  This paper focuses on the velocity planning and energy management problems
for Connected and Automated Vehicles (CAVs) with hybrid electric powertrains.
The eco-driving problem is formulated in the spatial domain as a nonlinear
dynamic optimization problem, in which information about the upcoming speed
limits and road topography is assumed to be known a priori. To solve this
problem, a novel Dynamic Programming (DP) based optimization method is
proposed, in which a causal Equivalent Consumption Minimization Strategy (ECMS)
is embedded. The underlying vehicle model to predict energy consumption over
real-world routes is validated using experimental data.
  Further, a multi-layer hierarchical control architecture is proposed as a
pathway to real-time implementation in a vehicle. The DP-ECMS algorithm is
introduced for a long-horizon optimization problem, and then adapted for a
receding horizon implementation using principles in Approximate Dynamic
Programming (ADP). This computationally economical alternative to the
traditional DP solution is then benchmarked and evaluated.
