A Particle Swarm Optimization and Ant Colony Optimization Fusion Algorithm-based Model Predictve Torque Coordnation Control Strategy for Distributed Electric Drive Vehicle

doi:10.12382/bgxb.2022.0819

Abstract

Abstract:

For the dynamic control challenges caused by the coupling effect of multiple power sources and high nonlinearity in distributed electric drive vehicle, a model predictive torque coordination control strategy based on particle swarm optimization and ant colony optimization is proposed, which uses a 7-degree-of-freedom vehicle dynamics model as the prediction model. The simulation and actual vehicle test platforms were built, and the multiple operating conditions were test. The test results show that the proposed torque coordination control strategy can be used to adjust the control mode according to the experimental conditions, thus achieving a comprehensive optimal control effect of power, economy, and handling stability.

Key words: distributed electric drive vehicle, torque coordination control, particle swarm optimization algorithm, ant colony, model predictive control

CLC Number:

U469.72

ZHANG Yuanbo, XIANG Changle, WANG Weida, CHEN Yongdan. A Particle Swarm Optimization and Ant Colony Optimization Fusion Algorithm-based Model Predictve Torque Coordnation Control Strategy for Distributed Electric Drive Vehicle[J]. Acta Armamentarii, 2023, 44(11): 3253-3258.

Figures/Tables 15

Fig.1 Model predictive torque coordination control strategy of distributed electric drive vehicle based on particle swarm optimization-ant colony optimization algorithm

Table 1 Key parameters of distributed drive electric vehicle

试验设备	相关参数
车辆	迎风面积3m²,质量约2300kg
电池组	磷酸铁锂电池组,电压340V,容量60A·h
驱动电机	永磁同步电机,额定功率30kW,峰值功率70kW,额定转速3000r/min,峰值转速6000r/min,额定转矩100N·m,峰值转矩210N·m
减速器	传动比3.4
轮胎	半径0.35m
综合控制器	RaPIDECU S1,芯片型号MPC5674F,32位,主频160MHz,CAN通道数4

Fig.2 Model predictive torque coordination control strategy-Chinese automobile driving cycle

Fig.3 Rule-based control strategy-Chinese automobile driving cycle

Fig.4 Model predictive torque coordination control strategy-sinusoidal steering condition

Fig.5 No direct yaw torque control strategy-sinusoidal steering condition

Fig.6 Chessboard adhesion coefficient cycle

Fig.7 Model predictive torque coordinaion control strategy-chessboard adhesion coefficient cycle

Fig.8 PID control strategy-chessboard adhesion coefficient cycle

Fig.9 Test vehicle

Fig.10 Equivalent control strategy

Fig.11 Real vehicle test-Chinese automobile driving cycle

Fig.12 Real vehicle test-acceleration condition

Fig.13 Real vehicle test-braking condition

Fig.14 Real vehicle test-sinusoidal steering condition

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