Zheng, Chen (Faculty of Transportation Engineering,Kunming University of scie), Wu, Simin (Kunming University of Science and Technology,Kunming,650500,Chin), Shen, Shiquan (Kunming University of Science and Technology), JIangwei, Shen (Faculty of Transportation Engineering,Kunming University of scie), Liu, Yonggang (Chongqing University), Wanchao, Li (Faculty of Transportation Engineering,Kunming University of scie)

Real-Time Velocity Planning and Energy Management for Plug-In Hybrid Electric Vehicle Based on V2V and V2I Communications

Scheduled for presentation during the Invited session "Special session on benchmark challenging (1)" (MoBT3), Monday, August 23, 2021, 17:50−18:10, Room T3

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

The performance of plug-in hybrid electric vehicle (PHEV) energy management is highly dependent on speed prediction. Real-time communication between vehicles and vehicles (V2V), vehicles and infrastructures (V2I) provides a great opportunity to find the optimal speed trajectory that can be used to develop an energy management strategy (EMS) for PHEV to minimize fuel consumption. In this paper, a real-time speed planning algorithm and energy management optimization strategy for PHEV under car following condition in connected environment is proposed. Firstly, based on the traffic scenario, a real-time speed planning algorithm for ego vehicle is proposed to obtain the optimal reference speed using the sequential quadratic programming (SQP) with the aim at minimizing fuel consumption and improving car following performance. And then, based on the predicted velocity, a real-time distribution of battery power and engine power of PHEV is carried out using model prediction control (MPC). The simulation results show that the ego vehicle with the speed planning algorithm proposed in this paper can effectively follow the preceding vehicle to reach the destination under the conditions of meeting the maximum speed limit and safe distance. And under the framework of rolling optimization, the real-time control of energy management of ego vehicle is realized. The fuel economy of the ego vehicle is improved as compared with the preceding vehicle under one randomly generated traffic scenarios.