Journal of Soft Computing Paradigm
IRO Journals
Volume 5 — Issue 2 — June 2023

A Comparative Study of Machine Learning-based Approaches for Battery Prognostic Health Analysis using MATLAB

https://doi.org/10.36548/jscp.2023.2.002
Aravind M
Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Hemkumar G
Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Karun RS
Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Mohammed Siddique M
Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Vasanthan B
Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
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How to Cite

M, Aravind, Hemkumar G, Karun RS, Mohammed Siddique M, and Vasanthan B. 2023. “A Comparative Study of Machine Learning-Based Approaches for Battery Prognostic Health Analysis Using MATLAB”. Journal of Soft Computing Paradigm 5 (2): 95-115. https://doi.org/10.36548/jscp.2023.2.002.
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Keywords

— Battery health
— Machine learning
— MATLAB
— Predictive modelling
— Battery management system
Published: 24-05-2023

Abstract

Battery health analysis is crucial for the efficient and reliable operation of battery-powered systems, such as electric vehicles and renewable energy systems. In recent years, machine learning techniques have gained significant attention for battery health analysis due to their ability to handle complex and nonlinear relationships in battery data. In this study, a machine learning-based approach for battery health analysis using MATLAB has been presented. To analyze battery data, a combination of unsupervised and supervised machine learning, not excluding support vector machines, k- means clustering, principal component analysis and decision tree, has been employed. The efficacy of the technique is illustrated by using experimental battery data to show that it can properly estimate battery health and identify potential degradation causes. This approach can be easily integrated into battery management systems to improve performance and extend the life of batteries in various applications.

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