基于最小能耗的电动汽车横摆稳定性灰色预测可拓控制研究

doi:10.3901/JME.2019.02.156

机械工程学报 ›› 2019, Vol. 55 ›› Issue (2): 156-167.doi: 10.3901/JME.2019.02.156

基于最小能耗的电动汽车横摆稳定性灰色预测可拓控制研究

陈无畏¹, 王晓¹, 谈东奎¹, 林澍¹, 孙晓文¹, 谢有浩^1,2

1. 合肥工业大学汽车与交通工程学院合肥 230009;
2. 安徽猎豹汽车有限公司滁州 239064

收稿日期:2017-06-12 修回日期:2018-05-16 出版日期:2019-01-20 发布日期:2019-01-20
通讯作者: 王晓(通信作者),男,1992年出生。主要研究方向为车辆动力学与控制。E-mail:2587192229@qq.com
作者简介:陈无畏,男,1951年出生,博士,教授,博士研究生导师。主要研究方向为车辆动力学与控制。E-mail:hfgdcjs@126.com
基金资助:
国家自然科学基金（51675151，U1564201，51375131）、安徽省科技重大专项（17030901060）和江苏省道路载运工具新技术应用重点实验室开放课题（BM20082061703）资助项目

Study on the Grey Predictive Extension Control of Yaw Stability of Electric Vehicle Based on the Minimum Energy Consumption

CHEN Wuwei¹, WANG Xiao¹, TAN Dongkui¹, LIN Shu¹, SUN Xiaowen¹, XIE Youhao^1,2

1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009;
2. Anhui Leopaard Co., Ltd., Chuzhou 239064

Received:2017-06-12 Revised:2018-05-16 Online:2019-01-20 Published:2019-01-20

摘要/Abstract

摘要： 根据四轮驱动轮毂电机电动汽车车轮驱动转矩独立可控的特点，通过控制轮毂电机的输出转矩从而控制四个车轮的驱动力/制动力，产生附加横摆力矩，实现电动汽车的横摆稳定性控制。整车控制策略采用分层控制，上层为附加横摆力矩控制器，分别设计基于横摆角速度的模糊控制器、基于质心侧偏角的模糊控制器和可拓联合控制器，下层为驱动力分配控制器，分为稳定性控制模式、最小能耗控制模式和联合控制模式，采用伪逆优化算法对各车轮的驱动力矩进行优化分配。采用灰色控制模型对实际的横摆角速度和质心侧偏角数据进行预处理。根据电动汽车行驶状态，将控制域划分为经典域、可拓域和非域，在不同的域内采用不同的控制模式，在保证整车横摆稳定性的同时降低整车驱动能耗，提高续航里程。在Matlab/Simulink软件中建立整车动力学模型，并在双移线工况下进行横摆稳定性控制与最小能耗控制的仿真分析。仿真结果表明，整车控制策略能有效保障汽车行驶时的横摆稳定性，同时可以降低整车的驱动能耗。最后，利用轮毂电机试验台并采用Carsim和LabVIEW进行硬件在环试验，验证整车控制策略。

关键词: 灰色预测, 可拓控制, 轮毂电机, 伪逆算法, 直接横摆力矩控制, 最小能耗

Abstract: According to the characteristics that the driving torque of each wheel of a four-wheel hub motor drive electric vehicle can be independently controlled, the stability control of the electric vehicle could be realized by controlling the output torque of wheel hub motor (i.e., adjusting the wheel driving force or brake force) to generate additional yaw moment. The hierarchical control strategy is applied for the vehicle stability control. The upper layer is a yaw moment controller, which includes two fuzzy controllers based on yaw rate and sideslip angle, respectively, and an extension combination controller. The lower layer is a driving force distribution controller, which utilizes the pseudo inverse algorithm to optimize the driving torque allocation of each wheel. Its control modes are divided into stability control, minimum energy consumption control and combination control. The gray control model is used to preprocess the actual yaw rate and sideslip angle. According to the electric vehicle driving state, the control domain is divided into three domains, i.e., classic domain, extension domain and non-domain. And in different domains, different control modes are employed to ensure the vehicle's stability and reduce the energy consumption. The vehicle dynamics model is established in Matlab/Simulink. The simulations of stability control and minimum energy consumption control have been carried out in double lane change condition. The simulation results show that the proposed control strategy can effectively guarantee the vehicle's stability and minimize the energy consumption. Finally, the control strategy was verified on a wheel hub motor test bench based on Carsim and LabVIEW.

Key words: direct yaw moment control, extension control, gray prediction, pseudo inverse algorithm minimum energy consumption, wheel hub motor

中图分类号:

U461

陈无畏, 王晓, 谈东奎, 林澍, 孙晓文, 谢有浩. 基于最小能耗的电动汽车横摆稳定性灰色预测可拓控制研究[J]. 机械工程学报, 2019, 55(2): 156-167.

CHEN Wuwei, WANG Xiao, TAN Dongkui, LIN Shu, SUN Xiaowen, XIE Youhao. Study on the Grey Predictive Extension Control of Yaw Stability of Electric Vehicle Based on the Minimum Energy Consumption[J]. Journal of Mechanical Engineering, 2019, 55(2): 156-167.

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基于最小能耗的电动汽车横摆稳定性灰色预测可拓控制研究

Study on the Grey Predictive Extension Control of Yaw Stability of Electric Vehicle Based on the Minimum Energy Consumption

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