Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview

As a global optimization method, dynamic programming (DP) can be employed to seek the optimal velocity with minimum energy consumption for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively and save considerable computing time. Second, the developed IDP and model predictive control (MPC) are combined to establish a real-time cruising controller called IDP-MPC for an EV with terrain preview. In the predictive controller, we use the IDP to solve a constrained finite horizon nonlinear optimization problem. Finally, to assess the performance of the proposed cruising controller, simulation on a realistic urban expressway road terrain is implemented. Energy-saving potential of the IDP-MPC controller is explored by comparing to DP and constant speed (CS) cruising controllers. The comparative study indicates that the IDP-MPC controller can obtain near-optimal energy-saving capacity compared to DP controller.