Yuan, Junkai (Shandong University of Technology), Shao, Jinju (Shandong University of Technology), Li, Xunyi (Shandong University of Technology), Ying, Kangjian (Shandong University of Technology)

Speed ​​Planning and Energy Optimal Control of Hybrid Electric Vehicles Based on Internet of Vehicles

Scheduled for presentation during the Invited session "Special session on benchmark challenging (1)" (MoBT3), Monday, August 23, 2021, 17:30−17:50, 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 20, 2024

Abstract

Under the premise of no red light running and collision accidents, a hierarchical optimization control strategy is proposed to optimize the energy distribution of a hybrid electric vehicle adapting to multi working conditions in the environment of internet of vehicles, so as to reduce the energy consumption of acceleration or deceleration, and achieve the purpose of energy saving. Traffic signal light timing vehicle tracking speed controller is used to generate the reference speed according to the traffic signal timing, front vehicle speed and position. Model predictive controller (MPC) is used to optimize the reference speed to obtain the target speed sequence and acceleration sequence for a lower layer. Then the upper level target speed is transformed into vehicle driving demand torque and power through the equilibrium equation of total road load force and tractive force in the lower level control. The certain rules are provided to realize adaptive distribution about engine torque, motor torque and mechanical braking force. Based on the simulation platform provided by E-COSM 2021, the effectiveness and real-time performance of the hierarchical optimization control strategy are verified under seven working conditions. The simulation results show that the control strategy proposed in this paper can avoid red light running and collision accidents, reduce red light parking, and save the total fuel consumption by 11.88%~19.25% compared with he traffic signal timing + PID + MPC control scheme provided by the organizer of the competition, realizing the comprehensive improvement of system economy and working conditions adaptability.