A review of AI techniques for intelligent electric vehicle battery management
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Full Text |
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Author |
Murkur Rajesh, Shaik Hussain Vali, Pradyumna Kumar Dhal, A. Naresh, Sadhu Radha Krishna and Vempalle Rafi
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e-ISSN |
1819-6608 |
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On Pages
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2084-2094
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Volume No. |
20
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Issue No. |
24
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Issue Date |
February 20, 2026
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DOI |
https://doi.org/10.59018/1225231
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Keywords |
artificial intelligence (AI), battery management system (BMS), electric vehicles, machine learning, state of charge, state of health.
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Abstract
Electric vehicles (EVs) have received a lot of attention from the automotive industry as a means to decrease carbon emissions and address environmental issues on a worldwide scale. But EV efficiency can take a hit as battery health and performance inevitably decline. Many people are interested in artificial intelligence (AI) methods because of their potential to improve electric vehicle (EV) safety, dependability, and performance by properly assessing battery health, analyzing problems, and controlling temperature. This research delves into the impacts of artificial intelligence approaches on electric vehicle (EV) battery management systems (BMSs) and assesses their efficacy.A statistical analysis of pertinent BMS publications is conducted using a number of approaches. Current research trends, authorship, cooperation, publishers, keyword analysis, and research categorization are among the important features that the statistical analysis evaluates. In addition, the aims, contributions, benefits, and drawbacks of advanced AI approaches are thoroughly examined. Moreover, a plethora of important issues and problems are highlighted, in addition to several important guidelines and suggestions for possible future improvement. The statistical study might serve as a roadmap for future academics looking to create sustainable, innovative BMS technology for electric vehicles.
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