WO2023036029A1 - 车辆稳定性控制方法、系统、车辆和存储介质 - Google Patents

车辆稳定性控制方法、系统、车辆和存储介质 Download PDF

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WO2023036029A1
WO2023036029A1 PCT/CN2022/116077 CN2022116077W WO2023036029A1 WO 2023036029 A1 WO2023036029 A1 WO 2023036029A1 CN 2022116077 W CN2022116077 W CN 2022116077W WO 2023036029 A1 WO2023036029 A1 WO 2023036029A1
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Prior art keywords
vehicle
yaw rate
stability control
mass
center
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PCT/CN2022/116077
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English (en)
French (fr)
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张建
王相玲
姜洪伟
李林润
刘秋铮
王御
王宇
袁文建
黄贺
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中国第一汽车股份有限公司
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Publication of WO2023036029A1 publication Critical patent/WO2023036029A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
    • B60T8/17—Using electrical or electronic regulation means to control braking
    • B60T8/1755—Brake regulation specially adapted to control the stability of the vehicle, e.g. taking into account yaw rate or transverse acceleration in a curve
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
    • B60T8/17—Using electrical or electronic regulation means to control braking
    • B60T8/1755—Brake regulation specially adapted to control the stability of the vehicle, e.g. taking into account yaw rate or transverse acceleration in a curve
    • B60T8/17551—Brake regulation specially adapted to control the stability of the vehicle, e.g. taking into account yaw rate or transverse acceleration in a curve determining control parameters related to vehicle stability used in the regulation, e.g. by calculations involving measured or detected parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Definitions

  • the present application relates to the technical field of automobiles, for example, to a vehicle stability control method, system, vehicle and storage medium.
  • the present application provides a vehicle stability control method, system, vehicle and storage medium.
  • the present application provides a vehicle stability control method, including:
  • the additional yaw moment M z is distributed to obtain the wheel cylinder brake pressure required for vehicle stability control of at least one wheel.
  • the present application provides a vehicle stability control system, including:
  • the state estimation module is configured to estimate the center of mass sideslip angle and yaw rate of the vehicle according to the signals measured by the vehicle sensors to obtain the estimated value of the center of mass sideslip angle of the vehicle and yaw rate estimates
  • the expected value identification module is configured to identify the expected center of mass sideslip angle ⁇ d and yaw rate expected value ⁇ d expected by the driver at the current moment according to the longitudinal vehicle speed v x and the front wheel angle ⁇ ;
  • the sliding mode control module is connected to the state estimation module and the expected value identification module respectively, and is set to The difference from the expected value of the sideslip angle ⁇ d of the center of mass, and the estimated value of the yaw rate The difference with the expected value of yaw rate ⁇ d obtains the additional yaw moment M z required for vehicle stability control;
  • the control distribution module is communicated with the sliding mode control module, and is configured to distribute the additional yaw moment M z to obtain the wheel cylinder brake pressure required for vehicle stability control of at least one wheel.
  • the application provides a vehicle, the vehicle comprising:
  • processors one or more processors
  • a storage device configured to store one or more programs
  • the one or more processors implement the vehicle stability control method described in any one of the above items.
  • the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the vehicle stability control method as described above is implemented.
  • Fig. 1 is a flowchart of a vehicle stability control method in Embodiment 1 of the present application
  • Fig. 2 is a four-wheel model diagram of the vehicle yaw plane in Embodiment 1 of the present application;
  • Fig. 3 is a comparison diagram of the yaw moment generation effect in the first embodiment of the present application.
  • Fig. 4 is a curve diagram of estimation of center of mass sideslip angle in Embodiment 1 of the present application.
  • Fig. 5 is a curve diagram of yaw rate estimation in Embodiment 1 of the present application.
  • Fig. 6 is a curve diagram of the steering wheel angle in the double line shifting working condition in the first embodiment of the present application.
  • Fig. 7 is a curve diagram of the yaw angular velocity in the double line shifting working condition in the first embodiment of the present application.
  • Fig. 8 is a curve diagram of the side slip angle of the center of mass in the double line shifting working condition in the first embodiment of the present application;
  • Fig. 9 is a pressure curve diagram of the bottom brake wheel cylinder under the double line shifting working condition in the first embodiment of the present application.
  • Fig. 10 is a schematic diagram of a vehicle in Embodiment 3 of the present application.
  • connection should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection. Connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediary, and it can be the internal communication of two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in this application in specific situations.
  • a first feature being "on” or “under” a second feature may include direct contact between the first and second features, and may also include the first and second features Not in direct contact but through another characteristic contact between them.
  • “above”, “above” and “above” the first feature on the second feature include that the first feature is directly above and obliquely above the second feature, or simply means that the first feature is horizontally higher than the second feature.
  • “Below”, “beneath” and “under” the first feature to the second feature include that the first feature is directly below and obliquely below the second feature, or simply means that the first feature has a lower level than the second feature.
  • the vehicle greatly facilitates people's travel. During the driving process, it is inevitable to face continuous large turning conditions. In such working conditions, the vehicle is likely to lose stability due to factors such as axle load transfer, vehicle structural parameter changes, and insufficient road adhesion, which will cause many serious traffic accidents such as tail flicking and rollover. Effective control to ensure driving safety.
  • the vehicle stability control method includes the following steps:
  • the additional yaw moment can be accurately calculated, and the additional yaw moment can be reasonably distributed, which can fully ensure that the vehicle has good steering stability under extreme turning conditions and improve the driving safety of the driver.
  • the stability control converges quickly, the algorithm complexity is low, the complexity of solution is reduced, and the practical application control of the vehicle is realized.
  • step S1 the estimated value of the side slip angle of the vehicle's center of mass is obtained by using a nonlinear vehicle dynamics model combined with unscented Kalman filter estimation and yaw rate estimates For example, considering the close relationship between the vehicle stability control and the side slip angle and yaw rate, the four-wheel model of the vehicle yaw plane is established as shown in Fig.
  • m represents the mass of the vehicle
  • v x represents the longitudinal velocity of the vehicle body in the earth coordinate system
  • ⁇ represents the yaw rate of the vehicle
  • ⁇ represents the side slip angle of the vehicle
  • ⁇ represents the front wheel steering angle; respectively represent the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel
  • I z represents the moment of inertia of the vehicle at the center of mass
  • l f and l r represent the distance from the center of mass to the centerline of the front axle and the rear axle Distance
  • M z represents the additional yaw moment required for vehicle stability control.
  • the lateral force on the tires of the vehicle affects the lateral and yaw motions of the vehicle, and a reasonable description of the force on the tires will affect the estimation accuracy of the side slip angle.
  • the lateral force of the tire can be expressed as:
  • the side slip angles of the front and rear tires are approximately expressed as:
  • the nonlinear vehicle dynamics model (1)-(5) is discretized by forward Euler method as:
  • W (k) represents the process noise whose covariance matrix is Q (k) ;
  • V (k) represents the measurement noise whose covariance matrix is R (k) .
  • ⁇ represents the scale parameter
  • n is the vector dimension of the state to be estimated
  • i represents the corresponding sampling point.
  • the mean weighted value corresponding to the Sigma point is:
  • the covariance weight corresponding to the Sigma point is:
  • ⁇ ⁇ and ⁇ ⁇ represent the designed sampling point parameters.
  • the mean and variance corresponding to the one-step forecast value are:
  • the corresponding Kalman gain matrix is:
  • the estimation accuracy and sensitivity to noise of Kalman filter are closely related to the selection of initial variance of process noise and measurement noise.
  • the effects of process noise covariance matrix Q and measurement noise covariance matrix R on observer estimation results are opposite.
  • Q process noise covariance matrix
  • R measurement noise covariance matrix
  • the signals measured by the vehicle sensors include the yaw rate ⁇ of the vehicle, the front wheel steering angle ⁇ and the longitudinal vehicle speed v x .
  • the side slip angle of the center of mass of the vehicle estimated based on the unscented Kalman filter fully considers the non-linear characteristics of the vehicle, and the estimation accuracy is high.
  • the method of identifying the expected value of the center of mass sideslip angle ⁇ d and the expected value of the yaw rate ⁇ d expected by the driver at the current moment is as follows:
  • K represents the vehicle stability factor, kg ⁇ rad/N.
  • step S3 based on the principle of sliding mode control, combined with the estimated value of the sideslip angle of the center of mass The difference from the expected value of the center of mass sideslip angle ⁇ d , and the estimated value of the yaw rate The difference from the desired value ⁇ d of the yaw rate results in the additional yaw moment M z required for vehicle stability control.
  • an exponential reaching law is used for sliding mode control. Based on the exponential approaching sliding mode control method, the stability control converges quickly and the algorithm complexity is low, which can fully ensure that the vehicle has good steering stability under extreme turning conditions and improve the driving safety of the driver.
  • the additional yaw moment M z required for vehicle stability control is calculated by using a linear two-degree-of-freedom vehicle dynamics model combined with a sliding mode control principle.
  • the nonlinear dynamic model is not conducive to the design of vehicle stability control, so a linear two-degree-of-freedom vehicle dynamic model is introduced.
  • the tire lateral force and slip angle can be approximated as:
  • C f and C r respectively represent the tire cornering stiffness of the front wheel and the rear wheel, N/rad.
  • the deviation between the yaw rate and the side slip angle of the center of mass and the ideal value is selected as the sliding surface, namely:
  • k ⁇ and k ⁇ are normal coefficients of sliding mode surface correction.
  • ⁇ 1 and ⁇ 2 represent the designed exponential reaching law parameters. Set ⁇ 1 >0, ⁇ 2 >0, then by It can be seen that the designed sliding mode controller can satisfy the accessibility condition.
  • the switching function is optimized with a saturation function, namely:
  • the control distribution of the additional yaw torque required for stability control to the tire brake pressure is realized through a single-wheel differential braking technique.
  • Figure 3 shows the yaw moment effect diagram of braking a single vehicle on the whole vehicle. It can be seen that when the vehicle is turning, the inner rear wheel of the braking vehicle will generate an additional lateral force in the same direction as the vehicle turns. The outer front wheel of the braking vehicle will generate an additional yaw moment in the opposite direction to the vehicle steering and the efficiency is the highest. From this, it can be deduced that when the vehicle understeers, it can be achieved by braking the inner rear wheel. Correct understeer of the vehicle; similarly, braking the outer front wheels can correct oversteer of the vehicle.
  • this paper judges whether the vehicle is understeering or oversteering by combining the positive and negative values of steering wheel angle, steering wheel angular velocity and expected additional yaw moment.
  • the formulated brake wheel selection rule table is shown in Table 1, and it is stipulated that the counterclockwise direction is positive.
  • a joint simulation platform is built based on the vehicle dynamics simulation software CarSim and MATLAB/Simulink, and a simulation experiment is performed on the vehicle stability control method.
  • the designed controller is simulated and tested under the standard double-lane shifting condition, the driver's speed is set at 80km/h, and the road surface adhesion coefficient is 0.3.
  • Fig. 4 and Fig. 5 respectively show the estimation results of sideslip angle and yaw rate of center of mass by unscented Kalman filter under the double-lane-shifting condition. It can be seen from the experimental curve that the estimated yaw rate is basically consistent with the real value. The steady-state tracking error of the center-of-mass slip angle is within 0.2deg. Although there was a certain deviation in the estimated value of the sideslip angle of the center of mass at the 4th s, it was still within the acceptable range of error.
  • Figure 6, Figure 7, and Figure 8 are the experimental curves of steering wheel angle, yaw rate, and side slip angle of the center of mass. From the experimental curves, it can be seen that in the state of no controller, the steering wheel angle of the vehicle changes in a relatively large interval, and the steering wheel has not returned to normal after about 9 seconds after the end of driving, and the sideslip angle of the center of mass changes greatly, showing a trend of instability and divergence. And it failed to return to zero in the straight driving condition at the end of the double lane change condition, resulting in the average change value of the vehicle yaw rate maintaining a high level.
  • the front wheel steering angle of the vehicle is controlled within the reasonable range -2deg-2deg required by the driving trajectory, and the yaw rate of the vehicle can closely follow the driver's desired yaw rate.
  • the following error is within 0.5deg/s, and the center-of-mass slip angle changes relatively smoothly, which is always controlled within 2deg.
  • Figure 9 the actual distribution value of the braking pressure control at the bottom layer of the sliding mode control is shown. It can be seen that the wheel cylinder braking pressure changes smoothly, which meets the control transformation limit of the actual braking system.
  • This embodiment provides a vehicle stability control system, including: a state estimation module, an expected value identification module, a sliding mode control module and a control distribution module.
  • the state estimation module can estimate the center of mass side slip angle and yaw rate of the vehicle according to the signal measured by the vehicle sensor to obtain the estimated value of the center of mass side slip angle of the vehicle and yaw rate estimates
  • the expected value identification module can identify the expected center of mass sideslip angle ⁇ d and yaw rate ⁇ d expected by the driver at the current moment according to the longitudinal vehicle speed v x and the front wheel angle ⁇ ;
  • the sliding mode control module communicates with both the state estimation module and the expected value identification module connection, enabling estimation of the sideslip angle based on the center of mass The difference from the expected value of the center of mass sideslip angle ⁇ d , and the estimated value of the yaw rate The difference between the expected value of the yaw rate ⁇ d and the additional yaw moment M z required for vehicle stability control;
  • the control distribution module communicates with the sliding mode control module, and can distribute the additional yaw moment M z to obtain the vehicle Stability controls the wheel cylinder brake pressure
  • the additional yaw moment M z can be accurately calculated, and the additional yaw moment M z can be reasonably distributed, which can fully ensure that the vehicle has good steering stability under extreme turning conditions and improve the driving safety of the driver. sex.
  • the stability control converges quickly, the algorithm complexity is low, the complexity of solution is reduced, and the practical application control of the vehicle is realized.
  • FIG. 10 is a schematic structural diagram of the vehicle in this embodiment.
  • FIG. 10 shows a block diagram of an exemplary vehicle 312 used to implement embodiments of the present application.
  • the vehicle 312 shown in FIG. 10 is only an example, and should not limit the functions and scope of use of the embodiment of the present application.
  • a vehicle 312 takes the form of a universal terminal.
  • Components of the vehicle 312 may include, but are not limited to: a vehicle body (not shown in FIG. 10 ), one or more processors 316, a storage device 328, a bus 318 connecting various system components (including the storage device 328 and the processor 316) .
  • Bus 318 represents one or more of several types of bus structures, including a storage device bus or controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures.
  • these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Micro Channel Architecture (Micro Channel Architecture, MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards Association, VESA) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
  • Vehicle 312 includes various computer system readable media. These media can be any available media that can be accessed by the vehicle 312, including volatile and non-volatile media, removable and non-removable media.
  • Storage device 328 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 330 and/or cache memory 332 .
  • Vehicle 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media.
  • storage system 334 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 10, commonly referred to as a "hard drive”).
  • a disk drive for reading and writing to a removable non-volatile disk such as a "floppy disk”
  • CDROM Compact Disc Read-Only Disk
  • each drive may be connected to bus 318 through one or more data media interfaces.
  • the storage device 328 may include at least one program product having a set (for example, at least one) of program modules configured to execute the functions of the various embodiments of the present application.
  • the program module 342 generally executes the functions and/or methods in the embodiments described in this application.
  • the vehicle 312 may also communicate with one or more external devices 314 (e.g., a keyboard, pointing terminal, display 324, etc.), may also communicate with one or more terminals that enable a user to interact with the vehicle 312, and/or communicate with the Vehicle 312 is capable of communicating with any terminal (eg, network card, modem, etc.) that communicates with one or more other computing terminals. Such communication may be through an input/output (I/O, Input/Output) interface 322 .
  • the vehicle 312 can also communicate with one or more networks (such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the Internet) through the network adapter 320.
  • networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the Internet
  • network adapter 320 communicates with other modules of vehicle 312 via bus 318 .
  • vehicle 312 includes but not limited to: microcode, terminal drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.
  • the processor 316 executes various functional applications and data processing by running the programs stored in the storage device 328 , for example, implementing a vehicle stability control method provided in Embodiment 1 of the present application.
  • the vehicle stability control method includes the following steps:
  • This embodiment provides a storage medium, such as a computer-readable storage medium, on which a computer program is stored.
  • a vehicle stability control method as provided in Embodiment 1 of the present application is implemented.
  • the vehicle stability control method includes the following steps:
  • the storage medium may be a non-transitory storage medium.
  • the computer storage medium in the embodiments of the present application may use any combination of one or more computer-readable media.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • the program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency, radio frequency), etc., or any suitable combination of the above.
  • Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, and conventional procedural Programming language - such as "C" or a similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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Abstract

本申请涉及汽车技术领域，例如涉及一种车辆稳定性控制方法、系统、车辆和存储介质，车辆稳定性控制方法包括：根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值I和横摆角速度估计值II根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；根据所述质心侧偏角估计值I与所述质心侧偏角期望值β d的差值，以及所述横摆角速度估计值II与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；对附加横摆力矩M z进行分配，得到整车稳定性控制至少一个车轮所需的轮缸制动压力。

Description

车辆稳定性控制方法、系统、车辆和存储介质
本申请要求在2021年09月10日提交中国专利局、申请号为202111059998.9的中国专利申请的优先权，以上申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及汽车技术领域，例如涉及一种车辆稳定性控制方法、系统、车辆和存储介质。
背景技术
随着人们对驾乘体验的要求越来越高，汽车不断向智能化方向发展。在行车过程中，不可避免会面对连续大转弯的工况。在此种工况中，车辆易因轴荷转移、车辆结构参数变化、路面附着力不足等因素失去稳定性，由此引发甩尾、侧翻等众多严重的交通事故，因此，需要对汽车进行有效控制，保证行车的安全。
传统车辆稳定性控制大多基于经典的PID(Proportional Integral Derivative，比例积分微分)控制，该算法较为依赖工程经验，针对不同的工况需要更换控制参数以提高系统的鲁棒性，工程师标定负担大。虽然模型预测控制能够考虑执行器特性、稳定性能指标等诸多因素，但预测的精度较低，导致车辆控制的稳定性较差，而且由于其算法运算求解过程的复杂度较高，较难实时地应用于车辆的实际控制中。
发明内容
本申请提供一种车辆稳定性控制方法、系统、车辆和存储介质。
本申请提供一种车辆稳定性控制方法，包括：
根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000001
和横摆角速度估计值
Figure PCTCN2022116077-appb-000002
根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
根据所述质心侧偏角估计值
Figure PCTCN2022116077-appb-000003
与所述质心侧偏角期望值β d的差值，以及所述横摆角速度估计值
Figure PCTCN2022116077-appb-000004
与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
对附加横摆力矩M z进行分配，得到整车稳定性控制至少一个车轮所需的轮 缸制动压力。
本申请提供一种车辆稳定性控制系统，包括：
状态估算模块，设置为根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000005
和横摆角速度估计值
Figure PCTCN2022116077-appb-000006
期望值辨识模块，设置为根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
滑模控制模块，与所述状态估算模块和所述期望值辨识模块分别通讯连接，设置为根据所述质心侧偏角估计值
Figure PCTCN2022116077-appb-000007
与所述质心侧偏角期望值β d的差值，以及所述横摆角速度估计值
Figure PCTCN2022116077-appb-000008
与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
控制分配模块，与所述滑模控制模块通讯连接，设置为对附加横摆力矩M z进行分配，得到整车稳定性控制至少一个车轮所需的轮缸制动压力。
本申请提供一种车辆，所述车辆包括：
一个或多个处理器；
存储装置，设置为存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如上任一项所述的车辆稳定性控制方法。
本申请提供一种存储介质，其上存储有计算机程序，该程序被处理器执行时实现如上所述的车辆稳定性控制方法。
附图说明
图1是本申请实施例一中一种车辆稳定性控制方法的流程图；
图2是本申请实施例一中车辆横摆平面四轮模型图；
图3是本申请实施例一中横摆力矩产生效果对比图；
图4是本申请实施例一中质心侧偏角估计曲线图；
图5是本申请实施例一中横摆角速度估计曲线图；
图6是本申请实施例一中双移线工况方向盘转角曲线图；
图7是本申请实施例一中双移线工况横摆角速度曲线图；
图8是本申请实施例一中双移线工况质心侧偏角曲线图；
图9是本申请实施例一中双移线工况底层制动轮缸压力曲线图；
图10是本申请实施例三中车辆的示意图。
具体实施方式
下面结合附图和实施方式进一步说明本申请的实施例。可以理解的是，此处所描述的具体实施例仅仅用于解释本申请，而非对本申请的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本申请相关的部分而非全部。
在本申请的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本申请中的具体含义。
在本申请中，除非另有明确的规定和限定，第一特征在第二特征之“上”或之“下”可以包括第一和第二特征直接接触，也可以包括第一和第二特征不是直接接触而是通过它们之间的另外的特征接触。而且，第一特征在第二特征“之上”、“上方”和“上面”包括第一特征在第二特征正上方和斜上方，或仅仅表示第一特征水平高度高于第二特征。第一特征在第二特征“之下”、“下方”和“下面”包括第一特征在第二特征正下方和斜下方，或仅仅表示第一特征水平高度小于第二特征。
车辆极大地方便了人们的出行，在行车过程中，不可避免会面对连续大转弯的工况。在此种工况中，车辆易因轴荷转移、车辆结构参数变化、路面附着力不足等因素失去稳定性，由此引发甩尾、侧翻等众多严重的交通事故，因此，需要对汽车进行有效控制，保证行车的安全。
为了能够提升车辆控制的稳定性，而且降低控制过程求解的复杂度，实现对车辆的实际控制，如图1所示，本申请提供一种车辆稳定性控制方法。本车辆稳定性控制方法包括如下步骤：
S1、根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000009
和横摆角速度估计值
Figure PCTCN2022116077-appb-000010
S2、根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
S3、根据质心侧偏角估计值
Figure PCTCN2022116077-appb-000011
与质心侧偏角期望值β d的差值，以及横摆角速度估计值
Figure PCTCN2022116077-appb-000012
与横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
S4、对附加横摆力矩M z进行分配，得到整车稳定性控制各个车轮所需的轮缸制动压力。
通过上述方式，可以准确计算得到附加横摆力矩，并对附加横摆力矩进行合理分配，能够充分保证车辆在极限转弯工况下具有良好的转向稳定性，提高驾驶员行驶安全性。而且采用本车辆稳定性控制方法，稳定性控制收敛快，算法复杂度低，降低求解的复杂度，实现对车辆的实际应用控制。
在一实施例中，步骤S1，使用非线性的车辆动力学模型结合无迹卡尔曼滤波估算得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000013
和横摆角速度估计值
Figure PCTCN2022116077-appb-000014
例如，考虑到车辆稳定性控制与质心侧偏角和横摆角速度存在密切关系，因此忽略空气阻力、轮胎回正力矩等作用，建立如图2所示的车辆横摆平面四轮模型。
那么可以得到车辆侧向运动和横摆运动的方程分别为：
Figure PCTCN2022116077-appb-000015
Figure PCTCN2022116077-appb-000016
式中，m表示车辆的整车质量；v x表示车身在大地坐标系下的纵向速度；γ表示车辆的横摆角速度；β表示车辆的侧偏角；δ表示前轮转向角；
Figure PCTCN2022116077-appb-000017
Figure PCTCN2022116077-appb-000018
分别表示左前轮、右前轮、左后轮、右后轮的侧向力；I z表示车辆在质心处的转动惯量；l f、l r分别表示质心到前轴和后轴中心线的距离；M z表示车辆稳定性控制所需的附加横摆力矩。
车辆运动轮胎受到的侧向力影响车辆侧向运动和横摆运动，合理地描述轮胎的受力情况将影响侧偏角估计精度。选用经典的魔术公式轮胎模型来描述轮胎的非线性特征，那么轮胎的侧向力可以表示为：
Figure PCTCN2022116077-appb-000019
式中，i＝f,r表示前，后车轮；j＝l,r表示左、右车轮；a i,j表示轮胎的侧偏角；B,C,D,E分别表示轮胎的刚度因子、曲线形状因子、曲线峰值因子和曲线曲率因子。
前、后轮胎的侧偏角近似表示为：
Figure PCTCN2022116077-appb-000020
Figure PCTCN2022116077-appb-000021
然后基于无迹卡尔曼滤波对质心侧偏角和横摆角速度进行估计：
本实施例设定横摆角速度和质心侧偏角为系统状态变量，即x＝[γ,β]；设定前轮转角和附加横摆力矩为系统输入，即u＝[δ,M z]；选用易用传感器测量的横摆角速度作为系统的观测变量，即y＝γ。将非线性车辆动力学模型(1)-(5)用向前欧拉法离散化整理为：
Figure PCTCN2022116077-appb-000022
式中，W (k)表示协方差阵为Q (k)的过程噪声；V (k)表示协方差阵为R (k)的测量噪声。
设定无迹卡尔曼滤波状态初始估计值和误差协方差矩阵分别为
Figure PCTCN2022116077-appb-000023
Figure PCTCN2022116077-appb-000024
在设定的估计点附近采样生成2n+1个Sigma点，可以表示为：
Figure PCTCN2022116077-appb-000025
式中，κ表示尺度参数；n为待估计状态的向量维数；i表示对应的采样点。Sigma点对应的均值加权值为：
Figure PCTCN2022116077-appb-000026
Sigma点对应的协方差加权值为：
Figure PCTCN2022116077-appb-000027
式中，α κ和β κ表示设计的采样点参数。
随后计算2n+1个Sigma点集的一步预测值：
Figure PCTCN2022116077-appb-000028
一步预测值对应的均值和方差为：
Figure PCTCN2022116077-appb-000029
Figure PCTCN2022116077-appb-000030
在一步预测值附近采样生成新的2n+1个Sigma点，可以表示为：
Figure PCTCN2022116077-appb-000031
那么预测获得的观测量为：
Figure PCTCN2022116077-appb-000032
系统预测观测量的均值和协方差为：
Figure PCTCN2022116077-appb-000033
Figure PCTCN2022116077-appb-000034
Figure PCTCN2022116077-appb-000035
对应的Kalman增益矩阵为：
Figure PCTCN2022116077-appb-000036
最后更新系统状态和方差：
Figure PCTCN2022116077-appb-000037
Figure PCTCN2022116077-appb-000038
卡尔曼滤波的估计精度及对噪声的敏感性与过程噪声和测量噪声的方差初值选取有着密切的关系。过程噪声协方差矩阵Q和测量噪声协方差矩阵R对于观测器估计结果的作用是相反的。随着Q增大，卡尔曼滤波器对非线性模型的信赖度降低，估计结果受到噪声影响，有较大的震荡，滤波结果较差；随着R增大，系统对测量的横摆角速度的信赖度降低，观测算法更多利用非线性模型运算结果进行估计，震荡情况明显得到改善。考虑到本申请横摆角速度传感器精度较高，最终选取
Figure PCTCN2022116077-appb-000039
R＝5。
在一实施例中，步骤S1中，整车传感器测量得到的信号包括车辆的横摆角速度γ、前轮转向角δ和纵向车速v x。利用非线性的车辆动力学模型结合无迹卡尔曼滤波对车辆的横摆角速度γ、前轮转向角δ和纵向车速v x进行计算，从而估算得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000040
和横摆角速度估计值
Figure PCTCN2022116077-appb-000041
通过上述方式，基于无迹卡尔曼滤波估算的车辆质心侧偏角，充分考虑车辆的非线性特性，估算精度较高。
在一实施例中，根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d的方法如下：
在稳态行驶时，车辆稳定性控制所需的附加横摆力矩为零，且横摆角速度和质心侧偏角为定值，那么此时M z＝0，
Figure PCTCN2022116077-appb-000042
将其代入线性公式(6)中，同时考虑路面附着极限，可得理想横摆角速度γ d为：
Figure PCTCN2022116077-appb-000043
Figure PCTCN2022116077-appb-000044
式中，K表示车辆稳定性因数，kg·rad/N。
驾驶员行车时通常希望车辆质心侧偏角尽可能趋近于零，以获得良好的驾驶感受，因此设定β d＝0。
在一实施例中，步骤S3中，基于滑模控制原理，结合质心侧偏角估计值
Figure PCTCN2022116077-appb-000045
与质心侧偏角期望值β d的差值，以及横摆角速度估计值
Figure PCTCN2022116077-appb-000046
与横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z。例如，采用指数趋近律进行滑模控制。基于指数趋近型滑模控制方法，稳定性控制收敛快，算法复杂度低，能够充分保证车辆在极限转弯工况下具有良好的转向稳定性，提高驾驶员行驶安全性。
在一实施例中，采用线性二自由度车辆动力学模型结合滑模控制原理对车辆稳定性控制所需的附加横摆力矩M z进行计算。例如，非线性动力学模型不利于整车稳定性控制的设计，因此引入线性二自由度车辆动力学模型。轮胎侧向力与侧偏角可以近似表示为：
F f＝C fα f                    (23)
F r＝C rα r                    (24)
式中，C f、C r分别表示前轮和后轮的轮胎侧偏刚度，N/rad。
考虑到前轮转角δ很小，cosδ近似为1。假设左右轮胎受力一致，那么可以获得线性二自由度车辆动力学模型为：
Figure PCTCN2022116077-appb-000047
式中，
Figure PCTCN2022116077-appb-000048
Figure PCTCN2022116077-appb-000049
获得维持车辆稳定性所需的附加横摆力矩。选用横摆角速度和质心侧偏角与理想值之间的偏差为滑模面，即：
s＝k γ(γ-γ d)+k β(β-β d)             (26)
式中，k γ和k β为滑模面修正常系数。
对滑模面进行求导可得：
Figure PCTCN2022116077-appb-000050
进行滑模控制时，我们希望被控系统离滑模面较远时获得较大的趋近速度， 缩短稳定性控制所需的时间；离滑模面较近时降低趋近速度，使系统在滑模面附近尽可能的稳定。因此选用指数趋近律进行滑模控制，可以表示为：
Figure PCTCN2022116077-appb-000051
式中，ε 1和ε 2表示设计的指数趋近律参数。设定ε 1＞0，ε 2＞0，那么由
Figure PCTCN2022116077-appb-000052
可知设计的滑模控制器能够满足可达性条件。
为了减缓系统在滑模面的抖震，对切换函数改用饱和函数进行优化，即：
Figure PCTCN2022116077-appb-000053
式中，
Figure PCTCN2022116077-appb-000054
表示换用的饱和函数边界层厚度。
最终求解得到车辆稳定性控制器的控制律为：
Figure PCTCN2022116077-appb-000055
在一实施例中，通过单轮差动制动技术实现稳定性控制所需的附加横摆转矩到轮胎制动压力的控制分配。如图3所示为制动单个车辆对整车产生的横摆力矩效果图，可以看出，车辆在转弯行驶的过程中，制动车辆的内后轮将产生与车辆转向同方向的附加横摆力矩且效率最高；制动车辆的外前轮将产生与车辆转向反方向的附加横摆力矩且效率最高，由此可以推出，当车辆出现不足转向的现象，可以通过制动内后轮来纠正车辆的不足转向；同理，制动外前轮可以纠正车辆的过度转向现象。
根据上述分析，本文结合方向盘转角、方向盘转角速度和期望附加横摆力矩的正负值来判断车辆是否处于不足转向或者过度转向。制定的制动轮选择规则表如表1所示，且规定逆时针方向为正。
表1 差动制动轮选择规则表
Figure PCTCN2022116077-appb-000056
Figure PCTCN2022116077-appb-000057
根据车辆单轮模型可知，对应车轮所需的差动制动压力P i,j与附加横摆力矩M z之间的关系为：
Figure PCTCN2022116077-appb-000058
式中，r表示车轮半径，单位可为m；K b表示对应车轮的制动效能因素，单位可为Nm/MPa；T b表示车辆的半轮距，单位可为m；J t表示轮胎的转动惯量，单位可为kg·m 2；
Figure PCTCN2022116077-appb-000059
表示对应车轮的角加速度，单位可为rad/·s 2。
在一实施例中，基于车辆动力学仿真软件CarSim和MATLAB/Simulink搭建联合仿真平台，对车辆稳定性控制方法进行仿真实验。
其中本文涉及的车辆动力学参数如表2所示。
表2 车辆参数
参数 数值 单位
m 1850 kg
T b 800 mm
l f 1400 mm
l r 1340 mm
r 310 mm
I z 2890 kg·m 2
C f -150000 N/rad
C r -150000 N/rad
K bf 350 Nm/MPa
K br 150 Nm/MPa
J t 1 kg·m 2
在标准双移线工况下对设计的控制器进行仿真测试，设定驾驶员车速为80km/h，路面附着系数为0.3。
图4和图5分别表示双移线工况下，无迹卡尔曼滤波质心侧偏角和横摆角速度的估计结果。从实验曲线可以看出，横摆角速度估计结果基本和真实值一致。质心侧偏角的稳态跟踪误差在0.2deg以内。虽然在第4s时，质心侧偏角估计值出现了一定偏差，但仍在误差接受范围之内。
图6、图7、和图8关于转向轮转角、横摆角速度、质心侧偏角的实验曲线。从实验曲线可以看出，无控制器状态下，车辆转向轮转角在较大的区间进行变换，在行驶结束9s左右方向盘仍未回正，质心侧偏角变化较大，呈现失稳发散趋势，且未能在双移线工况末端直行工况中归零，导致车辆横摆角速度平均变化值维持较高水平。
在本文设计的稳定性控制器作用下，车辆前轮转角式中控制在行驶轨迹所需的合理区间-2deg-2deg之内，整车横摆角速度能够紧密的跟随驾驶员期望的横摆角速度，跟随误差在0.5deg/s之内，质心侧偏角变换较为平缓，始终控制在2deg之内。如图9所示为滑模控制底层制动压力控制实际分配值，可以看出轮缸制动压力变换平缓，满足实际制动系统的控制变换极限。
实施例二
本实施例提拱了一种车辆稳定性控制系统，包括：状态估算模块、期望值辨识模块、滑模控制模块和控制分配模块。
其中，状态估算模块能够根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000060
和横摆角速度估计值
Figure PCTCN2022116077-appb-000061
期望值辨识模块能够根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；滑模控制模块与状态估算模块和期望值辨识模块均通讯连接，能够根据质心侧偏角估计值
Figure PCTCN2022116077-appb-000062
与质心侧偏角期望值β d的差值，以及横摆角速度估计值
Figure PCTCN2022116077-appb-000063
与横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；控制分配模块与滑模控制模块通讯连接，能够对附加横摆力矩M z进行分配，得到整车稳定性控制各个车轮所需的轮缸制动压力。
通过上述控制系统，可以准确计算得到附加横摆力矩M z，并对附加横摆力矩M z进行合理分配，能够充分保证车辆在极限转弯工况下具有良好的转向稳定性，提高驾驶员行驶安全性。而且采用本车辆稳定性控制方法，稳定性控制收敛快，算法复杂度低，降低求解的复杂度，实现对车辆的实际应用控制。
实施例三
图10为本实施例中的车辆的结构示意图。图10示出了用来实现本申请实施方式的示例性车辆312的框图。图10显示的车辆312仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图10所示，车辆312以通用终端的形式表现。车辆312的组件可以包括但不限于：车辆本体(图10中未示出)、一个或者多个处理器316，存储装置328，连接不同系统组件(包括存储装置328和处理器316)的总线318。
总线318表示几类总线结构中的一种或多种，包括存储装置总线或者存储装置控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(Industry Standard Architecture，ISA)总线，微通道体系结构(Micro Channel Architecture，MCA)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component Interconnect，PCI)总线。
车辆312包括多种计算机系统可读介质。这些介质可以是任何能够被车辆312访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
存储装置328可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(Random Access Memory，RAM)330和/或高速缓存存储器332。车辆312可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统334可以用于读写不可移动的、非易失性磁介质(图10未显示，通常称为“硬盘驱动器”)。尽管图10中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘，例如只读光盘(Compact Disc Read-Only Memory，CD-ROM),数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线318相连。存储装置328可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请各实施例的功能。
具有一组(至少一个)程序模块342的程序/实用工具340，可以存储在例如存储装置328中，这样的程序模块342包括但不限于操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块342通常执行本申请所描述的实施例中的功能和/或方法。
车辆312也可以与一个或多个外部设备314(例如键盘、指向终端、显示器324等)通信，还可与一个或者多个使得用户能与该车辆312交互的终端通信，和/或与使得该车辆312能与一个或多个其它计算终端进行通信的任何终端(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(I/O，Input/Output)接口322进行。并且，车辆312还可以通过网络适配器320与一个或者多个网络(例如局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图10所示，网络适配器320通过总线318与车辆312的其它模块通信。应当明白，尽管图10中未示出，可以结合车辆312使用其它硬件和/或软件模块，包括但不限于：微代码、终端驱动器、冗余处理器、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)系统、磁带驱动器以及数据备份存储系统等。
处理器316通过运行存储在存储装置328中的程序，从而执行各种功能应用以及数据处理，例如实现本申请实施例一所提供的一种车辆稳定性控制方法。本车辆稳定性控制方法包括如下步骤：
S1、根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进 行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000064
和横摆角速度估计值
Figure PCTCN2022116077-appb-000065
S2、根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
S3、根据质心侧偏角估计值
Figure PCTCN2022116077-appb-000066
与质心侧偏角期望值β d的差值，以及横摆角速度估计值
Figure PCTCN2022116077-appb-000067
与横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
S4、对附加横摆力矩M z进行分配，得到整车稳定性控制各个车轮所需的轮缸制动压力。
实施例四
本实施例提供一种存储介质，例如为计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请实施例一所提供的一种车辆稳定性控制方法。本车辆稳定性控制方法包括如下步骤：
S1、根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
Figure PCTCN2022116077-appb-000068
和横摆角速度估计值
Figure PCTCN2022116077-appb-000069
S2、根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
S3、根据质心侧偏角估计值
Figure PCTCN2022116077-appb-000070
与质心侧偏角期望值β d的差值，以及横摆角速度估计值
Figure PCTCN2022116077-appb-000071
与横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
S4、对附加横摆力矩M z进行分配，得到整车稳定性控制各个车轮所需的轮缸制动压力。
存储介质可以是非暂态(non-transitory)存储介质。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM,Erasable Programmable Read-Only Memory)或闪存、光纤、便携式紧凑磁盘只读存储器(CD-ROM,Compact Disc Read-Only Memory)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指 令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于无线、电线、光缆、RF(Radio Frequency，射频)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或终端上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
本申请的上述实施例是为了清楚说明本申请所作的举例，而并非是对本申请的实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。凡在本申请的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本申请权利要求的保护范围之内。

Claims (10)

  1. 一种车辆稳定性控制方法，包括：
    根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
    Figure PCTCN2022116077-appb-100001
    和横摆角速度估计值
    Figure PCTCN2022116077-appb-100002
    根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
    根据所述质心侧偏角估计值
    Figure PCTCN2022116077-appb-100003
    与所述质心侧偏角期望值β d的差值，以及所述横摆角速度估计值
    Figure PCTCN2022116077-appb-100004
    与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
    对附加横摆力矩M z进行分配，得到整车稳定性控制至少一个车轮所需的轮缸制动压力。
  2. 根据权利要求1所述的方法，其中，使用非线性的车辆动力学模型结合无迹卡尔曼滤波估算得到车辆的质心侧偏角估计值
    Figure PCTCN2022116077-appb-100005
    和横摆角速度估计值
    Figure PCTCN2022116077-appb-100006
  3. 根据权利要求1所述的方法，其中，所述整车传感器测量得到的信号包括车辆的横摆角速度γ、前轮转向角δ和纵向车速v x。
  4. 根据权利要求1所述的方法，其中，基于滑模控制原理，结合所述质心侧偏角估计值
    Figure PCTCN2022116077-appb-100007
    与所述质心侧偏角期望值β d的差值，以及所述横摆角速度估计值
    Figure PCTCN2022116077-appb-100008
    与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z。
  5. 根据权利要求4所述的方法，其中，采用指数趋近律进行滑模控制。
  6. 根据权利要求4所述的方法，其中，采用线性二自由度车辆动力学模型结合滑模控制原理对车辆稳定性控制所需的附加横摆力矩M z进行计算。
  7. 根据权利要求1所述的方法，其中，基于车辆动力学仿真软件CarSim和MATLAB/Simulink搭建联合仿真平台，对所述车辆稳定性控制方法进行仿真实验。
  8. 一种车辆稳定性控制系统，包括：
    状态估算模块，设置为根据整车传感器测量得到的信号对车辆的质心侧偏角和横摆角速度进行估计得到车辆的质心侧偏角估计值
    Figure PCTCN2022116077-appb-100009
    和横摆角速度估计值
    Figure PCTCN2022116077-appb-100010
    期望值辨识模块，设置为根据纵向车速v x和前轮转角δ辨识当前时刻驾驶员期望的质心侧偏角期望值β d和横摆角速度期望值γ d；
    滑模控制模块，与所述状态估算模块和所述期望值辨识模块分别通讯连接，设置为根据所述质心侧偏角估计值
    Figure PCTCN2022116077-appb-100011
    与所述质心侧偏角期望值β d的差值，以及所 述横摆角速度估计值
    Figure PCTCN2022116077-appb-100012
    与所述横摆角速度期望值γ d的差值得到车辆稳定性控制所需的附加横摆力矩M z；
    控制分配模块，与所述滑模控制模块通讯连接，设置为对附加横摆力矩M z进行分配，得到整车稳定性控制至少一个车轮所需的轮缸制动压力。
  9. 一种车辆，包括：
    一个或多个处理器；
    存储装置，设置为存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-7中任一项所述的车辆稳定性控制方法。
  10. 一种存储介质，所述存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-7中任一项所述的车辆稳定性控制方法。
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CN109522666A (zh) * 2018-11-27 2019-03-26 袁小芳 一种分布式电动汽车稳定性控制方法
CN113682282A (zh) * 2021-09-10 2021-11-23 中国第一汽车股份有限公司 一种车辆稳定性控制方法、系统、车辆和存储介质

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