基于粒子群优化-蚁群融合算法的分布式电驱动车辆模型预测转矩协调控制策略

doi:10.12382/bgxb.2022.0819

摘要/Abstract

摘要：

针对分布式电驱动车辆多动力源耦合作用和高度非线性造成的动力学控制难题,以 7自由度整车动力学模型为预测模型,以粒子群优化-蚁群融合算法为优化方法,提出一种基于粒子群优化-蚁群融合算法的模型预测转矩协调控制策略,并搭建了仿真实验和实车试验平台,进行了多种工况试验。试验结果表明,新提出的转矩协调控制策略能够根据试验工况调整控制模式,实现动力性、经济性和操纵稳定性的综合最优控制效果。

关键词: 分布式电驱动车辆, 转矩协调控制, 粒子群优化算法, 蚁群算法, 模型预测控制

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

中图分类号:

U469.72

张渊博, 项昌乐, 王伟达, 陈泳丹. 基于粒子群优化-蚁群融合算法的分布式电驱动车辆模型预测转矩协调控制策略[J]. 兵工学报, 2023, 44(11): 3253-3258.

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.

图/表 15

图1 基于粒子群优化-蚁群算法的分布式电驱动车辆模型预测转矩协调控制策略示意图

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

表1 分布式驱动电动车的关键参数

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

图2 模型预测转矩协调控制策略-中国汽车行驶工况

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

图3 规则式控制策略-中国汽车行驶工况

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

图4 模型预测转矩协调控制策略-正弦转向工况

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

图5 无直接横摆力矩控制-正弦转向工况

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

图6 棋盘附着循环工况示意图

Fig.6 Chessboard adhesion coefficient cycle

图7 模型预测转矩协调控制策略-棋盘附着循环工况

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

图8 PID控制策略-棋盘附着循环工况

Fig.8 PID control strategy-chessboard adhesion coefficient cycle

图9 试验样车

Fig.9 Test vehicle

图10 等效控制策略结构图

Fig.10 Equivalent control strategy

图11 循环工况实车试验图

Fig.11 Real vehicle test-Chinese automobile driving cycle

图12 加速工况实车试验图

Fig.12 Real vehicle test-acceleration condition

图13 紧急制动工况实车试验图

Fig.13 Real vehicle test-braking condition

图14 正弦转向实车试验图

Fig.14 Real vehicle test-sinusoidal steering condition

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