Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize vehicle velocity powertrain Connected Automated Vehicles (CAVs), commonly referred to as Eco-Driving problem. This paper presents a novel computationally efficient algorithm planning energy management CAV with hybrid electric powertrain. The problem is formulated dynamic, constrained optimization spatial domain, where information about upcoming speed limits road topography assumed known. solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into Dynamic Programming (DP) obtain sub-optimal solution provides results close global optimum at fraction computational cost. Further, multi-layer hierarchical architecture proposed path causal, real-time implementation. DP-ECMS converted Model Predictive Control (MPC) framework using principles Approximate (ADP). causal implementation finally benchmarked optimal obtained DP for different scenarios.