EV Energy Optimization through Q-Learning and Heat Management: A Data-Driven Approach

Electric vehicles (EVs) face significant challenges in maximizing energy efficiency and managing thermal loads, both of which are critical to enhancing range, battery health, and overall performance. This research presents a novel, data-driven approach that integrates Q-learning with thermal-aware optimization to improve EV energy consumption patterns. By leveraging real-world telemetry data and engineering a new derived feature—heat generated from energy usage—we trained a reinforcement learning model to dynamically recommend optimal driving and operational strategies. Through simulation, our framework achieved up to a 10 percent improvement in the coefficient of performance (COP) and up to a 15 percent reduction in heat generation across varying driving conditions. These enhancements contribute to better energy conservation and mitigate thermal stress on critical EV components like motors and batteries. Additionally, the model delivers actionable recommendations on optimal vehicle speed and battery management, offering drivers practical guidance to maximize efficiency during trips. The results underscore the potential of combining reinforcement learning with heat-aware metrics for intelligent EV management, paving the way for smarter, more resilient electric mobility systems.