Dynamic Platoon Re-Sequencing for Electric Vehicles Based on Bootstrapped DQN

The energy consumption imbalance among electric vehicles (EVs) within a fixed platoon primarily originates from the distinct aerodynamic drag forces at different positions. This imbalance further causes practical challenges, such as inconsistent battery degradation rates and divergent charging durations. To tackle these challenges, dynamically adjusting the platoon formations during the journey is essential, which requires identifying the optimal vehicle sequences at designated re-sequencing points. In this research, we formulate the Optimal Re-Sequencing (ORS) problem as a multi-objective optimization problem that minimizes the imbalance of energy consumption, ensures a minimum remaining state of charge (SOC) for energy security, and penalizes excessive formation changes to maintain stability. To solve this optimization problem, we propose a deep reinforcement learning (DRL) framework based on Bootstrapped Deep Q-Networks. This framework integrates multi-head Q-value estimation and prioritized experience replay mechanisms to improve exploration efficiency and learning stability. Through simulation experiments based on a modeled Suzhou–Nanjing route, our proposed approach achieves a final SOC standard deviation of 0.00524, representing a 47% reduction compared with existing works, demonstrating superior efficiency and effectiveness in achieving fair energy consumption across EV platoons.