Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment

doi:10.3901/JME.2020.14.119

Abstract

Abstract: How to consider both the cooperative control of the vehicle platoon and fuel economy is one of the key technologies to improve the traffic efficiency and give full play to energy saving potential in connected environment. Firstly, in order to solve the collaborative control of the vehicle platoon and reduce fuel consumption, a multi-vehicle speed planning and following model is established for hybrid electric vehicle platoon in urban roads. Based on the model, the model predictive control (MPC) is designed to plan the future speed and improve the stability of hybrid electric vehicle platoon. Secondly, a layered model predictive control is used to design a real-time energy management strategy. The upper layer obtains the optimal economic vehicle velocity with the goal of optimizing the speed and frequent acceleration. The lower layer control system implements energy management for the hybrid electric vehicle based on the optimized vehicle speed. This real-time energy management strategy optimizes vehicle fuel economy on the premise of ensuring stable follow-up. Finally, the algorithm is compared with the results of dynamic programming(DP). The results show that the method can realize the coordinated follow-up control of the entire fleet and obtain better fuel economy.

Key words: hybrid electric vehicle, network environment, model predictive control, energy management strategy

CLC Number:

U461

TANG Xiaolin, LI Shanshan, WANG Hong, DUAN Ziwen, LI Yinong, ZHENG Ling. Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment[J]. Journal of Mechanical Engineering, 2020, 56(14): 119-128.

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