Leveraging Artificial Intelligence to Enhance Electric Vehicle Battery Management and Environmental Sustainability

Abstract

Background: The influence of greenhouse gases on global warming due to emissions from fossil-fueled vehicles has become so harsh and significant, calling for global attention due to carbon emissions. The minimisation of energy consumed when the same mileage is covered, while driving together with environmental sustainability, has called for the progressive replacement of fuel vehicles with electric vehicles (EVs). Thus, there is a need to incorporate data analysis and artificial intelligence to improve the quality of EV batteries. The advancement of EV battery quality is critical to achieving sustainable transportation goals and combating climate change. Aim: The study aimed to examine leveraging data analysis and artificial intelligence to enhance EV battery quality and support environmental sustainability. Methodology: Consultation was made with articles, books, journals, preprints, and lots more to source the required information in the area of how the quality of EV batteries can be improved via the application of data analysis and artificial intelligence. Discussion: It is imperative to develop and enhance EV battery quality, which can be achieved via artificial intelligence incorporation into battery management systems. Substantial research outputs have revealed the suitability of existing EV types, the charging techniques, costs, and vehicle emissions that can assist in the shift from fuel-based vehicles to electric vehicles. This review article has presented the fundamental knowledge of EVs’ superiority over traditional fossil-fueled vehicles. Future mobility has been transformed through charging technology improvement and the incorporation of emerging trends. The application of artificial intelligence via battery management system (BMS) to enhance EV battery quality and the methods via which this can be achieved were presented. Conclusion: Data analysis and artificial intelligence have found promising contributions to enhancing the quality of EV batteries while still maintaining a sustainable environment. However, numerous sophisticated calculation issues and elongated processing times have made the incorporation of optimization approaches into AI methods quite challenging, which calls for the attention of future researchers.