Publication

Abstract : As the call for electric cars (EVs) continues to surge, making sure efficient access to charging infrastructure will become paramount for encouraging their large adoption. The research addresses the urgent undertaking of optimising EV charging infrastructure by means of predicting EV charging requirements and recommending the nearest charging stations based totally on the car's charging functionality. The work goal is to expand a predictive approach that anticipates EV charging requirements and guides users to the most suitable charging stations. The approach utilizes number of object mastering algorithms, which include Decision Tree Regression, K-Nearest Neighbours Regression, and Support Vector Regression, trained on real-local charging station records from Bangalore. These techniques are designed to estimate charging points at various geographic locations, empowering EV customers to make informed choices based on their instant charging requirements. The research demonstrates the software of each set of rules in exactly predicting charging points and identifying the most suitable charging stations. Beyond Bangalore, the proposed technique can be adapted to benefit other cities and regions, contributing to sustainable transportation, and expediting the transition to electric mobility.