A Survey on AI-Based Electric Vehicle Charging Station Recommendation and Route Optimization Systems
A Survey on AI-Based Electric Vehicle Charging Station Recommendation and Route Optimization Systems
Authors:
Mr. Gowtham Raj M1, Abhimanyu N2, Chiranth G3, GC Likith3, Hemanth Raje H K5
Assistant Professor, Dept of CSE, KSIT, Karnataka, India1
Student, Dept of CSE, KSIT, Karnataka, India2-5
Abstract – The transition to EVs leads to a need for innovative ways of managing charge stations as the rapid development of car ownership creates problems associated with long waiting times and hard decision-making related to the route in the absence of guidance. In view of the existing literature regarding the topic of navigation for users of e-vehicles and prediction based on artificial intelligence, as well as modeling of charging stations' location and routes by using network graphs, this review combines various insights provided in different sources. First, the research related to navigational assistance for electric cars mostly revolves around user-friendly interfaces that make it easier for drivers to navigate through the network of available charge stations. Second, there are studies using machine learning methods and techniques for more accurate forecasting of charging demands among the consumers of electric cars. Another aspect covered is the location of charge stations. There are certain algorithms of graphs that may help design optimal routes. The need for intelligent charging stations arises from the growing number of users of electric cars. Finding the right destination on the road, particularly with the aid of real-time assistance, is still rare. Four domains have contributed recent research results from experts in each area: movement analysis, EV technology, automated forecast generation based on the demand for charging facilities, facility siting, and shortest path routing through complex connectivity networks. Collectively, they provide clues about how solutions to current challenges could be achieved. The results from the survey data indicate that if all the above aspects are combined into one system, then there will be a reduction in wait times, better routing decisions, and more balanced usage of charging stations.
KEY WORDS: Electric Vehicles, Charging Infrastructure, Demand Prediction, Route Optimization, Machine Learning, Multi-Criteria Decision Making, Smart Mobility