基于路面附着系数估计的分布式驱动电动汽车横摆稳定性控制

doi:10.3969/j.issn.1674-8484.2026.04.002

摘要/Abstract

摘要：

针对分布式驱动电动汽车在低附着、对开及附着突变路面下易发生横摆失稳的问题，该文提出一种基于路面附着系数估计的横摆稳定性控制策略。该策略针对不同工况下附着信息可观性差异，构建双模型并行无迹Kalman滤波（UKF）路面附着系数在线估计方法，并将估计结果用于参考横摆角速度、质心侧偏角约束修正及滑模切换增益自适应设计；同时，在下层驱动力矩分配中建立基于附着约束的四轮驱动力矩优化分配策略。结果表明：相比常规滑模控制策略，所提方法使横摆角速度峰值平均下降15.19%，质心侧偏角峰值平均下降22.90%。说明该策略能够实时适应路面附着变化，有效提升复杂路面下车辆操纵稳定性。

关键词: 分布式驱动, 路面附着系数, 无迹Kalman滤波（UKF）, 直接横摆力矩控制, 滑模控制, 转矩分配

Abstract:

A yaw stability control strategy based on road adhesion coefficient estimation was proposed to address the yaw instability of distributed-drive electric vehicles under low-adhesion, split-μ, and abrupt road adhesion variation conditions. Considering the differences in the observability of adhesion information under different driving conditions, an online road adhesion coefficient estimation method was developed based on dual-model parallel unscented Kalman filtering (UKF). The estimated adhesion coefficient was then introduced to modify the constraints of the reference yaw rate and vehicle sideslip angle, and to adaptively tune the switching gain of the sliding mode controller. Meanwhile, in the lower-layer torque allocation, an optimal four-wheel drive torque allocation strategy based on adhesion constraints was established. The results show that, compared with the conventional sliding mode control strategy, the proposed method reduces the peak yaw rate by 15.19% and the peak vehicle sideslip angle by 22.90% on average, indicating that the proposed strategy can adapt to road adhesion changes in real time and effectively improve vehicle handling stability under complex road conditions.

Key words: distributed drive, road adhesion coefficient, unscented Kalman filter (UKF), direct yaw moment control, sliding mode control, torque distribution

中图分类号:

U461.6

陈硕, 张风奇, 付叶雨, 解少博. 基于路面附着系数估计的分布式驱动电动汽车横摆稳定性控制[J]. 汽车安全与节能学报, 2026, 17(4): 438-449.

CHEN Shuo, ZHANG Fengqi, FU Yeyu, XIE Shaobo. Yaw stability control of distributed-drive electric vehicles based on road adhesion coefficient estimation[J]. Journal of Automotive Safety and Energy, 2026, 17(4): 438-449.

图/表 9

参考文献 22

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整车质量，m	1 447.2 kg
横摆转动惯量，I_z	1 536.7 kg·m²
质心到前轴距离，a	1.015 m
质心到后轴距离，b	1.895 m
前轮侧偏刚度，C_f	-85 kN / rad
后轮侧偏刚度，C_r	-59 kN / rad
轮距，W_d	1.675 m
车轮半径，R	0.325 m

整车质量，m	1 447.2 kg
横摆转动惯量，I_z	1 536.7 kg·m²
质心到前轴距离，a	1.015 m
质心到后轴距离，b	1.895 m
前轮侧偏刚度，C_f	-85 kN / rad
后轮侧偏刚度，C_r	-59 kN / rad
轮距，W_d	1.675 m
车轮半径，R	0.325 m

	工况	β峰值降低率/ %	γ峰值降低率/ %
SMC相比 LQR	低速、低附着	15.26	11.03
	低速、对开	15.11	6.94
	低速、对接	15.64	7.53
	高速、低附着	34.51	28.41
	高速、对开	20.06	16.11
	高速、对接	27.24	17.20
基于μ的 SMC相比 SMC	低速、低附着	17.19	10.97
	低速、对开	8.34	4.52
	低速、对接	22.54	10.26
	高速、低附着	36.97	25.37
	高速、对开	25.04	16.90
	高速、对接	27.34	23.09

	工况	β峰值降低率/ %	γ峰值降低率/ %
SMC相比 LQR	低速、低附着	15.26	11.03
	低速、对开	15.11	6.94
	低速、对接	15.64	7.53
	高速、低附着	34.51	28.41
	高速、对开	20.06	16.11
	高速、对接	27.24	17.20
基于μ的 SMC相比 SMC	低速、低附着	17.19	10.97
	低速、对开	8.34	4.52
	低速、对接	22.54	10.26
	高速、低附着	36.97	25.37
	高速、对开	25.04	16.90
	高速、对接	27.34	23.09

控制算法	工况	RMSE (β) rad	RMSE (γ) (rad·s^-1)	a_y峰值 g
LQR	低速、低附着	0.006 84	0.013 83	0.285 2
	低速、对开	0.005 49	0.013 34	0.349 6
	低速、对接	0.005 91	0.012 99	0.322 6
	高速、低附着	0.005 62	0.014 43	0.348 9
	高速、对开	0.019 35	0.032 96	0.480 5
	高速、对接	0.009 35	0.021 81	0.412 7
SMC	低速、低附着	0.005 86	0.007 04	0.284 9
	低速、对开	0.004 90	0.008 58	0.332 4
	低速、对接	0.005 35	0.008 69	0.296 7
	高速、低附着	0.004 91	0.008 96	0.345 1
	高速、对开	0.016 24	0.027 09	0.435 1
	高速、对接	0.008 33	0.019 36	0.409 1
基于μ的SMC	低速、低附着	0.004 21	0.004 84	0.277 7
	低速、对开	0.004 15	0.007 12	0.324 0
	低速、对接	0.004 03	0.005 29	0.283 9
	高速、低附着	0.004 54	0.006 47	0.327 2
	高速、对开	0.004 90	0.005 44	0.361 1
	高速、对接	0.004 82	0.005 83	0.349 3