融合预瞄特性的智能电动汽车稳定性模型预测控制研究

doi:10.19562/j.chinasae.qcgc.2023.05.002

Abstract

Abstract:

A feedforward and feedback control method with preview characteristics for intelligent electric vehicle stability is proposed. The vehicle preview model is established， and the road curvature is introduced in as a factor influencing vehicle dynamic characteristics according to vehicle preview perception of the environment. Based on the change of vehicle posture guided by the preview information， the desired longitudinal vehicle speed is described according to the road friction condition and the vehicle speed index model and the stability feedforward control method for tire lateral stiffness compensation is established. At the same time， the model prediction control （MPC） is used to design the stability feedback control law， with the preview model and preview time adjusted according to the road information adaptively to eliminate the influence of uncertain factors such as feedforward control error and road disturbance. The research results suggest that the proposed control method has lower vehicle centroid sideslip angle， yaw rate and lateral acceleration， and higher tracking accuracy. In simulation test， compared with no control method， MPC feedback control， and feedforward + MPC feedback control with fixed parameter， the control strategy proposed in this paper reduces the peak mass center sideslip angle by 41.3%， 28.9% and 10.0% respectively under dual shift conditions， and the peak yaw rate by 18.0%， 6.7%， and 2.0% respectively. Under the other dual shift conditions， the peak values of the center of mass sideslip angle decreases by 27.2%， 8.7% and 8.0% respectively， and the peak value of the yaw rate decreases by 16.9%， 12.9% and 8.6% respectively. Compared with MPC feedback control， feedforward + MPC feedback control with fixed parameter， the maximum vehicle sideslip angle decreases by 49.8% and 34.8% respectively， and the maximum yaw rate decreases by 21.8% and 12.7% respectively under the S-shaped condition. Under the other S-shaped condition， the maximum vehicle sideslip angle decreases by 36.6% and 18.6%， and the maximum yaw rate decreases by 17.7% and 12.4% respectively.

Key words: intelligent electric vehicle, stability control, preview characteristics, feedforward + feedback control, model predictive control

Yilin He,Jian Ma,Shukai Yang,Wei Zheng,Qifan Xue. Research on Stability Model Predictive Control of Intelligent Electric Vehicle with Preview Characteristics[J].Automotive Engineering, 2023, 45(5): 719-734.

Figures/Tables 15

References 30

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参数	数值
整备质量/kg	1 250
轴距/m	2.910
质心到前轴距离/m	1.015
质心到后轴距离/m	1.895
质心高度/m	0.540
绕z轴转动惯量/（kg·m²）	1 536.7
前轮侧偏刚度/（N·rad^-1）	-76 000
后轮侧偏刚度/（N·rad^-1）	-110 100

状态参数	项目	双移线工况：u=90 km/h，μ=0.6				双移线工况：u=120 km/h，μ=0.85
状态参数	项目	无控制	反馈控制	前馈+定参数MPC	前馈+自适应参数MPC	无控制	反馈控制	前馈+定参数MPC	前馈+自适应参数MPC
质心侧偏角	峰值	0.92°	0.76°	0.60°	0.54°	1.73°	1.38°	1.37°	1.26°
质心侧偏角	降低百分比	41.3%	28.9%	10.0%		27.2%	8.7%	8.0%
横摆角速度	峰值	-11.75°/s	-10.33°/s	-9.84（°）/s	-9.64（°）/s	-12.94（°）/s	-12.34（°）/s	-11.76（°）/s	-10.75（°）/s
横摆角速度	降低百分比	18.0%	6.7%	2.0%		16.9%	12.9%	8.6%
侧向加速度	峰值	-0.45g	-0.43g	-0.39g	-0.35g	-0.69g	-0.68g	-0.66g	-0.58g
侧向加速度	降低百分比	22.2%	18.6%	10.3%		15.9%	14.7%	12.1%

状态参数	项目	蛇行工况：90 km/h，μ=0.6			蛇行工况：120 km/h，μ=0.85
状态参数	项目	反馈控制	前馈+定参数MPC	前馈+自适应参数MPC	反馈控制	前馈+定参数MPC	前馈+自适应参数MPC
质心侧偏角	峰值	-4.57°	-3.51°	-2.29°	-4.13°	-3.22°	-2.62°
质心侧偏角	降低百分比	49.8%	34.8%		36.6%	18.6%
横摆角速度	峰值	23.66（°）/s	21.19（°）/s	18.50（°）/s	-20.95（°）/s	-19.69（°）/s	-17.24（°）/s
横摆角速度	降低百分比	21.8%	12.7%		17.7%	12.4%
侧向加速度	峰值	0.58g	0.55g	0.53g	-0.70g	-0.69g	-0.65g
侧向加速度	降低百分比	8.6%	3.6%		7.1%	5.8%

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Research on Stability Model Predictive Control of Intelligent Electric Vehicle with Preview Characteristics

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