Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities
M. S. Hossain Lipu,
Md. Sazal Miah,
Taskin Jamal,
Tuhibur Rahman,
Shaheer Ansari,
Md. Siddikur Rahman,
Ratil H. Ashique,
A. S. M. Shihavuddin,
Mohammed Nazmus Shakib
Affiliations
M. S. Hossain Lipu
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
Md. Sazal Miah
School of Engineering Information Technology and Physical Sciences, Federation University Australia, Ballarat, VIC 3353, Australia
Taskin Jamal
Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Tuhibur Rahman
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
Shaheer Ansari
Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Md. Siddikur Rahman
Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Ratil H. Ashique
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
A. S. M. Shihavuddin
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
Mohammed Nazmus Shakib
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.
