Optimizing Battery Charging Strategies through Q-Learning and Electric Vehicle Powertrain System Modeling

<div class="section abstract"><div class="htmlview paragraph">Battery health status and driving rangeof electric vehicles (EVs) are critical factors in determining their market penetration. Choosing an optimal charging strategy—specifying how, when, and for how long to charge based on the driver’s travel behavior—can significantly mitigate battery degradation and extend battery life. This study introduces an EV powertrain system energy model designed to enhance the prediction accuracy of battery status under real-world driving conditions. By integrating with the Q-learning approach, this studyprovides tailored recommendations on charging behaviors, including charger type, start time, and charging duration. This study innovatively considers the rental costs caused by the battery capacity not being able to meet the daily driving range. Simulating a typical three-year usage scenario for an average driver in New England, the results indicate that thecharging strategy proposed by this study reduces battery degradation rates by 1.53‰, 3.57‰, and 7.68‰ compared to strategies using only Level 2 charging, direct-current fast charging, or extreme fast charging, respectively. This combination of data-driven and physical-modeling approach demonstrates that integrating intelligent charging strategies can improve battery health, reduce operational costs, and meet driver travel demands, thereby enhancing the overall economic feasibility of EVs over their lifecycle.</div></div>