Electric Vehicle Range Prediction

Mannava Divya Teja; Nuthalapati Ganesh; Mutyala Bhuvan Saieesh

doi:10.37591/.v13i3.7652

Abstract

The introduction of new energy vehicles has emerged as a new trend in the automotive industry inresponse to growing energy and environmental issues. The electric vehicle (EV) is the driving force behind newenergy vehicles. The one major problem electric vehicles have always been the distance(range) of the travel and mapto the nearby charging stations. For range prediction in the present study, four machine learningalgorithms—multiple linear regression, random forest regression, polynomial regression, and support vectorregression—are employed and contrasted with the most recent research. Python implementation is used for asimulation model that takes the coordinates of the starting point, endpoint, Map of the surrounding location land, andthe locations of charging stations to predict an optimal path for the vehicle to travel. We use an A* path-findingalgorithm to traverse the black-and-white map and find the optimal path from source to destination. This workproposes a novel Machine learning (ML) based predictive strategy to estimate the driving range of electric vehiclesand route them to the nearby charging stations. The range predictor considers the specific vehicle parameters over adistributed network of charging stations. The charging stations are modeled as entities with many charging pointsand, in each stage of the prediction the status of availability of charging points is monitored and updated to a clouddatabase. A better overview of the driving range and vehicle’s energy consumption may help reduce the overall rangeanxiety of many EV drivers.