer, 2023. "State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression," Energies, MDPI, vol. 17(1), pages 1-14, December.

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