Economic Cruising Velocity Optimization Using Iterative Dynamic Programming of Connected Electric Vehicle

doi:10.3901/JME.2021.06.121

Abstract

Abstract: To improve the energy efficiency of connected electric vehicle (EV), a receding distance horizon velocity planning strategy (RDHVP) is proposed, for optimizing economic cruising velocity at dynamic traffic environment with varying slopes and velocity limitations. The RDHVP strategy uses receding optimization strategy and iterative dynamic programming (IDP) algorithm, which can decouple the spatio-temporal coupling of energy optimization and dynamic traffic constraints, to achieve energy-saving cruising velocity fast optimization. Considering varying velocity limitations, the energy-optimal control problem of EV is staged by velocity limitations. The RDHVP strategy can realize staged problem receding optimization in distance domain, to avoid sharply changed vehicle force caused by varying velocity limitations. The IDP algorithm is formulated based on the principle of dynamic programming with consideration of boundary and grid scaling strategy, which can quickly solve the optimization problem and obtain the energy-optimal cruising velocity profile. Finally, to verify the effectiveness of proposed strategy, the simulation model is formulated using collected real traffic information. The results show the RDHVP strategy improves energy efficiency and reduces computing time significantly, compared with the conventional constant speed cruising strategy and regular dynamic programming optimization method.

Key words: connected vehicle, eco-driving, transportation cyber physical systems, receding optimization, iterative dynamic programming

CLC Number:

U461

DONG Haoxuan, YIN Guodong, ZHUANG Weichao, CHEN Hao, ZHOU Yichen, WANG Yan. Economic Cruising Velocity Optimization Using Iterative Dynamic Programming of Connected Electric Vehicle[J]. Journal of Mechanical Engineering, 2021, 57(6): 121-130.

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