Vehicle parameter identification method and system considering measurement error and wheelbase error

Abstract

The application provides a vehicle parameter identification method and system considering measurement error and wheelbase error, comprising the following steps: constructing a target function based on a mathematical model of a target vehicle, considering measurement error and wheelbase error; obtaining mechanical information of the target vehicle and setting calibration parameters; collecting motion information of the target vehicle in real time according to the calibration parameters; optimizing the target function based on the mechanical information and the motion information to obtain an optimization result; updating target vehicle parameters by using the optimization result; and repeating the step to perform rolling update on the target vehicle parameters. According to real-time measured data, the application constructs a residual equation containing sensor measurement error and vehicle parameter error, and uses a nonlinear least square method to solve the sensor measurement error and the vehicle parameter error, thereby improving the accuracy of vehicle parameter identification.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and more specifically, to a method and system for identifying vehicle parameters that takes into account measurement errors and wheelbase errors. Background Technology

[0002] Currently, Model Predictive Control (MPC) is widely used in autonomous driving. Its basic idea is to predict the future output of the system using an existing model, the current system state, and future control variables, achieving control through a rolling solution of a constrained optimization problem. Vehicle models are divided into kinematic and dynamic models. For autonomous vehicles with low speeds, kinematic MPC can be used for control. The effectiveness of MPC control is significantly affected by the vehicle model; therefore, obtaining accurate vehicle parameters in real time helps improve the accuracy of MPC control. The accuracy of parameter identification in existing technologies needs improvement.

[0003] Chinese patent application CN112406889A discloses a kinematics-based vehicle predictive control method and processor, comprising: acquiring the vehicle driving state quantity at the current moment, acquiring the vehicle driving state quantity at the previous moment, and the vehicle control quantity at the previous moment (obtained by solving the vehicle state quantity at the previous moment and the kinematic MPC containing vehicle parameters); modifying the vehicle parameters according to the difference between the predicted state quantity and the actual state quantity to obtain the modified vehicle parameters. The actual state quantity is the vehicle driving state quantity at the current moment, and the predicted state quantity is the vehicle driving state quantity determined based on the vehicle driving state quantity at the previous moment, the vehicle control quantity at the previous moment, and the vehicle kinematic prediction model containing the vehicle parameters. This patent solution has the following main drawbacks: 1. When using kinematic formulas to identify the vehicle wheelbase L, it directly uses... replace 1. It is easy to cause large deviations; 2. The front wheel steering angle is calculated by the steering wheel sensor, which may have zero deviation; 3. Considering the characteristics of the vehicle's actuators, directly using the vehicle's driving state and control quantities to predict future state quantities will have a large error with the actual vehicle state, resulting in inaccurate parameter identification. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a vehicle parameter identification method and system that takes into account measurement errors and wheelbase errors.

[0005] In a first aspect, the present invention provides a vehicle parameter identification method that considers measurement error and wheelbase error, comprising:

[0006] Step S1: Based on the mathematical model of the target vehicle, considering measurement error and wheelbase error, construct the objective function;

[0007] Step S2: Obtain the mechanical information of the target vehicle and set the calibration parameters;

[0008] Step S3: Collect the motion information of the target vehicle in real time according to the calibration parameters;

[0009] Step S4: Optimize the objective function based on the mechanical information and the motion information to obtain the optimization result;

[0010] Step S5: Update the target vehicle parameters using the optimization results;

[0011] Step S6: Repeat steps S3 to S5 to continuously update the target vehicle parameters.

[0012] Preferably, the mathematical model is a kinematic model based on the midpoint of the rear axle.

[0013]

[0014] Or a kinematic model based on the center of mass.

[0015]

[0016] in, δ is the yaw rate, v is the vehicle speed, and δ is the yaw rate. s For steering wheel angle, i s Where L is the steering system transmission ratio, L is the wheelbase, and β is the angle between the vehicle speed and the vehicle's longitudinal direction.

[0017] The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value.

[0018] Preferably, the objective function is

[0019]

[0020] Where m is the number of sampling points, and Δl is the error to be optimized, calculated as follows:

[0021]

[0022] or

[0023]

[0024] Where l0 is the wheelbase error and δ0 is the steering wheel angle error. This represents the yaw rate error.

[0025] Preferably, the mechanical information includes wheelbase L and drivetrain steering ratio i. s ;

[0026] The motion information includes vehicle speed v and steering wheel angle δ. s and yaw rate

[0027] The calibration parameters include the calibration interval duration and the calibration step size. Therefore, the number of sampling points in the calibration interval = calibration interval duration / calibration step size.

[0028] Preferably, step S3 includes:

[0029] Let the calibration interval duration be T. b The calibration step size is T. s Then the number of sampling points in the calibration interval is m = T b / T s ;

[0030] Within the calibration range, the vehicle speed value v1, steering wheel angle value δ1, and yaw rate value are first collected at the first sampling point. Then, the vehicle speed value v2, steering wheel angle value δ2, and yaw rate value were collected at the second sampling point. This process continues until the vehicle speed value v at the m-th sampling point is finally collected. m Steering wheel angle value δ m and yaw rate value

[0031] v1, v2, ..., v m Store this data to obtain the vehicle speed dataset V = {v1, v2, ..., v...} m}; δ1, δ2, ..., δ m Store this data to obtain the steering wheel angle dataset Δ={δ1,δ2,…,δ m};Will Store it to obtain the yaw rate dataset.

[0032] Preferably, the method for optimizing the objective function in step S4 is the nonlinear least squares method;

[0033] The optimization results include the optimized wheelbase error l 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error

[0034] Preferably, step S5 includes:

[0035] The target vehicle parameters include the wheelbase parameter and motion parameters of the target vehicle, and the motion parameters include the steering wheel angle parameter and the yaw rate parameter;

[0036] Based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error Where T s To calibrate the step size, T l The filtering time constant is related to the wheelbase;

[0037] According to the updated wheelbase error l 0_au and update the wheelbase parameter L bu The updated wheelbase parameter L is obtained. au = bu + 0_ ;

[0038] Based on the optimized steering wheel angle error δ 0_op And the steering wheel angle error δ before the update 0_bu Calculate the updated steering wheel angle error Where T s To calibrate the step size, T δ The filtering time constant is related to the steering wheel angle;

[0039] According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameter δ before the update bu The updated steering wheel angle parameter δ is obtained. au = bu + 0_ ;

[0040] Based on the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error Where T s To calibrate the step size, The filtering time constant is related to the yaw rate;

[0041] According to the updated yaw rate error And the yaw rate parameters before the update Obtain the updated yaw rate parameters

[0042] Secondly, the present invention provides a vehicle parameter identification system that considers measurement errors and wheelbase errors, comprising an objective function construction module, a mechanical information acquisition module, a calibration parameter setting module, a motion information acquisition module, an objective function optimization module, and a parameter update and transmission module, wherein:

[0043] The objective function construction module constructs the objective function based on the mathematical model of the target vehicle, taking into account measurement errors and wheelbase errors;

[0044] The mechanical information acquisition module acquires the mechanical information of the target vehicle;

[0045] The calibration parameter setting module sets the calibration parameters;

[0046] The motion information acquisition module acquires the motion information of the target vehicle in real time according to the calibration parameters;

[0047] The objective function optimization module optimizes the objective function based on the mechanical information and the motion information to obtain the optimization result;

[0048] The parameter update transmission module uses the optimization results to update the target vehicle parameters and transmits the updated parameters to the target vehicle.

[0049] The motion information acquisition module, the objective function optimization module, and the parameter update and transmission module sequentially and repeatedly execute their respective tasks to continuously update the target vehicle parameters.

[0050] Preferably, it also includes a controller and sensors for the target vehicle, wherein:

[0051] The mechanical information acquisition module acquires the mechanical information of the target vehicle through the target vehicle's controller;

[0052] The motion information acquisition module collects the motion information of the target vehicle in real time through the target vehicle's sensors;

[0053] The parameter calculation and update module transmits the updated parameters to the controller of the target vehicle.

[0054] Preferably, the mathematical model is a kinematic model based on the midpoint of the rear axle.

[0055]

[0056] Or a kinematic model based on the center of mass

[0057]

[0058] in, δ is the yaw rate, v is the vehicle speed, and δ is the yaw rate. s For steering wheel angle, is Where L is the steering system transmission ratio, L is the wheelbase, and β is the angle between the vehicle speed and the vehicle's longitudinal direction.

[0059] The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value.

[0060] The objective function is:

[0061]

[0062] Where m is the number of sampling points, and Δl is the error to be optimized, calculated as follows:

[0063]

[0064] Where l0 is the wheelbase error and δ0 is the steering wheel angle error. This represents the yaw rate error.

[0065] Preferably, the mechanical information includes wheelbase L and drivetrain steering ratio i. s The motion information includes vehicle speed v and steering wheel angle δ. s and yaw rate The calibration parameters include the calibration interval duration and the calibration step size. Therefore, the number of sampling points in the calibration interval = calibration interval duration / calibration step size.

[0066] The method for setting calibration parameters in the calibration parameter setting module includes:

[0067] Let the calibration interval duration be T. b The calibration step size is T. s Then the number of sampling points in the calibration interval is m = T b / T s ;

[0068] Within the calibration interval, the vehicle speed value v1, steering wheel angle value δ1, and yaw rate value are first collected at the first sampling point. Then, the vehicle speed value v2, steering wheel angle value δ2, and yaw rate value were collected at the second sampling point. This process continues until the vehicle speed value v at the m-th sampling point is finally collected. m Steering wheel angle value δ m and yaw rate value

[0069] v1, v2, ..., v mStore this data to obtain the vehicle speed dataset V = {v1, v2, ..., v...} m}; δ1, δ2, ..., δ m Store this data to obtain the steering wheel angle dataset Δ={δ1,δ2,…,δ m};Will Store it to obtain the yaw rate dataset.

[0070] Preferably, the objective function optimization module optimizes the objective function using a nonlinear least squares method; the optimization result includes the optimized wheelbase error l. 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error

[0071] The method by which the parameter update transmission module updates the target vehicle parameters using the optimization results includes:

[0072] The target vehicle parameters include the wheelbase parameter and motion parameters of the target vehicle, and the motion parameters include the steering wheel angle parameter and the yaw rate parameter;

[0073] Based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error Where T s To calibrate the step size, T l The filtering time constant is related to the wheelbase;

[0074] According to the updated wheelbase error l 0_au and update the wheelbase parameter L bu The updated wheelbase parameter L is obtained. au = bu + 0_ ;

[0075] Based on the optimized steering wheel angle error δ 0_op And the steering wheel angle error δ before the update 0_bu Calculate the updated steering wheel angle error Where T s To calibrate the step size, T δ The filtering time constant is related to the steering wheel angle;

[0076] According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameter δ before the update bu The updated steering wheel angle parameter δ is obtained. au = bu + 0_ ;

[0077] Based on the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error Where T s To calibrate the step size, The filtering time constant is related to the yaw rate;

[0078] According to the updated yaw rate error And the yaw rate parameters before the update Obtain the updated yaw rate parameters

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] 1. Based on real-time measured data, this invention constructs a residual equation that includes sensor measurement error and vehicle parameter error, and uses a nonlinear least squares optimization method to solve for the sensor measurement error and vehicle parameter error, thereby improving the accuracy of vehicle parameter identification.

[0081] 2. Since this invention does not require the use of MPC control output, it can be quickly verified using historical data. Attached Figure Description

[0082] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0083] Figure 1 A flowchart of a vehicle parameter identification method provided in an embodiment of the present invention.

[0084] Figure 2 This is a structural diagram of the vehicle parameter identification system provided in an embodiment of the present invention. Detailed Implementation

[0085] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0086] This invention provides a vehicle parameter identification method that considers measurement errors and wheelbase errors, such as... Figure 1 As shown, it includes:

[0087] Step S1: Based on the mathematical model of the target vehicle, considering measurement error and wheelbase error, construct the objective function;

[0088] In one embodiment, the mathematical model is a kinematic model based on the rear axle midpoint.

[0089]

[0090] Or a kinematic model based on the center of mass.

[0091]

[0092] in, δ is the yaw rate, v is the vehicle speed, and δ is the yaw rate. s For steering wheel angle, i s Where L is the steering system transmission ratio, L is the wheelbase, and β is the angle between the vehicle speed and the vehicle's longitudinal direction.

[0093] The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value.

[0094] In this embodiment, the wheelbase L is the obtained wheelbase value; the steering wheel angle δ s This refers to the measured steering wheel angle value; the yaw rate... This is the measured yaw rate value.

[0095] In one embodiment, the objective function is

[0096]

[0097] Where m is the number of sampling points, and Δl is the error to be optimized, calculated as follows:

[0098]

[0099] or

[0100]

[0101] Where l0 is the wheelbase error and δ0 is the steering wheel angle error. This represents the yaw rate error.

[0102] Step S2: Obtain the mechanical information of the target vehicle and set the calibration parameters;

[0103] In one embodiment, the mechanical information includes wheelbase L and drivetrain steering ratio i. s ;

[0104] The motion information includes vehicle speed v and steering wheel angle δ. s and yaw rate

[0105] The calibration parameters include the calibration interval duration and the calibration step size. Therefore, the number of sampling points in the calibration interval = calibration interval duration / calibration step size.

[0106] Step S3: Collect the motion information of the target vehicle in real time according to the calibration parameters;

[0107] In one embodiment, step S3 includes:

[0108] Let the calibration interval duration be T. b The calibration step size is T. s Then the number of sampling points in the calibration interval is m = T b / T s ;

[0109] Within the calibration range, the vehicle speed value v1, steering wheel angle value δ1, and yaw rate value are first collected at the first sampling point. Then, the vehicle speed value v2, steering wheel angle value δ2, and yaw rate value were collected at the second sampling point. This process continues until the vehicle speed value v at the m-th sampling point is finally collected. m Steering wheel angle value δ m and yaw rate value

[0110] v1, v2, ..., v m Store this data to obtain the vehicle speed dataset V = {v1, v2, ..., v...} m}; δ1, δ2, ..., δ m Store this data to obtain the steering wheel angle dataset Δ={δ1,δ2,…,δ m};Will Store it to obtain the yaw rate dataset.

[0111] In this embodiment, the calibration interval duration T is set. b =20s, calibration step size T s =0.02s, then the number of sampling points in the calibration interval is m = 20s / 0.02s = 1000;

[0112] Step S4: Optimize the objective function based on the mechanical information and the motion information to obtain the optimization result;

[0113] In one embodiment, the method for optimizing the objective function in step S4 employs the nonlinear least squares method;

[0114] The optimization results include the optimized wheelbase error l 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error

[0115] Step S5: Update the target vehicle parameters using the optimization results;

[0116] In one embodiment, step S5 includes:

[0117] The target vehicle parameters include the wheelbase parameter and motion parameters of the target vehicle, and the motion parameters include the steering wheel angle parameter and the yaw rate parameter;

[0118] Based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error Where T s To calibrate the step size, T l The filtering time constant is related to the wheelbase;

[0119] According to the updated wheelbase error l 0_au and update the wheelbase parameter L bu The updated wheelbase parameter L is obtained. au = bu + 0_ ;

[0120] Based on the optimized steering wheel angle error δ 0_op And the steering wheel angle error δ before the update 0_bu Calculate the updated steering wheel angle error Where T s To calibrate the step size, T δ The filtering time constant is related to the steering wheel angle;

[0121] According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameter δ before the update bu The updated steering wheel angle parameter δ is obtained. au = bu + 0_ ;

[0122] Based on the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error Where T s To calibrate the step size, The filtering time constant is related to the yaw rate;

[0123] According to the updated yaw rate error And the yaw rate parameters before the update Obtain the updated yaw rate parameters

[0124] Step S6: Repeat steps S3 to S5 to continuously update the target vehicle parameters.

[0125] The present invention also provides a vehicle parameter identification system that takes into account measurement errors and wheelbase errors, such as... Figure 2 As shown, it includes an objective function construction module, a mechanical information acquisition module, a calibration parameter setting module, a motion information acquisition module, an objective function optimization module, and a parameter update and transmission module, wherein:

[0126] The objective function construction module constructs the objective function based on the mathematical model of the target vehicle, taking into account measurement errors and wheelbase errors;

[0127] The mechanical information acquisition module acquires the mechanical information of the target vehicle;

[0128] The calibration parameter setting module sets the calibration parameters;

[0129] The motion information acquisition module acquires the motion information of the target vehicle in real time according to the calibration parameters;

[0130] The objective function optimization module optimizes the objective function based on the mechanical information and the motion information to obtain the optimization result;

[0131] The parameter update transmission module uses the optimization results to update the target vehicle parameters and transmits the updated parameters to the target vehicle.

[0132] The motion information acquisition module, the objective function optimization module, and the parameter update and transmission module sequentially and repeatedly execute their respective tasks to continuously update the target vehicle parameters.

[0133] In one embodiment, the system further includes a controller and sensors for the target vehicle, wherein:

[0134] The mechanical information acquisition module acquires the mechanical information of the target vehicle through the target vehicle's controller;

[0135] The motion information acquisition module collects the motion information of the target vehicle in real time through the target vehicle's sensors; the parameter calculation and update module transmits the updated parameters to the target vehicle's controller.

[0136] Specifically, the mathematical model is a kinematic model based on the midpoint of the rear axle.

[0137]

[0138] Or a kinematic model based on the center of mass.

[0139]

[0140] in, δ is the yaw rate, v is the vehicle speed, and δ is the yaw rate. s For steering wheel angle, i s Where L is the steering system transmission ratio, L is the wheelbase, and β is the angle between the vehicle speed and the vehicle's longitudinal direction.

[0141] The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value.

[0142] In one embodiment, the objective function is

[0143]

[0144] Where m is the number of sampling points, and Δl is the error to be optimized, calculated as follows:

[0145]

[0146] Where l0 is the wheelbase error and δ0 is the steering wheel angle error. This represents the yaw rate error.

[0147] In one embodiment, the mechanical information includes wheelbase L and drivetrain steering ratio i. s The motion information includes vehicle speed v and steering wheel angle δ. s and yaw rate The calibration parameters include the calibration interval duration and the calibration step size. Therefore, the number of sampling points in the calibration interval = calibration interval duration / calibration step size.

[0148] The method for setting calibration parameters in the calibration parameter setting module includes:

[0149] Let the calibration interval duration be T. b The calibration step size is T. s Then the number of sampling points in the calibration interval is m = T b / T s ;

[0150] Within the calibration interval, the vehicle speed value v1, steering wheel angle value δ1, and yaw rate value are first collected at the first sampling point. Then, the vehicle speed value v2, steering wheel angle value δ2, and yaw rate value were collected at the second sampling point. This process continues until the vehicle speed value v at the m-th sampling point is finally collected. m Steering wheel angle value δ m and yaw rate value

[0151] v1, v2, ..., v m Store this data to obtain the vehicle speed dataset V = {v1, v2, ..., v...} m}; δ1, δ2, ..., δ m Store this data to obtain the steering wheel angle dataset Δ={δ1,δ2,…,δ m};Will Store it to obtain the yaw rate dataset.

[0152] In one embodiment, the objective function optimization module optimizes the objective function using a nonlinear least squares method; the optimization result includes the optimized wheelbase error l. 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error

[0153] The method by which the parameter update transmission module updates the target vehicle parameters using the optimization results includes:

[0154] The target vehicle parameters include the wheelbase parameter and motion parameters, wherein the motion parameters include the steering wheel angle parameter and the yaw rate parameter; based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error Where T s To calibrate the step size, T l The filtering time constant is related to the wheelbase;

[0155] According to the updated wheelbase error l 0_au and update the wheelbase parameter L bu The updated wheelbase parameter L is obtained. au = bu + 0_ ;

[0156] Based on the optimized steering wheel angle error δ 0_op And the steering wheel angle error δ before the update 0_bu Calculate the updated steering wheel angle error Where T s To calibrate the step size, T δThe filtering time constant is related to the steering wheel angle;

[0157] According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameter δ before the update bu The updated steering wheel angle parameter δ is obtained. au = bu + 0_ According to the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error Where T s To calibrate the step size, The filtering time constant is related to the yaw rate;

[0158] According to the updated yaw rate error And the yaw rate parameters before the update Obtain the updated yaw rate parameters

[0159] This invention is based on real-time measured data, including wheelbase L, vehicle speed v, and steering wheel angle δ. s and yaw rate The residual equation is constructed, and then the objective function is optimized and solved using the nonlinear least squares method to obtain the sensor measurement error and the vehicle parameter error, thereby improving the accuracy of vehicle parameter identification.

[0160] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for identifying vehicle parameters considering measurement error and wheelbase error, characterized in that, include: Step S1: Based on the mathematical model of the target vehicle, considering measurement error and wheelbase error, construct the objective function; Step S2: Obtain the mechanical information of the target vehicle and set the calibration parameters; Step S3: Collect the motion information of the target vehicle in real time according to the calibration parameters; Step S4: Optimize the objective function based on the mechanical information and the motion information to obtain the optimization result; Step S5: Update the target vehicle parameters using the optimization results; Step S6: Repeat steps S3 to S5 to continuously update the target vehicle parameters; The mathematical model is a kinematic model based on the midpoint of the rear axle. Or a kinematic model based on the center of mass in, The yaw rate is angular velocity. v For vehicle speed, δ s For steering wheel angle, i s For the steering system gear ratio, L Wheelbase β The angle between the vehicle speed and the longitudinal direction of the vehicle; The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value. The objective function is: in, m Δ is the number of sampling points. l The error to be optimized is calculated as follows: or in, l 0 represents wheelbase error. δ 0 represents the steering wheel angle error. This represents the yaw rate error.

2. The vehicle parameter identification method considering measurement error and wheelbase error according to claim 1, characterized in that, The mechanical information includes wheelbase. L and drivetrain steering ratio i s ; The motion information includes vehicle speed. v Steering wheel angle δ s and yaw rate ; The calibration parameters include the calibration interval duration and the calibration step size. Therefore, the number of sampling points in the calibration interval = calibration interval duration / calibration step size.

3. The vehicle parameter identification method considering measurement error and wheelbase error according to claim 2, characterized in that, Step S3 includes: Let the calibration interval duration be... T b The calibration step size is T s The number of sampling points in the calibration interval m = T b / T s ; Within the calibration interval, the vehicle speed value is first collected at the first sampling point. v 1. Steering wheel angle value δ 1 and yaw rate value Then, the vehicle speed value at the second sampling point was collected. v 2. Steering wheel angle value δ 2 and yaw rate value And so on, until the last collection is completed. m Vehicle speed values ​​at sampling points v m Steering wheel angle value δ m and yaw rate value ; Will v 1. v 2、…、 v m Store it to obtain the vehicle speed dataset V = { v 1, v 2, …, v m };Will δ 1. δ 2、…、 δ m Store it to obtain the steering wheel angle dataset Δ = { δ 1, δ 2, …, δ m };Will , … Store it to obtain the yaw rate dataset. = { , , …, } 4. The vehicle parameter identification method considering measurement error and wheelbase error according to claim 1, characterized in that, The method for optimizing the objective function in step S4 is the nonlinear least squares method; The optimization results include the optimized wheelbase error. l 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error .

5. The vehicle parameter identification method considering measurement error and wheelbase error according to claim 4, characterized in that, Step S5 includes: The target vehicle parameters include the wheelbase parameter and motion parameters of the target vehicle, and the motion parameters include the steering wheel angle parameter and the yaw rate parameter; Based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error ,in T s To calibrate the step size, T l The filtering time constant is related to the wheelbase; According to the updated wheelbase error l 0_au and update the wheelbase parameters L bu The updated wheelbase parameters are obtained. ; Based on the optimized steering wheel angle error δ 0_op Steering wheel angle error before update δ 0_bu Calculate the updated steering wheel angle error ,in T s To calibrate the step size, T δ The filtering time constant is related to the steering wheel angle; According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameters before the update δ bu The updated steering wheel angle parameters are obtained. ; Based on the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error ,in T s To calibrate the step size, The filtering time constant is related to the yaw rate; According to the updated yaw rate error And the yaw rate parameters before the update The updated yaw rate parameters were obtained. .

6. A vehicle parameter identification system considering measurement error and wheelbase error, characterized in that, It includes a target function construction module, a mechanical information acquisition module, a calibration parameter setting module, a motion information acquisition module, a target function optimization module, and a parameter update and transmission module, wherein: The objective function construction module constructs the objective function based on the mathematical model of the target vehicle, taking into account measurement errors and wheelbase errors; The mechanical information acquisition module acquires the mechanical information of the target vehicle; The calibration parameter setting module sets the calibration parameters; The motion information acquisition module acquires the motion information of the target vehicle in real time according to the calibration parameters; The objective function optimization module optimizes the objective function based on the mechanical information and the motion information to obtain the optimization result; The parameter update transmission module uses the optimization results to update the target vehicle parameters and transmits the updated parameters to the target vehicle. The motion information acquisition module, the objective function optimization module, and the parameter update transmission module sequentially and repeatedly execute their respective tasks to continuously update the target vehicle parameters. The mathematical model is a kinematic model based on the midpoint of the rear axle. Or a kinematic model based on the center of mass in, The yaw rate is angular velocity. v For vehicle speed, δ s For steering wheel angle, i s For the steering system gear ratio, L Wheelbase β The angle between the vehicle speed and the longitudinal direction of the vehicle; The measurement errors include yaw rate error and steering wheel angle error. The yaw rate error is the difference between the actual yaw rate value and the measured yaw rate value. The steering wheel angle error is the difference between the actual steering wheel angle value and the measured steering wheel angle value. The wheelbase error is the difference between the actual wheelbase value and the obtained wheelbase value. The objective function is: in, m Δ is the number of sampling points. l The error to be optimized is calculated as follows: in, l 0 represents wheelbase error. δ 0 represents the steering wheel angle error. This represents the yaw rate error.

7. The vehicle parameter identification system considering measurement error and wheelbase error according to claim 6, characterized in that, It also includes the target vehicle's controller and sensors, among which: The mechanical information acquisition module acquires the mechanical information of the target vehicle through the target vehicle's controller; The motion information acquisition module collects the motion information of the target vehicle in real time through the target vehicle's sensors; The parameter calculation and update module transmits the updated parameters to the controller of the target vehicle.

8. The vehicle parameter identification system considering measurement error and wheelbase error according to claim 6, characterized in that, The mechanical information includes wheelbase. L and drivetrain steering ratio i s The motion information includes vehicle speed. v Steering wheel angle δ s and yaw rate The calibration parameters include the calibration interval duration and the calibration step size, so the number of sampling points in the calibration interval = calibration interval duration / calibration step size; The method for setting calibration parameters in the calibration parameter setting module includes: Let the calibration interval duration be... T b The calibration step size is T s The number of sampling points in the calibration interval m = T b / T s ; Within the calibration interval, the vehicle speed value is first collected at the first sampling point. v 1. Steering wheel angle value δ 1 and yaw rate value Then, the vehicle speed value at the second sampling point was collected. v 2. Steering wheel angle value δ 2 and yaw rate value And so on, until the last collection is completed. m Vehicle speed values ​​at sampling points v m Steering wheel angle value δ m and yaw rate value ; Will v 1. v 2、…、 v m Store it to obtain the vehicle speed dataset V = { v 1, v 2, …, v m };Will δ 1. δ 2、…、 δ m Store it to obtain the steering wheel angle dataset Δ = { δ 1, δ 2, …, δ m };Will , … Store it to obtain the yaw rate dataset. = { , , …, } 9. The vehicle parameter identification system considering measurement error and wheelbase error according to claim 6, characterized in that, The objective function optimization module optimizes the objective function using a nonlinear least squares method; the optimization result includes the optimized wheelbase error. l 0_op Optimized steering wheel angle error δ 0_op And the optimized yaw rate error ; The method by which the parameter update transmission module updates the target vehicle parameters using the optimization results includes: The target vehicle parameters include the wheelbase parameter and motion parameters of the target vehicle, and the motion parameters include the steering wheel angle parameter and the yaw rate parameter; Based on the optimized wheelbase error l 0_op and the wheelbase error before updating l 0_bu Calculate the updated wheelbase error ,in T s To calibrate the step size, T l The filtering time constant is related to the wheelbase; According to the updated wheelbase error l 0_au and update the wheelbase parameters L bu The updated wheelbase parameters are obtained. ; Based on the optimized steering wheel angle error δ 0_op Steering wheel angle error before update δ 0_bu Calculate the updated steering wheel angle error ,in T s To calibrate the step size, T δ The filtering time constant is related to the steering wheel angle; According to the updated steering wheel angle error δ 0_au And the steering wheel angle parameters before the update δ bu The updated steering wheel angle parameters are obtained. ; Based on the optimized yaw rate error Compared with the yaw rate error before the update Calculate the updated yaw rate error ,in T s To calibrate the step size, The filtering time constant is related to the yaw rate; According to the updated yaw rate error And the yaw rate parameters before the update The updated yaw rate parameters were obtained. .

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