Energy-Efficient Control of Connected Autonomous Electric Vehicles in Mixed Traffic with Human-Driven Vehicles

Signalized intersections are recognized as traffic bottlenecks that increase vehicle stops, leading to frequent acceleration events on urban roads and potentially elevating overall fuel consumption when red signals are encountered. With advances in emerging technologies, connected autonomous vehicles (CAVs) can be optimally controlled to improve energy efficiency while accounting for future traffic constraints. In mixed traffic environments, a controlled CAV can directly influence the energy efficiency of following human-driven vehicles (HVs) because of car-following behavior. This paper considers the CAV and its following HV simultaneously in the design of energy-efficient vehicle control strategies. By leveraging traffic prediction results, the speed trajectory and powertrain operation of a connected autonomous electric vehicle (CAEV) is co-optimized, explicitly accounting for multiple HVs behind it. Simulation results show that the total energy savings of a vehicle platoon increased from 7.47% to 10.05% as the number of HVs considered in the optimization rises from zero to five. Furthermore, the effect of multiple CAEVs on platoon-level energy efficiency is comprehensively analyzed, with improvements from 8.1% to 15.2% when multiple HVs follow each CAEV.