Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review
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 ru...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/jece/9962670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849727257169362944 |
|---|---|
| author | Sathish J. Ramash Kumar K. Saraswathi D. |
| author_facet | Sathish J. Ramash Kumar K. Saraswathi D. |
| author_sort | Sathish J. |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6807048a02f44b86ba809e4c7c7b263b |
| institution | DOAJ |
| issn | 2090-0155 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-6807048a02f44b86ba809e4c7c7b263b2025-08-20T03:09:54ZengWileyJournal of Electrical and Computer Engineering2090-01552025-01-01202510.1155/jece/9962670Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive ReviewSathish J.0Ramash Kumar K.1Saraswathi D.2Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringSchool of Computer Science and EngineeringElectric 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.http://dx.doi.org/10.1155/jece/9962670 |
| spellingShingle | Sathish J. Ramash Kumar K. Saraswathi D. Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review Journal of Electrical and Computer Engineering |
| title | Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review |
| title_full | Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review |
| title_fullStr | Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review |
| title_full_unstemmed | Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review |
| title_short | Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review |
| title_sort | exploring machine learning and deep learning approaches for battery management systems in evs a comprehensive review |
| url | http://dx.doi.org/10.1155/jece/9962670 |
| work_keys_str_mv | AT sathishj exploringmachinelearninganddeeplearningapproachesforbatterymanagementsystemsinevsacomprehensivereview AT ramashkumark exploringmachinelearninganddeeplearningapproachesforbatterymanagementsystemsinevsacomprehensivereview AT saraswathid exploringmachinelearninganddeeplearningapproachesforbatterymanagementsystemsinevsacomprehensivereview |