Abstract
This paper focuses on an eco-driving based hierarchical robust energy management strategy for connected automated HEVs in the presence of uncertainty. The proposed control strategy includes a velocity optimizer, which evaluates the optimal vehicle velocity, and a powertrain energy manager, which evaluates the optimal power split between the engine and the battery in a hierarchical framework. The velocity optimizer accounts for regenerative braking and minimizes the total driving power and friction braking over a short control horizon. The hierarchical powertrain energy manager employs a long- and short-term strategy where it first approximately solves its problem over a long time horizon (the whole trip time in this paper) using the traffic data obtained from vehicle-to-infrastructure (V2I) connectivity. This is followed by a short-term decision maker that utilizes the velocity optimizer and long-term solution, and solves the energy management problem over a relatively short time horizon using robust prediction control methods to factor in any uncertainty in the velocity profile due to uncertain traffic. We solve the long-term energy management problem using pseudospectral optimal control method, and the short-term problem using robust tube-based model predictive control(MPC) method. Simulation results show the competence of our proposed approach, where our proposed co-optimization approach with long- and short-term solution results in ≈ 12% more energy efficiency than a baseline co-optimization approach.
Sustainable Smart Cities and Energy Management: The Labor Market Perspective
