  Data-driven cooperative control of connected and automated vehicles (CAVs)
has gained extensive research interest as it can utilize collected data to
generate control actions without relying on parametric system models that are
generally challenging to obtain. Existing methods mainly focused on improving
traffic safety and stability, while less emphasis has been placed on energy
efficiency in the presence of uncertainties and diversities of human-driven
vehicles (HDVs). In this paper, we employ a data-enabled predictive control
(DeePC) scheme to address the eco-driving of mixed traffic flows with diverse
behaviors of human drivers. Specifically, by incorporating the physical
relationship of the studied system and the Hankel matrix update from the
generalized behavior representation to a particular one, we develop a new
Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle
human driver diversities. In particular, a power consumption term is added to
the DeePC cost function to reduce the holistic energy consumption of both CAVs
and HDVs. Simulation results demonstrate the effectiveness of our approach in
accurately capturing random human driver behaviors and addressing the complex
dynamics of mixed traffic flows, while ensuring driving safety and traffic
efficiency. Furthermore, the proposed optimization framework achieves
substantial reductions in energy consumption, i.e., average reductions of 4.83%
and 9.16% when compared to the benchmark algorithms.
