Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview 2020-01-0132
Energy-oriented cruising control strategies for conventional vehicles have been studied for several years and tend to use model predictive control (MPC) to optimize the vehicle velocity with terrain profile preview. For electric vehicle (EV) with regenerative braking, the velocity profile should be different from conventional vehicles. As a global optimization method, dynamic programming (DP) can be implemented to calculate the optimal velocity 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 within an adaptive multidimensional search space. Second, combined with MPC framework, we introduce an IDP based MPC (IDP-MPC) to optimize velocity for an EV with terrain preview. In addition to energy efficiency, battery aging is also considered for charging and discharging rates may accelerate battery decay. Then, to test the energy optimization performance of the IDP-MPC strategy, a section of real urban expressway road terrain in Nanjing was selected by querying the Google Elevation API. Finally, energy-saving potential of the IDP-MPC is explored by comparing to conventional DP based MPC (DP-MPC) and constant speed (CS) cruising strategy.
The simulation results show the proposed IDP-MPC strategy can save considerable computing time while keeping the same energy improvements compared to conventional DP-MPC strategy. As an extension, safety of car-following will be present and simulated.
Fei Ju, Weichao Zhuang, Liangmo Wang, Qun Wang
Nanjing University of Science and Technology, Southeast University