Electric Vehicle Range Prediction using Random Forest Regression
B S Sagar, M. Pallikonda Rajasekaran, B Sakthisaravanan, R Jyothi, K. Lalitha, S. Sakthivel Murugan
Abstract
This research introduces a new method for predicting electric vehicles (EVs) range that combines cloud computing with random forest regression (RFR) approaches. Predicting the range properly is now critical for user comfort and efficient route planning due to the rising popularity of EVs. Factors like changing driving conditions and battery depletion might make traditional range calculation techniques inaccurate. It uses the processing power of the cloud to collect and analyze massive volumes of real-time data on variables like traffic, weather, and topography that have an impact on EVs range. Next, it uses a powerful machine learning (ML) algorithm for RFR to accurately forecast EVs range by modeling the intricate interactions between these factors. Results from experiments this technique works, with far better range estimate advanced approaches. Cloud computing scalability and flexibility also make it easy to predict models into preexisting EVs navigation systems, so drivers may get accurate range estimations in real time based on their own circumstances. It will improve EVs’ usability and dependability, which will lead to more people buying and using these eco-friendly vehicles.