AI-Powered EV Consumer Trends and Range Forecasting for Sustainable Mobility

This paper suggests a new approach for range forecasting of electric vehicles that uses cloud computing and random forest regression (RFR) techniques. Due to the increasing popularity of EVs, the accurate forecasting of range is becoming essential for passenger comfort as well as successful route planning. Classical range calculating methods can be incorrect some times because of fluctuating driving conditions and a drain in the battery. It leverages the processing power of the cloud to aggregate and process huge volumes of real-time data on factors such as weather, traffic, and altitude that influence EV range. Then it uses a robust machine learning (ML) algorithm for RFR to reliably forecast EV range by simulating the subtle interactions among these factors. Experiments demonstrate that this method is effective, performing better than sophisticated range estimation methods. Cloud computing's scalability and flexibility also make it easy to integrate forecast models into existing EV navigation systems, enabling drivers to obtain accurate range estimates in real-time based on their conditions. It will enhance the use and reliability of EVs, motivating more individuals to purchase and utilize these eco-friendly vehicles.