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

被引：10

作者：

Chen W. ^{[1
]}

Wang X. ^{[1
]}

Tan D. ^{[1
]}

Lin S. ^{[1
]}

Sun X. ^{[1
]}

Xie Y. ^{[1
,2
]}

机构：

[1] School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei

[2] Anhui Leopaard Co., Ltd., Chuzhou

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 02期

关键词：

Direct yaw moment control; Extension control; Gray prediction; Pseudo inverse algorithm minimum energy consumption; Wheel hub motor;

D O I：

10.3901/JME.2019.02.156

中图分类号：

学科分类号：

摘要：

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. © 2019 Journal of Mechanical Engineering.

引用

页码：156 / 167

页数：11

共 24 条

[1]

Bai H., Drive control strategy study based on 4 in-wheel-motor drive pure electric vehicle, (2015)

[2]

Li B., Li W., Kennedy O., Et al., Dynamics analysis of an omni-directional vehicle, International Journal of Automotive Technology, 15, 3, pp. 387-398, (2014)

[3]

Zhang L.X., Wu G.Q., Combination of front steering and differential braking control for the path tracking of autonomous vehicle, SAE 2016 World Congress and Exhibition, (2016)

[4]

Ko S.Y., Ko J.W., Lee S.M., Et al., A study on in-wheel motor control to improve vehicle stability using human-in-the-loop simulation, Journal of Power Electronics, 13, 4, pp. 536-545, (2013)

[5]

Ren B., Chen H., Zhao H., Et al., MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution, Mechatronics, 38, pp. 103-114, (2015)

[6]

Gu C., Liu H., Chen X., Torque distribution based on efficiency optimization of four-wheel independent drive electric vehicle, Journal of Tongji University, 43, 10, pp. 1150-1156, (2015)

[7]

Yu Z., Zhang L., Xiong L., Optimized torque distribution control to achieve higher fuel economy of 4WD electric vehicle with four in-wheel motors, Journal of Tongji University, 33, 10, pp. 1355-1361, (2005)

[8]

Chen Y.H., Hedrick J.K., Guo K.H., A novel direct yaw moment controller for in-wheel motor electric vehicles, Vehicle System Dynamics, 51, 6, pp. 924-942, (2013)

[9]

Li L., Lu Y., Wang R., Et al., A three-dimensional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC, IEEE Transactions on Industrial Electronics, 64, 4, pp. 3389-3401, (2015)

[10]

Zhang X., Research on multi-step thrust allocation optimization algorithm of vessels, (2015)

← 1 2 3 →