Hybrid machine learning models for improved state of health estimation of lithium-ion batteries in electric vehicles
Namrata Mohanty, Neeraj Kumar Goyal, V.N. Achutha Naikan
Abstract. The operation of electric vehicles (EVs) across diverse conditions requires robust state of health (SOH) estimation models for battery reliability and safety. This research conducts a comprehensive evaluation of lithium-ion battery (LIB) SOH prediction models, examining the impact of varied discharge rates (C/20, 1C, 2C, and 3C) and temperatures (0°C, 25°C, and 35°C). Utilizing a novel univariate mean square error (MSE) feature selection approach with random forest regression (RF), influential degradation factors, including voltage, state of charge (SOC), surface temperature and cycles are identified. Two hybrid machine learning (ML) models, integrating XGBoost (XG) with artificial neural network (ANN) and RF are proposed for SOH estimation, which show enhanced predictive accuracy and scalability. A comparative analysis with additional ML models (RF, AdaBoost, XGBoost, and decision tree) demonstrates superior performance of these proposed hybrid models. At C/20, XG-ANN achieves MSE values of 0.2235, 0.4677, and 1.18 at 5℃, 25℃, and 35℃, while XG-RF records MSE of 0.0312, 0.0006, and 0.0006 at 5℃, 25℃, and 35℃ with an accuracy of 99.99%, showcasing their efficacy in robust SOH estimation. These insights contribute to advancing safe and sustainable battery management in the electric mobility industry.
Keywords
Lithium-Ion Batteries (LIBS), State of Health (SOH), Machine Learning (ML), Hybrid Models, Battery Degradation
Published online 3/1/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Namrata Mohanty, Neeraj Kumar Goyal, V.N. Achutha Naikan, Hybrid machine learning models for improved state of health estimation of lithium-ion batteries in electric vehicles, Materials Research Proceedings, Vol. 49, pp 39-47, 2025
DOI: https://doi.org/10.21741/9781644903438-5
The article was published as article 5 of the book Mechanical Engineering for Sustainable Development
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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