Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review

• Published in: Journal of Electrical and Computer Engineering Vol. 2025; no. 1
• Main Authors: Sathish, J, Ramash, Kumar K, Saraswathi, D
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 01.01.2025; Wiley
• Subjects: Aging; Algorithms; Alternative energy sources; Artificial intelligence; Automobiles, Electric; Batteries; Climate change; Data analysis; Data mining; Data processing; Deep learning; Electric vehicles; Emission standards; Emissions; Energy resources; Fault detection; Fault diagnosis; Fossil fuels; Literature reviews; Lithium; Machine learning; Neural networks; Nuclear energy; Parameters; Power management; Real time; Reliability; State of charge; Thermal management; Transportation equipment industry; Transportation services; China
• ISSN: 2090-0147; 2090-0155; 2090-0155
• DOI: 10.1155/jece/9962670

Electric vehicles (EVs) are a promising zero‐emission technology in the automobile industry, but they face several challenges in terms of performance, reliability, and safety. Batteries are the heart of the EV system which helps to run the vehicle with reliability. Batteries during the process of running undergo various changes that need to be addressed. On the other hand, real‐time data analysis and online access to information are necessary conditions in the modern world. Machine learning and deep learning algorithms mimic humans by focusing on statistical data and algorithms on a real‐time basis. Therefore, in today’s research, machine learning and deep learning algorithms are used in EV technologies to obtain a more efficient and capable system. The battery management system (BMS) is the main part that is often in need of data processing of battery parameters and diagnosis of the problem. This paper explores the comprehensive literature review on machine learning and deep learning approaches for BMS in EVs. The state of charge (SOC) estimation, charge equalization and cell balancing, fault detection and diagnosis, and thermal management systems using various combined machine learning and deep learning techniques are discussed. By synthesizing insights from various studies, this article presents improved parameters and valuable inferences. This article aims to highlight the pivotal role of artificial intelligence (AI) and deep learning in improving the functionality of the BMS, ultimately contributing to the performance and longevity of EVs.