双电机后驱车辆操纵稳定性分层混杂模型预测控制

doi:10.3901/JME.2019.22.123

机械工程学报 ›› 2019, Vol. 55 ›› Issue (22): 123-130.doi: 10.3901/JME.2019.22.123

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双电机后驱车辆操纵稳定性分层混杂模型预测控制

林程^1,2, 曹放¹, 梁晟¹, 高翔¹, 董爱道³

1. 北京理工大学电动车辆国家工程实验室北京 100081;
2. 北京电动车辆协同创新中心北京 100081;
3. 北京理工华创电动车技术有限公司北京 100081

收稿日期:2019-03-20 修回日期:2019-10-05 出版日期:2019-11-20 发布日期:2020-02-29
通讯作者: 梁晟(通信作者),男,1994年出生,博士研究生。主要研究方向为分布式驱动电动汽车车辆动力学控制。E-mail:liangsheng@bit.edu.cn
作者简介:林程,男,1968年出生,博士,教授,博士研究生导师。主要研究方向为电动汽车车辆动力学控制。E-mail:lincheng@bit.edu.cn
基金资助:
国家自然科学基金（51575044）和北京市科技计划（D171100007517001）资助项目。

Yaw Stability Control of Distributed Drive Electric Vehicle Based on Hierarchical Hybrid Model Predictive Control

LIN Cheng^1,2, CAO Fang¹, LIANG Sheng¹, GAO Xiang¹, DONG Aidao³

1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081;
2. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081;
3. BIT HuaChuang Electric Vehicle Technology Co., Ltd., Beijing 100081

Received:2019-03-20 Revised:2019-10-05 Online:2019-11-20 Published:2020-02-29

摘要/Abstract

摘要： 为改善车辆在复杂工况下的操纵稳定性，解决低附着路面易失稳的问题，针对后驱双电机轮边驱动电动汽车提出一种结合直接横摆控制与主动转向控制的操纵稳定性控制策略。控制策略采用分层控制结构：上层控制器采用多输入多输出系统的模型预测控制，对目标附加横摆力矩与前轮主动转向角进行求解；下层转矩分配控制器采用混杂模型预测控制（hMPC），将轮胎纵向力的非线性特征简化为分段的混杂系统，在分配驱动转矩时考虑车轮在不同工况下的滑转情况。搭建了基于dSPACE实时仿真系统的仿真平台，在高附着、低附着路面下进行半实物仿真试验。仿真结果表明，与二次规划（QP）转矩分配算法相比，高附着路面工况下平均相对误差减小了17.64%，方均根误差减小了42.86%，最大偏离误差相对减少了7.64%；低附着路面工况下可以有效防止车辆失稳，改善操纵稳定性。

关键词: 分布式驱动, 操纵稳定性, 混杂系统, 模型预测控制

Abstract: In order to improve the stability of the vehicle under complicated working conditions, especially on low-adhesive road, a driving stability control strategy combining direct yaw control and active steering control is proposed for the dual-motor driven electric vehicle. A hierarchical control structure is used in the control strategy, including an upper controller for calculating the vehicle's additional yaw moment and active steering angle, and a lower controller for distributing the driving torque. The upper controller adopts the model predictive control of the multi-input and multi-output system, and solves the target additional yaw moment and active steering angle; The hybrid model predictive control is adopted in lower controller, which simplified the nonlinear characteristics of the tire into a segmented hybrid system. The slip condition of the wheel is considered while the driving torque is distributed, thereby the steering stability of the vehicle under complicated working conditions is improved. The hardware-in-the-loop simulation platform based on the dSPACE real-time simulation system is used to perform the semi-physical simulation. The simulation results show that compared with the quadratic programming (QP) torque distribution algorithm, the average relative error under high adhesion road conditions is reduced by 13.64%, the root mean square error accuracy is improved by 42.86%, and the relative deviation of the maximum deviation error is reduced by 7.49%; under low adhesion road conditions, the algorithm can effectively prevent vehicle instability and improve steering stability.

Key words: distributed driven, yaw stability, hybrid system, model predictive control

中图分类号:

U461

林程, 曹放, 梁晟, 高翔, 董爱道. 双电机后驱车辆操纵稳定性分层混杂模型预测控制[J]. 机械工程学报, 2019, 55(22): 123-130.

LIN Cheng, CAO Fang, LIANG Sheng, GAO Xiang, DONG Aidao. Yaw Stability Control of Distributed Drive Electric Vehicle Based on Hierarchical Hybrid Model Predictive Control[J]. Journal of Mechanical Engineering, 2019, 55(22): 123-130.

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双电机后驱车辆操纵稳定性分层混杂模型预测控制

Yaw Stability Control of Distributed Drive Electric Vehicle Based on Hierarchical Hybrid Model Predictive Control

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