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Data-driven cooperative control of connected and automated vehicles (CAVs)has gained extensive research interest as it can utilize collected data togenerate control actions without relying on parametric system models that aregenerally challenging to obtain. Existing methods mainly focused on improvingtraffic safety and stability, while less emphasis has been placed on energyefficiency in the presence of uncertainties and diversities of human-drivenvehicles (HDVs). In this paper, we employ a data-enabled predictive control(DeePC) scheme to address the eco-driving of mixed traffic flows with diversebehaviors of human drivers. Specifically, by incorporating the physicalrelationship of the studied system and the Hankel matrix update from thegeneralized behavior representation to a particular one, we develop a newPhysics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handlehuman driver diversities. In particular, a power consumption term is added tothe DeePC cost function to reduce the holistic energy consumption of both CAVsand HDVs. Simulation results demonstrate the effectiveness of our approach inaccurately capturing random human driver behaviors and addressing the complexdynamics of mixed traffic flows, while ensuring driving safety and trafficefficiency. Furthermore, the proposed optimization framework achievessubstantial reductions in energy consumption, i.e., average reductions of 4.83%and 9.16% when compared to the benchmark algorithms.

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors