考虑轮胎侧偏刚度在线更新的智能电动汽车状态估计

付越胜; 李韶华; 王桂洋

doi:10.13433/j.cnki.1003-8728.20220190

考虑轮胎侧偏刚度在线更新的智能电动汽车状态估计

doi: 10.13433/j.cnki.1003-8728.20220190

付越胜^{1, 2,},
李韶华^{1, 2, ,},
王桂洋^{1, 2}

1.
石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室, 石家庄 050043
2.
石家庄铁道大学机械工程学院, 石家庄 050043

基金项目:

国家自然科学基金项目 11972238

详细信息

作者简介:
付越胜, 硕士研究生, 975204852@qq.com

通讯作者:
李韶华, 教授, 博士生导师, lishaohua@stdu.edu.cn

中图分类号: U461.6
计量
- 文章访问数: 181
- HTML全文浏览量: 108
- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-22
- 刊出日期: 2024-01-25

State Estimation of Intelligent Electric Vehicle Considering Online Updating of Tire Cornering Stiffness

FU Yuesheng^{1, 2
,},
LI Shaohua^{1, 2
, ,},
WANG Guiyang^{1, 2}

1.
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2.
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

摘要

摘要: 实时准确地估计车辆行驶状态是汽车智能化发展的前提，而现有的研究通常忽略了轮胎侧偏刚度的时变特性，在整车模型中引入线性轮胎模型严重影响到极限工况下车辆状态的估计精度。基于此，提出了一种轮胎侧偏刚度在线更新的智能电动汽车纵向车速、横摆角速度和质心侧偏角的估计算法。基于模糊自适应扩展卡尔曼滤波算法(FAEKF) 建立了车辆状态估计模型，采用模糊控制器对扩展卡尔曼滤波中含观测噪声协方差的卡尔曼增益矩阵进行实时调节，达到算法的自适应效果。以带遗忘因子递推最小二乘法(FFRLS) 为基础建立了轮胎侧偏刚度估计模型。将两种算法以嵌入式的方式结合提出FAEKF+FFRLS算法，更好地实现了状态与参数联合估计和互相校正，通过Trucksim和MATLAB/Simulink联合仿真对算法进行了验证。结果表明: 相比于标准的EKF算法，所提出的状态估计算法具有更高的精度，更好的稳定性和鲁棒性。
- 智能汽车 /
- 状态估计 /
- 递推最小二乘法 /
- 扩展卡尔曼滤波 /
- 模糊控制
Abstract: The real-time and accurate estimation of vehicle states is the premise of vehicle intelligence development. However, the existing researches usually ignore the time-varying characteristics of tire cornering stiffness, and introducing linear tire model into vehicle model seriously affects the estimation accuracy of vehicle states under extreme conditions. An algorithm for estimating intelligent electric vehicle longitudinal speed, yaw rate and sideslip angle of vehicle mass center with tire cornering stiffness updated online is proposed. Based on the fuzzy adaptive extended Kalman filter (FAEKF), the vehicle state estimation model is established. The fuzzy controller is used to adjust the Kalman gain matrix including the covariance of observation noise in EKF algorithm in real time to achieve the adaptive effect of the algorithm. Using the forgetting-factor recursive least square method (FFRLS), the estimation model of tire cornering stiffness is established. A new FAEKF+FFRLS algorithm is proposed by combining the two algorithms in an embedded way, which can better realize the joint estimation and mutual correction of states and parameters. The algorithm is verified by co-simulation Trucksim and MATLAB/Simulink. The results show that compared with the standard EKF algorithm, the proposed state estimation algorithm has higher accuracy, better stability and robustness.
- intelligent electric vehicle /
- state estimation /
- recursive least square method /
- extended Kalman filter /
- fuzzy control

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图 1 三自由度整车动力学模型

Figure 1. 3-DOF vehicle dynamics model

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图 2 联合估计算法流程

Figure 2. Procedures of joint estimation algorithm

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图 3 电动汽车驱动原理图

Figure 3. The driving principle diagram of electric vehicles

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图 4 减速驱动轮毂电机结构图

Figure 4. Structurs of deceleration drive hub motor

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图 5 含时变噪声的侧向加速度(工况2)

Figure 5. Lateral acceleration with time-varying noise (operating condition 2)

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图 6 轮胎侧偏刚度估计值与参考值

Figure 6. Estimated and reference values of tire cornering stiffness

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图 7 纵向车速估计结果对比

Figure 7. Comparison of estimated results of longitudinal vehicle speed

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图 8 横摆角速度估计结果对比

Figure 8. Comparison of estimated results of yaw rate

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图 9 质心侧偏角估计结果对比

Figure 9. Comparison of estimated results of sideslip angle

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表 1 整车参数

Table 1. Vehicle parameters

车辆模型参数	数值
整车质量m/kg	1 787
质心高度h/mm	587
质心到前轴的距离a/mm	1 514
质心到后轴的距离b/mm	1 836
横摆转动惯量I_z/(kg·m²)	4 415
车轮转动惯量I_w/(kg·m²)	3
车轮滚动半径R_w/mm	357
电机传动系效率η	0.9
减速器传动比ξ	6

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表 2 轮胎侧偏刚度参考值

Table 2. Reference values of tire cornering stiffness

工况	前轮侧偏刚度/(N·rad^-1)	后轮侧偏刚度/(N·rad^-1)
1	-84 810	-75 400
2	-85 910	-79 110
3	-53 960	-52 110

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表 3 估计值的相对误差

工况	算法	相对误差/%
工况	算法	横摆角速度	质心侧偏角
1	FAEKF+FFRLS	0.63	1.47
	EKF	7.98	4.93
2	FAEKF+FFRLS	2.50	4.91
	EKF	15.84	15.21
3	FAEKF+FFRLS	3.88	5.36
	EKF	10.09	41.00

下载: 导出CSV

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