Smart and Sustainable EVs: A Comprehensive Review of Energy Storage, Management, and Conversion Systems

This in-depth research takes into consideration the unification of the postal service and electric vehicle (EV) energy storage technologies and intelligent management system, and power conversion architecture and, respectively, in the EVs to be built in the close future. We are looking at Lithium-ion battery, solid state battery and hybrid extending battery chemistries as well as new supercapacitor technologies and their energy density as well as their lifecycle performances. This research is to evaluate the performance of the battery management systems (BMS) augmented with Deep Reinforcement Learning (DRL) based on Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms using TensorFlow 2.X that resulted in 98.7% of accuracy of the State of Charge prediction over real-time and increased the battery life span by 23%. Advanced DC-DC Converters, bidirectional Charging Infrastructurerez-Infrastructure And Vehicle-to-Grid (V2G) In Integration Strategies Critically Evaluated Our results shows that ML-based predictive analytics coupled with multi-objective optimization tools can be very useful to enhance energy efficiency (up to 18%), thermal degradation and can permit an adaptation of power distribution. This review provides useful information and working knowledge to automotive engineers, researchers and policy makers who are working for greenness of transportation ecosystem.