Reducing Driver’s Range Anxiety for Electric Vehicle using Machine Learning

Vehicles are an indispensable part of peoples’ living be it for commercial or personal uses. The Vehicle that is powered by an Electric Motor that can be recharged when power drained and produces zero emission while driving which makes it an environmentally friendly option. The adoption of Electric Vehicles is still hampered by a number of problems, range anxiety being one of them. Range anxiety is the fear that an Electric Vehicle (EV) driver could have if their battery runs out and it is unable to reach a charging station. The proposed system overcomes the driver’s anxiety of battery’s performance by effective machine learning prediction and displays the amount of energy consumed earlier, before the start of the journey. The driver can compare and conclude whether the place can be reached before battery runoff. The system is compared with three different algorithms - multilinear regression algorithm, polynomial regression algorithm, random forest algorithm .The reason for choosing these three algorithms is ,it can process one dependent variables with more than one independent variables respectively .The innovative method used here scrutinised the consumption of battery into three mode sport mode ,eco mode and city mode.each mode gives Thus the system provides the potential of accuracy as 81.11% and to be a useful tool for electric vehicle owners, as it provides personalised energy consumption estimates and route planning capabilities based on the specific journey details