Optimal Resequencing of Connected and Autonomous Electric Vehicles in Battery SOC-Aware Platooning

Abstract

The issue of imbalanced energy consumption in electric vehicle (EV) platoons stems from fixed vehicle positions within the formation, leading to practical challenges such as uneven battery degradation and varying charging times. This problem can be addressed by dynamically adjusting fleet formations during a trip, necessitating the determination of optimal vehicle sequences at specific resequencing locations. In this article, we formulate the optimal resequencing problem as a minimum-variance optimization task and propose five distinct approaches: brute-force, modified brute-force, modified fixed platoon, state of charge (SOC) ranking, and max-min swap algorithms. Real-world route experiments demonstrate that the max-min swap algorithm outperforms the modified fixed platoon algorithm in both performance and execution time. Notably, the max-min swap and SOC ranking algorithms achieve an average variance reduction of over 90% in final SOCs compared to the modified fixed platoon algorithm while maintaining comparable running times, showcasing their superior efficiency and effectiveness in balancing energy consumption across EV platoons.