Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles
Keywords:
Battery Management System, Electric Vehicles, State of Charge, Machine Learning, Lithium-ion BatteriesSynopsis
This is a Chapter in:
Book:
Competitive Tools, Techniques, and Methods
Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9
Series:
Chronicle of Computing
Chapter Abstract:
Developing an effective and accurate battery management system that can predict the state of charge of electric vehicles is essential to enhance the safety and efficiency of electric vehicles. The assessment of the state of charge of the battery is important not only for determining the amount of energy that is available from the battery but also for determining how long the battery will last. This paper provides a brief understanding of how the state of charge estimation was predicted before and after the era of Machine Learning (ML). In addition, it proposes an accurate and fast state of charge estimation for lithium-ion batteries in electric vehicle applications using machine learning. The proposed model is designed to be generalizable across various inputs, applicable to both new and old batteries, and robust under different charging and discharging scenarios. Model performance and accuracy were evaluated using predefined metrics such as Root Mean Square Error and Mean Absolute Error. Among the four machine learning algorithms that achieved an approximate error of 0.75, while Multiple Linear Regression (MLR) model was chosen for its lightness and speed in training and testing. The findings of this research contribute to the advancement of Battery Management System (BMS) design and implementation for enhancing the efficiency and safety of Lithium-Ion Batteries (LIBs) in real-world driving scenarios. In the future, further research might be conducted to study the implementation of a variety of deep learning algorithms, as well as the estimation of battery health and the remaining useful life.
Cite this paper as:
Eid A. M., Nassif A. B., Ghenai C., Youssef H. Y., Alkhaja L. A., Almazrouei H. H. (2024). Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAPE24#11. https://doi.org/10.55432/978-1-6692-0007-9_12
Presented at:
The 2024 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 3, 2024
Contact:
Ali Bou Nassif
anassif@sharjah.ac.ae
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