Artificial Intelligence Techniques for Electric Vehicle Battery Management Systems – A Critical Review

The automotive industry has given electric cars, or EVs, a lot of attention in an effort to lower carbon emissions and address environmental issues on a global scale. The efficiency of EVs, however, may be negatively impacted by the eventual decline in battery health and performance. Artificial intelligence (AI) techniques have drawn a lot of attention for their capacity to precisely diagnose battery health, analyse faults, and regulate temperature in order to increase the safety, dependability, and effective optimization of EV performance. To enhance the electric vehicles‘ (EV) battery management system (BMS), this study examines and assesses the impact of AI techniques. Different methodologies are used to conduct a statistical analysis from pertinent papers on BMS. Essential characteristics including current research trends, keyword analysis, nation analysis, authorship, collaboration, publishers, and research classification are evaluated by the statistical analysis. Furthermore, the aims, contributions, benefits, and drawbacks of cutting-edge AI techniques are addressed in detail. Furthermore, a number of important guidelines and recommendations, together with a number of noteworthy concerns and challenges, are offered for possible future improvement. Future researchers might use the statistical analysis as a roadmap to create cutting-edge BMS technologies for EVs that operate and manage sustainably.