A Recommendation System for Electric Vehicles Users Based on Restricted Boltzmann Machine and WaterWheel Plant Algorithms

Abstract

Ensuring reliable and easily accessible charging infrastructure becomes crucial as more people adopt electric vehicles. This study introduces a recommendation system designed to assist electric vehicle users in finding convenient charging stations, enhancing the charging experience, and reducing range anxiety. The system employs advanced data analysis techniques to offer personalized suggestions based on users’ preferences. Real-time data on factors like charging station availability, individual preferences, and past usage patterns are collected and processed using a restricted Boltzmann machine-learning algorithm. The waterwheel plant algorithm, known for its effectiveness in solving complex optimization problems, is utilized to optimize the parameters of the restricted Boltzmann machine. The recommendation system considers various user preferences, including charging speed, cost, network compatibility, amenities, and proximity to the user’s current location. The system aims to minimize user frustration, improve charging performance, and enhance customer satisfaction by addressing these aspects. Results indicate the system’s efficiency in suggesting convenient charging locations. The study explores the statistical significance of the optimized waterwheel plant algorithm and restricted Boltzmann machine model through Wilcoxon rank-sum and Analysis of Variance tests.

Keywords

• Electric vehicles
• charging stations
• recommendation systems
• optimal location
• data analytics
• machine learning