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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\u2010time 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\u2010time basis. Therefore, in today\u2019s 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.<\/jats:p>","DOI":"10.1155\/jece\/9962670","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T04:05:20Z","timestamp":1746849920000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review"],"prefix":"10.1155","volume":"2025","author":[{"given":"Sathish","family":"J.","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-520X","authenticated-orcid":false,"given":"Ramash Kumar","family":"K.","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0602-0075","authenticated-orcid":false,"given":"Saraswathi","family":"D.","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"e_1_2_8_1_2","unstructured":"Precedence Research 2023 11 Transportation Services Market (By Purpose: Commuter Travel Tourism and Leisure Travel Business Travel Cargo and Freight Travel Shipping and Delivery Travel; By Destination: Domestic International; By Type: Public Buses Electric Buses Subways Taxis Auto Rickshaws Ferries Other Public Transport Vehicles)-Global Industry Analysis Size Share Growth Trends Regional Outlook and Forecast 2023-2032"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2020.10.739"},{"key":"e_1_2_8_3_2","unstructured":"Transport Chapter 10 in Climate Change 2022: Mitigation of Climate Change 2022."},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"GuarnieriM. 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