Event-Triggered Safe-Aware Platoon Re-Sequencing for SoC Balance of Connected Electric Vehicles

The position-dependent aerodynamic benefits in connected electric vehicle (EV) platoons provide an opportunity to improve energy efficiency, but they may also result in uneven state-of-charge (SoC) distribution among vehicles during long-distance driving. This paper proposes an event-triggered safe-aware platoon re-sequencing framework to achieve adaptive energy balance optimization while ensuring safe operation in dynamic traffic environments. The platoon re-sequencing problem is formulated by considering the coupling relationships among vehicle positions, aerodynamic energy-saving effects, battery evolution, and traffic-dependent feasibility constraints. To enable safety-aware decision making, a traffic prediction and classification module based on long short-term memory (LSTM) networks and fuzzy c-means (FCM) clustering is developed to predict future traffic conditions and determine whether platoon re-sequencing can be safely executed. Furthermore, an event-triggered mechanism (ETM) is designed to adaptively regulate the execution of re-sequencing actions according to the real-time SoC imbalance and travel progress of the platoon. Two triggering strategies, including a state-aware self-resetting trigger and an adaptive cooldown-based trigger, are developed to dynamically determine appropriate re-sequencing instants under persistent energy imbalance conditions. The proposed framework separates re-sequencing decision generation from action execution, where a reinforcement learning policy provides candidate platoon orders while the traffic prediction module and ETM jointly determine their safe and adaptive execution. The proposed method enables connected EV platoons to autonomously adjust their formation sequence in response to evolving energy distribution and traffic conditions, thereby improving terminal SoC balance while maintaining safe platoon operation.