A Machine Learning Framework for Balancing Charging Efficiency and Driver Satisfaction in Electric Vehicles

Abstract

This paper optimises electric vehicle (EV) charging schedules, reduces wait periods, and increases energy efficiency in order to combine driver enjoyment and charging efficiency. The proposed architecture analyses real-time data, including battery state, charging station availability, traffic, electricity consumption, and user preferences, in order to offer intelligent charging recommendations. The objective of advanced machine learning technologies, such as predictive analytics, reinforcement learning, and optimisation models, is to optimise the distribution of charging infrastructure demand, increase battery life, and reduce costs. In order to enhance overall customer satisfaction, the algorithm also considers driver comfort criteria, such as preferable charging times, route distance, and charging speed. The potential to enhance power utilisation, provide more environmentally friendly transportation, and establish a smart electric vehicle (EV) ecosystem is present in the combination of smart energy management and customised charging strategies that are presented in this paper.