Optimal Re-Sequencing of Electric Vehicle Platoons Based on Deep Reinforcement Learning

This study addresses the issue of uneven energy consumption in electric vehicle (EV) platoons, arising from the static sequencing of vehicles within the platoon. Such an imbalance can negatively impact the efficiency of individual vehicles and the driving performance of the entire platoon. Our approach proposes dynamically altering the formation of the platoon during transit to balance energy use. The core challenge is to identify the most efficient vehicle sequence at predetermined re-sequencing points during the journey. To address this, we introduce three innovative methods based on deep reinforcement learning, chosen for their ability to handle complex, dynamic optimization problems. Our experimental studies, conducted on actual transportation networks, demonstrate these methods significantly enhance energy management and distribution efficiency in EV platoons, highlighting their potential for practical applications in intelligent transportation systems.