Utilizing machine Learning Techniques to Predict State-of-Charge in Li-ion Batteries

Conference paper


Khatri, A., Lota, J., Nepal, P. and Amirhosseini, M. H. 2024. Utilizing machine Learning Techniques to Predict State-of-Charge in Li-ion Batteries. IS'24: 12th IEEE International Conference on Intelligent Systems. Varna, Bulgaria 29 - 31 Aug 2024 IEEE. https://doi.org/10.1109/IS61756.2024.10705192
AuthorsKhatri, A., Lota, J., Nepal, P. and Amirhosseini, M. H.
TypeConference paper
Abstract

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustainability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively.

KeywordsLi-ion batteries; battery management system; state-of-charge; machine learning
Year2024
ConferenceIS'24: 12th IEEE International Conference on Intelligent Systems
PublisherIEEE
Accepted author manuscript
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Anyone
Publication dates
Online09 Oct 2024
Publication process dates
Accepted11 May 2024
Deposited08 Oct 2024
Journal citationpp. 1-5
ISSN2767-9802
Book title2024 IEEE 12th International Conference on Intelligent Systems (IS)
ISBN979-8-3503-5098-2
Digital Object Identifier (DOI)https://doi.org/10.1109/IS61756.2024.10705192
Copyright holder© 2024, IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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