Smart Control and Analysis of EV Battery Systems for Enhanced Performance and Longevity

This research investigates the application of Random Forest regression for predicting the lifetime of electric vehicle (EV) batteries. By analyzing various factors influencing battery degradation, such as charging/discharging cycles, temperature, and usage patterns, the Random Forest model can effectively predict battery lifespan. This research contributes to the advancement of EV technology by providing valuable insights into battery degradation and enabling more accurate predictions of battery lifespan. These predictions will facilitate better battery management strategies, improve vehicle performance, and enhance the overall adoption of electric vehicles.