Whole vehicle parameter processing method and device, storage medium and electronic equipment

Abstract

The invention provides a whole vehicle parameter processing method and device, a storage medium and electronic equipment. The whole vehicle parameter processing method comprises the steps that all whole vehicle parameters of a vehicle are determined; for each whole vehicle parameter, acquiring collected data of the whole vehicle parameter, and calibrating the whole vehicle parameter based on the collected data of the whole vehicle parameter; determining the whole vehicle parameter with the self-adaptive learning mark as a target parameter; and for each target parameter, matching processing is carried out based on the adaptive learning logic of the target parameter, and when a matching result representing that the parameters of the vehicle are abnormal is obtained, parameter abnormity management and control processing is carried out. And after the whole vehicle parameters with the self-adaptive learning marks are matched, when a matching result representing that the parameters of the vehicle are abnormal is obtained, parameter abnormality management and control processing is performed, so that the risk caused by parameter abnormality is reduced, and the risk of a driver is reduced.

Description

Technical Field

[0001] The present invention relates to the field of automotive electronic technology, and in particular to a whole vehicle parameter processing method and device, a storage medium and an electronic device. Background Art

[0002] Chassis braking system is closely related to driving safety, and the calibration and matching of braking vehicle parameters have always been the focus of automotive electronics suppliers. For traditional chassis braking controllers, such as controller solutions that use an integrated braking system and redundant braking units, or controller solutions that use an electronic stability control system and an electric brake booster system, the braking parameters used are mostly derived from the input of the original equipment manufacturer (OEM) and the input of the caliper / tire supplier, and based on this part of the input, the verification is completed through calibration.

[0003] The parameters obtained by the traditional parameter calibration method are relatively fixed. When the vehicle is modified or the vehicle is worn or the tires are replaced during driving, the vehicle parameters cannot be changed adaptively, resulting in abnormal vehicle matching parameters and posing a danger to the driver. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a vehicle parameter processing method and device, a storage medium and an electronic device. The present invention calibrates various vehicle parameters of the vehicle, and matches the vehicle parameters with adaptive learning marks. When an abnormal matching result is obtained in the parameters characterizing the vehicle, parameter abnormality control is performed to reduce the danger to the driver.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of the present invention discloses a vehicle parameter processing method, comprising:

[0007] Determine the vehicle parameters;

[0008] For each of the vehicle parameters, acquiring collected data of the vehicle parameter, and calibrating the vehicle parameter based on the collected data of the vehicle parameter;

[0009] Determine the vehicle parameters with adaptive learning marks as target parameters;

[0010] For each of the target parameters, matching processing is performed based on the adaptive learning logic of the target parameter, and when a matching result indicating that there is an abnormality in the parameters of the vehicle is obtained, parameter abnormality control processing is performed.

[0011] The second aspect of the present invention discloses a vehicle parameter processing device, comprising:

[0012] A first determination unit, used to determine various vehicle parameters of the vehicle;

[0013] An acquisition unit, configured to acquire, for each of the vehicle parameters, collected data of the vehicle parameter, and calibrate the vehicle parameter based on the collected data of the vehicle parameter;

[0014] A second determination unit is used to determine the vehicle parameters with adaptive learning marks as target parameters;

[0015] The matching unit is used to perform matching processing for each of the target parameters based on the adaptive learning logic of the target parameters, and perform parameter abnormality control processing when a matching result indicating that the parameters of the vehicle are abnormal is obtained.

[0016] A third aspect of the present invention discloses a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the vehicle parameter processing method as described above.

[0017] A fourth aspect of the present invention discloses an electronic device, comprising a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to perform the vehicle parameter processing method as described above.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] The present invention provides a whole vehicle parameter processing method and device, storage medium and electronic equipment, including: determining various whole vehicle parameters of a vehicle; for each whole vehicle parameter, obtaining the collected data of the whole vehicle parameter, and calibrating the whole vehicle parameter based on the collected data of the whole vehicle parameter; determining the whole vehicle parameter with an adaptive learning mark as the target parameter; for each target parameter, performing matching processing based on the adaptive learning logic of the target parameter, and performing parameter abnormality control processing when a matching result indicating that the parameters characterizing the vehicle are abnormal is obtained. After matching processing is performed on the whole vehicle parameters with an adaptive learning mark, when a matching result indicating that the parameters characterizing the vehicle are abnormal is obtained, parameter abnormality control processing is performed to reduce the risk caused by parameter abnormality and reduce the risk of the driver. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0021] Figure 1 A flow chart of a vehicle parameter processing method provided by an embodiment of the present invention;

[0022] Figure 2 The calibration process of the wheel speed magnetic pole pair number provided in the embodiment of the present invention;

[0023] Figure 3 An example diagram of calculating the centroid position provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of the structure of a vehicle parameter processing device provided by an embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0028] Terminology explanation:

[0029] OEM: Original Equipment Manufacturer.

[0030] With the continuous improvement of automobile electrification, intelligence, and integration, the number and types of electrical equipment in the whole vehicle have increased sharply, and the difficulty of matching various controllers has also increased. For example, compared with the traditional controller solution, the chassis domain controller not only improves the integration of on-board sensors, but also can coordinate the actuators in the horizontal, longitudinal, and vertical directions of the chassis at the same time, greatly improving the boundary of vehicle stability and improving the comfort and passability of the whole vehicle. For the chassis domain controller solution, a variety of vehicle parameters need to be involved, and the parameter matching of the braking system requires more adaptability and self-learning logic. It can be said that the development of technology has put forward new requirements for the calibration of relevant parameters of the whole vehicle and the matching verification of parameters provided by OEMs.

[0031] In the current technical solution, the braking parameters are mostly derived from the input of OEM and caliper / tire suppliers. Based on these inputs, automotive electronics developers complete the calibration through calibration. The limitations of this calibration method are:

[0032] 1. The obtained parameters are relatively fixed and cannot adapt to the target vehicle's tire / caliper replacement, parameter changes caused by mechanical structure wear, etc., which may lead to a decrease in vehicle braking performance as the driving mileage increases.

[0033] 2. For some modified vehicles, there may be danger to the driver due to mismatching of the brake system.

[0034] 3. Calibration requires developers and OEMs to organize dedicated personnel to perform it in a specific environment, resulting in high costs for vehicle configuration changes, vehicle additions, and brake system additions of other models; consumers also have less choice in tires, calipers, etc.

[0035] In order to solve the above-mentioned problems, the present invention provides a whole vehicle parameter processing solution. After calibrating the various whole vehicle parameters of the vehicle, the whole vehicle parameters with adaptive learning marks are determined as target parameters, and matching processing is performed based on the adaptive learning logic of the target parameters. When the matching result that characterizes the parameters of the vehicle is abnormal is obtained, parameter abnormality control processing is performed to achieve control of the risk of abnormal vehicle parameters and reduce the danger to the driver.

[0036] The present invention can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, etc. The present invention can be applied to a system for calibrating and matching vehicle parameters, which can be built using computers, processors, servers and other devices.

[0037] Reference Figure 1, is a flow chart of a vehicle parameter processing method provided by an embodiment of the present invention, and is specifically described as follows S101-S104:

[0038] S101, determining various vehicle parameters of the vehicle.

[0039] Preferably, the various vehicle parameters include wheel speed magnetic pole pair number, shaft rotational inertia, shaft friction torque, brake coefficient, stability factor and front and rear axle lateral stiffness. Furthermore, in addition to the above contents, they also include transmission system transmission ratio, pedal curve and center of mass position.

[0040] S102: For each vehicle parameter, acquire the collected data of the vehicle parameter, and calibrate the vehicle parameter based on the collected data of the vehicle parameter.

[0041] It should be noted that different vehicle parameter data collection methods are used to obtain data, and some vehicle parameter data collection methods have multiple methods for obtaining data. The data acquisition method can be determined based on the actual application scenario.

[0042] The collected data for different vehicle parameters are different, and the calibration process of each vehicle parameter is explained one by one.

[0043] Reference Figure 2 , which is a calibration process of the wheel speed magnetic pole pair number provided in an embodiment of the present invention, is specifically described as follows:

[0044] S201, determining whether there is OEM input parameter data;

[0045] S202, when there is OEM input parameter data, the parameter data is used as the acquired data, and the parameter data is directly used to calibrate the wheel speed magnetic pole pair number;

[0046] S203, when there is no parameter data input by the OEM and the acquisition device is used to acquire the acquired data, the waveform data in the acquired data is used to calibrate the number of wheel speed magnetic pole pairs;

[0047] S204: When there is no parameter data input by the OEM and the message signal with wheel pulse count is used as the collected data, the wheel speed magnetic pole pair number is calibrated using the effective wheel radius of the vehicle and the wheel pulse signal in the message signal.

[0048] Furthermore, there are three ways to obtain the collected data of the wheel speed magnetic pole pair number. The first is to use the parameter data provided by the OEM of the vehicle as the collected data, the second is to use the collection equipment to collect the data of the tire of the vehicle to obtain the collected data, and the third is to use the message signal containing the wheel pulse count as the collected data. Preferably, when the parameter data provided by the OEM exists in the vehicle, the first method is directly used, and when the parameter data provided by the OEM does not exist in the vehicle, the second or third method is selected according to actual needs.

[0049] For the acquisition equipment mentioned in the second type, the collector is an oscilloscope and a sensor. The process of obtaining acquisition parameters is as follows: remove the wheel speed sensor wiring from any wheel and complete the acquisition through an oscilloscope; for example, for an intelligent ak protocol wheel speed sensor, it is powered by 9-16V, and the signal line is grounded through a resistor of about 100Ω. The oscilloscope collects the level of the far end of the resistor to obtain the waveform of the sensor. Put the car in neutral gear, push the car so that the designated wheel turns to an exact integer multiple of the number of circles, record the number of waveforms in the oscilloscope, and use the recorded data as the acquisition data. Different test methods can be used for sensors of different protocols and types. For example, the common ak protocol needs to record the number of the first large square wave corresponding to the 28mA current, while the PWM type sensor needs to count the number of square waves.

[0050] Furthermore, the collected data obtained using the second method includes the number of waveforms of the vehicle's tires after they have rotated an integer multiple of circles. The number of waveforms can be divided by the number of circles the tires have rotated to obtain the wheel speed pole pair number, and the calibration of the wheel speed pole pair number is now completed.

[0051] The message signal mentioned in the third type is obtained by the vehicle's CAN. Specifically, if the vehicle is equipped with controllers such as ESC / OneBox / EMB, it can be compared with the wheel pulse count message for verification, and the vehicle can be driven at an idle speed for a period of time so that the message signal contains the wheel pulse count information. At this time, the wheel pulse count in the message signal and the effective wheel radius of the vehicle are used to calculate the wheel speed pole pair number, and the calibration of the wheel speed pole pair number is completed.

[0052] Preferably, different wheels have corresponding wheel speed magnetic pole pairs. Under ideal conditions, the wheel speed magnetic pole pairs of all wheels are the same.

[0053] For the traditional system transmission ratio, the collected data of the transmission system transmission ratio is obtained, the wheel speed signal and the motor speed signal in the collected data of the transmission system transmission ratio are fitted to obtain the transmission system transmission ratio, and the calibration of the transmission system transmission ratio is completed.

[0054] Furthermore, when acquiring the acquisition data of the transmission ratio of the traditional system, the wheel speed signal and the motor speed signal are acquired, and the two signals are packaged into the acquisition data. Among them, when the current chassis CAN message of the vehicle has a wheel speed signal, the wheel speed signal is directly applied. When the current chassis CAN message of the vehicle does not have a wheel speed signal, refer to the calibration process of the wheel speed magnetic pole pair number above, and the external device performs parsing and recording to obtain the wheel speed signal. In other words, when there is a signal or message of the wheel speed that has been parsed in the vehicle, the speed is used. If not, refer to the calibration process of the wheel speed magnetic pole pair number above, and the external device performs parsing and recording.

[0055] When obtaining the motor speed signal, the vehicle can be driven to 30kph, coasted in neutral for a period of time, and the motor speed (i.e., motor speed signal) issued by the drive motor controller in the power domain is recorded. At the same time, the motor also outputs torque and drive shaft end torque. Preferably, the motor output torque and shaft end power torque signal (i.e., drive shaft end torque) can also be obtained through the vehicle CAN network, and then the power transmission ratio can be obtained by fitting the motor output torque and shaft end power torque signal. The ratio between the power transmission ratio and the traditional ratio of the traditional system is the assumption of the power domain on the torque transmission efficiency of the whole vehicle.

[0056] For shaft moment of inertia, the collected data of shaft moment of inertia includes the data of driven shaft and non-driven shaft. Shaft moment of inertia can also be called equivalent shaft moment of inertia.

[0057] The process of obtaining drive shaft data: lift the vehicle with a lift and turn off the TCS (traction control system) function, lightly step on the drive pedal, and record the drive torque signal and wheel speed signal. Furthermore, if the vehicle TCS cannot be connected and there is no relevant message about the wheel speed after unplugging the corresponding controller, the wheel speed signal can be collected through an external device.

[0058] The process of obtaining the shaft rotation inertia of the drive shaft is as follows: the drive shaft acceleration is calculated according to the wheel speed signal, and then the drive shaft acceleration is fitted with the driving torque to obtain the shaft rotation inertia of the drive shaft (equivalent to the motor end).

[0059] For the shaft rotation inertia of the non-driven shaft: make the wheel speed reach a certain rotation speed, use the method of lightly stepping on the brake pedal to obtain the braking torque signal of the braking system, and then use the braking torque information of the braking system to complete the calibration of the shaft rotation inertia of the non-driven shaft; or after the wheel speed reaches a certain rotation speed, use the relevant parameters of the subsequent calibrated brake shift to complete the calibration of the shaft rotation inertia of the non-driven shaft.

[0060] After completing the calibration of the shaft moment of inertia of all driven shafts and non-driven shafts in the vehicle, the shaft moment of inertia calibration process is completed.

[0061] It should be noted that the shaft moment of inertia calibrated at this time is the shaft moment of inertia without considering the shaft friction torque. After the friction torque is subsequently calibrated, the calibrated shaft friction torque can be used to correct the shaft moment of inertia.

[0062] For the shaft friction torque, the collected data of the shaft friction torque includes the driving shaft friction data and the non-driving shaft friction data. The driving shaft friction data and the non-driving shaft friction data are obtained from the collected data of the shaft friction torque, the driving shaft friction data are processed to calibrate the shaft friction torque of the driving shaft, and the non-driving shaft friction data are processed to calibrate the shaft friction torque of the non-driving shaft; further, the shaft friction torque of the driving shaft can be used to correct the shaft rotational inertia of the driving shaft mentioned above, and the shaft friction torque of the non-driving shaft can be used to correct the shaft rotational inertia of the non-driving shaft mentioned above.

[0063] Furthermore, when calibrating the shaft friction torque, for the shaft friction torque of the drive shaft: lift it with a lift, turn off the vehicle TCS function, speed up through the drive pedal / external device, for example, lightly step on the drive to rotate the vehicle drive shaft, so that each shaft has a certain speed, then release the drive / brake pedal, and put it in neutral, record the process of the wheel speed signal decaying to 0 (if the vehicle TCS cannot be turned off, there is no wheel speed message after unplugging the corresponding controller, then collect the wheel speed through an external device), calculate the shaft drive shaft deceleration based on the wheel speed signal, and the product of the drive shaft deceleration and the shaft rotation inertia of the drive shaft calibrated above is the shaft friction torque of the drive shaft. For the shaft friction torque of the non-drive shaft, it is necessary to use equipment to make it reach a certain speed, then release the drive / brake pedal, wait for the wheel speed signal to decay to 0, record the process, and then calculate the non-drive shaft deceleration, and the product of the non-drive shaft deceleration and the shaft rotation inertia of the non-drive shaft calibrated above is the shaft friction torque of the non-drive shaft.

[0064] After the shaft friction torque calibration is completed, it can be brought into the shaft rotation inertia calibration mentioned above to check the shaft rotation inertia and obtain a more accurate shaft rotation inertia.

[0065] For the pedal feel curve, the collected data of the pedal curve is obtained, the master cylinder pressure sensor information and the brake pedal depth information in the collected data of the pedal curve are fitted to obtain the pedal curve, and the calibration of the pedal curve is completed.

[0066] When acquiring the collected data of the pedal curve, it can be determined whether the vehicle has OEM input data. If so, the OEM input data is verified and used as the collected data. If there is no OEM input data, if the CAN network has the master cylinder pressure sensor information (VDA360, eg), the master cylinder pressure sensor information is recorded in real time. If there is no master cylinder pressure sensor information in the CAN network, a tee and an oil pressure sensor are added to the brake oil circuit where the hard pipe turns into a soft pipe to collect the master cylinder pressure sensor information. The driver steps on the brake pedal at various pedal depths / pedal speeds to record the brake pedal signal and hydraulic signal. The recorded brake pedal signal and hydraulic signal are the brake pedal depth information. At this point, the collected data of the pedal curve is obtained.

[0067] For the brake coefficient, the collected data of the brake coefficient is obtained, and the master cylinder pressure sensor coefficient and equipment parameters in the collected data of the shaft rotational inertia, shaft friction torque and brake coefficient are processed to obtain the friction coefficient of the brake and complete the calibration of the brake coefficient; the equipment parameters include but are not limited to the limit parameters such as the effective radius of the caliper and the cylinder area.

[0068] The brake coefficient calibration process is as follows: use a lift to put the vehicle in lifting mode, lightly step on the drive to rotate the vehicle drive shaft, and let the vehicle axle have a certain initial speed; then step on the brake pedal to slow down the vehicle, record the acceleration of the drive shaft (the acceleration of the drive shaft can be calculated by the motor speed, or an oscilloscope + resistance sensor can be used to calculate the acceleration of the drive shaft), record the brake pedal position, so the ESP is unplugged and there is no brake pressure message. The brake pressure can be calculated by using the brake pedal position and the previously marked pedal feel, or a sensor can be added to measure the brake pressure; record the master cylinder pressure sensor parameters, according to the previously calibrated shaft moment of inertia and shaft friction torque, the shaft braking torque at this time can be calculated by Newton's second law, and then the relationship between the shaft braking torque and the brake pressure can be obtained; using the caliper effective radius, cylinder area and other limit parameters, the friction coefficient of the friction plate can also be calculated, and the brake coefficient calibration is completed at this time.

[0069] For the center of mass position, the collected data of the center of mass position is obtained. When the vehicle does not have air springs, the vertical forces of the four wheels of the first vehicle in the collected data of the center of mass position are calculated to obtain the center of mass position, and the calibration of the center of mass position is completed. When the vehicle has air springs, the vertical forces of the four wheels of the second vehicle with different air spring heights are obtained from the collected data of the center of mass position, and each of the second vehicle's four wheel vertical forces is processed separately to obtain the center of mass position at different air spring heights, and the calibration of the center of mass position is completed.

[0070] It should be noted that when calibrating the center of mass position, when the vehicle has an empty spring, different center of mass positions need to be calibrated according to different empty spring heights. When the vehicle does not have an empty spring, the center of mass position only needs to be calibrated once.

[0071] The calibration process of the center of mass position is as follows: the vertical forces of the four wheels of the vehicle are measured on flat ground and on a ramp with a known slope. The center of mass position can be calculated based on the dynamic relationship. The parameters used to calculate the center of mass position need to be adjusted according to the change in the height of the empty spring.

[0072] Reference Figure 3 , which is an example diagram of calculating the centroid position provided in an embodiment of the present invention. The calculation of the centroid position has two parts, one part is to determine the plane position, and the other part is to determine the centroid height.

[0073] The formula used to determine the position of the plane is:

[0074] ;

[0075] , where 3.110 is the front and rear wheelbase of the vehicle. The front and rear wheelbases of different vehicles are different and should be changed according to actual applications. The unit of measurement can be m;

[0076] The formula used to determine the centroid height is:

[0077] As the center of rotation: ;

[0078] As the center of rotation: ;

[0079] Making a difference: ;

[0080] Among them, F1 is the vertical force of the front axle, F2 is the vertical force of the rear axle, is the distance from the center of the front axle to the center of mass of the vehicle, is the distance from the rear axle center to the vehicle mass center, is the friction force on the front axle, is the friction force on the rear axle, G is the gravity of the vehicle, and θ is the slope angle of the standard slope.

[0081] For the stability factor, the collected data of the stability factor is obtained, and the yaw rate measurement data and the yaw rate theoretical data in the collected data of the stability factor are fitted to obtain the stability factor, and the calibration of the stability factor is completed; it should be noted that when there are empty springs in the vehicle, the corresponding stability factor is calibrated according to the different empty spring heights, that is, the stability factor needs to be calibrated multiple times, and the specific content of the collected data is different for empty springs of different heights. When there are no empty springs in the vehicle, the stability factor is calibrated only once. Among them, the yaw rate measurement data can be understood as the data of the actual measured change of the yaw rate, and the yaw rate theoretical data is the data of the theoretical change of the yaw rate under ideal conditions.

[0082] When calibrating the stability factor, it is first necessary to confirm the source of the yaw rate. For example, the yaw rate can be obtained in the CAN network, or the yaw rate can be obtained by using an external sensor or an IMU sensor of the brake controller. On a high-adhesion road surface, the driver steers the wheel at a constant angular velocity at different vehicle speeds to perform steady-state steering and record the change of the yaw rate with the steering wheel angle. The stability factor K is calibrated based on the nominal value of the yaw rate (which can be understood as the yaw rate measurement data mentioned above) and the yaw rate measurement value (which can be understood as the yaw rate theoretical data mentioned above). For example, the stability factor K is fitted based on the calculation formula of matching the steady-state yaw rate measurement value with the nominal value of the yaw rate.

[0083] For the front and rear axle cornering stiffness, the collected data of the front and rear axle cornering stiffness is obtained, and the lateral speed measurement data, lateral speed theoretical data and current stability factor are obtained from the collected data of the front and rear axle cornering stiffness. The lateral speed measurement data and the lateral speed theoretical data are fitted to obtain the rear axle cornering stiffness, and the rear axle cornering stiffness and the current stability factor are processed to obtain the front axle cornering stiffness. The lateral speed measurement data is the measurement data of the lateral speed of the vehicle changing with the steering wheel angle when the vehicle turns the steering wheel at a constant angular speed for steady-state steering; the lateral speed theoretical data is the theoretical data of the lateral speed of the vehicle changing with the steering wheel angle when the vehicle performs steady-state steering.

[0084] When there are empty springs in the vehicle, it is necessary to calibrate different front and rear axle lateral stiffnesses according to different empty spring heights. When calibrating the front and rear axle lateral stiffnesses, the contents of the collected data for empty springs of different heights are different. When there are no empty springs in the vehicle, it is only necessary to calibrate the front and rear axle lateral stiffnesses once.

[0085] For example, for the front and rear axle cornering stiffness, the calibrated stability factor is first used, and different stability factors are used for air springs of different heights. Then, an optical vehicle speed sensor is installed on the side of the vehicle, which can collect the lateral speed of the vehicle in real time. On a high-adhesion road surface, the driver turns the steering wheel at a constant angular speed at different vehicle speeds to perform steady-state steering, and records the change of lateral speed with the steering wheel angle, so as to obtain the steady-state lateral speed value (which can be understood as the lateral speed measurement data mentioned above), match the nominal lateral speed value (which can be understood as the lateral speed theoretical data mentioned above), and then bring the nominal lateral speed value and the nominal lateral speed value into the calculation formula of the center of mass sideslip angle, so as to back-calculate and fit the rear axle cornering stiffness; then, using the stability factor and the rear axle cornering stiffness, the front axle cornering stiffness can be back-calculated and fitted.

[0086] S103: Determine the vehicle parameters with adaptive learning marks as target parameters.

[0087] In the method provided in an embodiment of the present invention, at least one of the wheel speed magnetic pole pair, the brake coefficient, the stability factor and the front and rear axle lateral stiffness has an adaptive learning mark. Therefore, the target parameters include at least one of the wheel speed magnetic pole pair, the brake coefficient, the stability factor and the front and rear axle lateral stiffness. Preferably, when the wheel speed magnetic pole pair, the brake coefficient, the stability factor and the front and rear axle lateral stiffness all have adaptive learning marks, the wheel speed magnetic pole pair, the brake coefficient, the stability factor and the front and rear axle lateral stiffness are all determined as target parameters.

[0088] S104: For each target parameter, a matching process is performed based on the adaptive learning logic of the target parameter, and when a matching result indicating that the parameter characterizing the vehicle is abnormal is obtained, a parameter abnormality control process is performed.

[0089] For the wheel speed pole pair number, the wheel pulse count data of each tire of the vehicle is obtained, and based on the wheel pulse count data, it is determined whether there is a spare tire among each tire. If there is a spare tire among each tire, a matching result is generated to indicate that there is an abnormality in the parameters of the vehicle. Then, the effective rolling radius of the spare tire is calculated, and the tire parameters of the vehicle are corrected based on the effective rolling radius of the spare tire to complete the parameter abnormality control processing.

[0090] After completing the wheel speed pole logarithm calibration, the tires can be matched with real-time parameters based on this, such as recording the wheel pulse counts issued by the brake controller in real time (which can be understood as the wheel pulse count data mentioned above), and checking the difference in the number of wheel pulse counts within the same time window. If it is identified that the wheel pulse count of a certain tire obviously does not match that of other wheels, for example, the difference between the wheel pulse count of a certain tire and that of other tires is greater than or equal to a threshold (the threshold can be set according to actual needs, for example, set to 2), then it can be determined that the tire is a spare tire.

[0091] For example, after determining the difference between the wheel pulse counts of the tires, if the difference between the wheel pulse count of a certain tire and the wheel pulse counts of other tires is greater than or equal to a threshold, and the tire pressure of the tire is lower than the preset tire pressure, the tire is determined to be a spare tire. Furthermore, the spare tire determined in this step can also be understood as a non-original factory tire of the vehicle.

[0092] Furthermore, after determining that the tire is a spare tire, the effective rolling radius of the spare tire can be determined, and the formula count A*radius A=count B*radius B is used to determine it, wherein A represents a wheel that is not a spare tire, radius A represents the effective rolling radius of wheel A, count A represents the wheel pulse count of wheel A, B represents the wheel of the spare tire, count B represents the wheel pulse count of the spare tire, and radius B represents the effective rolling radius of the spare tire; when count A, radius A, and count B are all known, radius B can be calculated, and then the calculated effective rolling radius can be updated to the vehicle parameters. Through a similar process, the wheels with lower tire pressure can also be calibrated to improve the accuracy of the vehicle speed estimation algorithm.

[0093] For the brake coefficient, the acceleration and master cylinder pressure values ​​of the vehicle are obtained in real time, the brake torque is determined based on the acceleration and master cylinder pressure values, and the longitudinal force of the vehicle is applied to check the brake torque to obtain the attenuation ratio of the brake coefficient. When the attenuation ratio matches the preset first warning condition, a matching result is generated to characterize the abnormality of the vehicle parameters, and a brake warning message is generated to complete the parameter abnormality control process. Exemplarily, when the attenuation ratio is greater than or equal to the preset warning ratio, the attenuation ratio matches the first warning condition, and the generated brake warning message is used to warn the driver that the friction pad is too worn, and the driver needs to drive carefully and replace the brake pad in time.

[0094] After completing the brake coefficient, the friction plate (which can be understood as the brake) can be matched with real-time parameters based on it. When it is identified that the vehicle is in a stable braking state and various active control functions are not triggered, the vehicle acceleration and the master cylinder pressure sensor value (i.e. the master cylinder pressure value mentioned above) are recorded in real time to calculate the braking torque. Since the deceleration term of the whole vehicle accounts for a much higher proportion of the longitudinal dynamics of the whole vehicle than the axle rotation deceleration (the rotational inertia of the axle and tire is very small compared to the mass of the whole vehicle), the product of the whole vehicle acceleration and the whole vehicle mass is the sum of the longitudinal forces. The braking torque value is checked using the longitudinal force to obtain the friction coefficient attenuation ratio of the friction plate (i.e. the attenuation ratio of the brake coefficient mentioned above). When the ratio is too high, an alarm is issued to remind the driver that the friction plate is excessively worn.

[0095] For the stability factor, when the vehicle is in a steady-state steering condition, the measured data and theoretical data of the vehicle's yaw rate are obtained, and the measured data and theoretical data of the yaw rate are fitted to obtain a fitting value. When the fitting value matches the preset stability factor update condition, a matching result is generated to characterize the abnormality of the vehicle's parameters, and the value of the stability factor is updated based on the fitting value to complete the parameter abnormality control process. It should be noted that when the fitting value is within the preset credible range, it is determined that the fitting value matches the preset stability factor update condition, and the fitting value is updated as the new stability factor value to the vehicle's parameters.

[0096] After the calibration of the stability factor is completed, the parameters of the stability factor can be matched in real time, such as judging whether the vehicle is in a steady-state steering condition in real time through high road adhesion, vehicle speed, and steering wheel angle, and within the time window after the steady-state steering condition is identified, the yaw rate sensor value is used to read the measurement data of the yaw rate, and the theoretical data of the yaw rate is obtained in real time. The measurement data of the yaw rate is obtained by measuring the sensor, and the theoretical data of the yaw rate is the data of the theoretical change of the yaw rate; the theoretical data of the yaw rate and the measurement data are fitted and matched to obtain a new stability factor. If the new stability factor is within a credible range, the new stability factor is used to update the stability factor in the vehicle control software.

[0097] When the target parameter is the front and rear axle cornering stiffness, the cornering stiffness change rate of each tire of the vehicle is determined, and when there is a match with the preset second warning condition in each cornering stiffness change rate, a matching result indicating that the vehicle parameter is abnormal is generated, and vehicle warning information is generated to complete the parameter abnormality control process. Exemplarily, when the cornering stiffness change rate is a negative value, the cornering stiffness change rate matches the second warning condition.

[0098] Furthermore, after completing the calibration of the front and rear axle cornering stiffness, the cornering stiffness parameters can be fine-tuned and matched in real time in conjunction with the change in the stability factor. Specifically, for example, it is determined in real time whether the vehicle is in a steady-state steering condition (determined by high road adhesion, stable speed, and small steering wheel angle input). If a steady-state steering condition is identified, the nominal value of the yaw rate and the yaw rate value are fitted and matched to obtain a new stability factor K. This method assumes that the wear of the front and rear wheels will not be too different. Assuming that after the wheels are worn, the cornering stiffness change rate of each tire is α, the change rate of the wheel cornering stiffness can be calculated by substituting it into the stability factor calculation formula. When the change rate is a negative value, it means that the steering characteristics of the vehicle have changed from understeering to oversteering due to tire wear or replacement. At this time, the system issues a vehicle warning message to remind the driver that the vehicle tires are excessively worn or the vehicle has been unreasonably modified, and the vehicle needs to be repaired in time. By issuing a warning to the driver, the driver can avoid danger in time.

[0099] For example, the relationship between the stability factor, the yaw rate, and the steady-state center of mass deviation angle is shown in the following formula:

[0100] ;

[0101] ;

[0102] in, is the distance from the front axle center to the center of mass, is the distance from the rear axle center to the center of mass; K is the stability factor, L is the front and rear wheelbase, and The longitudinal speed. is the rear axle cornering stiffness, beta is the center of mass side slip angle, is the lateral speed, and nom represents the nominal value.

[0103] The embodiment of the present invention determines the various vehicle parameters of the vehicle; for each vehicle parameter, the collected data of the vehicle parameter is obtained, and the vehicle parameter is calibrated based on the collected data of the vehicle parameter; the vehicle parameter with an adaptive learning mark is determined as the target parameter; for each target parameter, matching processing is performed based on the adaptive learning logic of the target parameter, and when the matching result that the parameter characterizing the vehicle is abnormal is obtained, parameter abnormality control processing is performed. After matching processing is performed on the vehicle parameters with adaptive learning marks, when the matching result that the parameter characterizing the vehicle is abnormal is obtained, parameter abnormality control processing is performed to reduce the risks caused by parameter abnormalities and reduce the risks of drivers.

[0104] The vehicle whole vehicle parameter calibration / matching method proposed in the present invention is simple to implement, has self-learning logic for core variable parameters, can identify the impact of tire wear / replacement on parameters, and make corrections through self-learning logic, thereby achieving better matching of the braking system; for some modified vehicles, the stability margin of the braking system matching is improved to reduce driving risks.

[0105] The method provided by the present invention is mainly aimed at the wire control brake system or the chassis domain controller. The present invention is simple to implement, has self-learning logic for the core variable parameters, can identify the wear / replacement of the tire and correct the parameters, and has strong matching performance; the implementation of the whole method is independent of other inputs, has self-checking, and is easier to control risks.

[0106] and Figure 1 Corresponding to the method shown in the figure, the present invention also provides a vehicle parameter processing device, which is used to support Figure 1 The specific implementation of the method shown in FIG. 1 is a schematic diagram of the structure of the device. Figure 4 The specific instructions are as follows:

[0107] The first determination unit 401 is used to determine various vehicle parameters of the vehicle;

[0108] An acquisition unit 402 is used to acquire, for each of the vehicle parameters, collected data of the vehicle parameter, and calibrate the vehicle parameter based on the collected data of the vehicle parameter;

[0109] A second determining unit 403 is used to determine the vehicle parameters with adaptive learning marks as target parameters;

[0110] The matching unit 404 is used to perform matching processing on each of the target parameters based on the adaptive learning logic of the target parameters, and perform parameter abnormality control processing when a matching result indicating that the parameters of the vehicle are abnormal is obtained.

[0111] The embodiment of the present invention determines the various vehicle parameters of the vehicle; for each vehicle parameter, the collected data of the vehicle parameter is obtained, and the vehicle parameter is calibrated based on the collected data of the vehicle parameter; the vehicle parameter with an adaptive learning mark is determined as the target parameter; for each target parameter, matching processing is performed based on the adaptive learning logic of the target parameter, and when the matching result that the parameter characterizing the vehicle is abnormal is obtained, parameter abnormality control processing is performed. After matching processing is performed on the vehicle parameters with adaptive learning marks, when the matching result that the parameter characterizing the vehicle is abnormal is obtained, parameter abnormality control processing is performed to reduce the risks caused by parameter abnormalities and reduce the risks of drivers.

[0112] In another embodiment provided by the present invention, the vehicle parameters of the device include wheel speed magnetic pole pair number, shaft rotational inertia, shaft friction torque, brake coefficient, stability factor and front and rear axle lateral stiffness.

[0113] In another embodiment provided by the present invention, the acquisition unit 402 of the device executes a process of acquiring the collected data of the whole vehicle parameter for each of the whole vehicle parameters, and calibrating the whole vehicle parameter based on the collected data of the whole vehicle parameter, including:

[0114] For the wheel speed magnetic pole pair number, the collected data of the wheel speed magnetic pole pair number is obtained. When the collected data is parameter data provided by the original equipment manufacturer of the vehicle, the parameter data is used to complete the calibration of the wheel speed magnetic pole pair number; if the collected data of the wheel speed magnetic pole pair number is data collected using a collection device, the wheel speed magnetic pole pair number of the vehicle is determined based on the waveform data in the collected data of the wheel speed magnetic pole pair number, and the calibration of the wheel speed magnetic pole pair number is completed; if the collected data of the wheel speed magnetic pole pair number is a message signal with a wheel pulse count, the wheel speed magnetic pole pair number of the vehicle is determined based on the wheel pulse count in the message signal and the effective wheel radius of the vehicle, and the calibration of the wheel speed magnetic pole pair number is completed;

[0115] For the shaft moment of inertia, acquiring the collected data of the wheel moment of inertia, acquiring the drive shaft data and the non-drive shaft data from the collected data of the shaft moment of inertia, processing the drive shaft data to calibrate the shaft moment of inertia of the drive shaft, and processing the non-drive shaft data to calibrate the shaft moment of inertia of the non-drive shaft;

[0116] For the shaft friction torque, acquiring collected data of the shaft friction torque, acquiring driving shaft friction data and non-driving shaft friction data from the collected data of the shaft friction torque, processing the driving shaft friction data to calibrate the shaft friction torque of the driving shaft, and processing the non-driving shaft friction data to calibrate the shaft friction torque of the non-driving shaft;

[0117] For the brake coefficient, acquiring the collected data of the brake coefficient, processing the shaft rotation inertia, the shaft friction torque, and the master cylinder pressure sensor coefficient and equipment parameters in the collected data of the brake coefficient, obtaining the friction coefficient of the brake, and completing the calibration of the brake coefficient;

[0118] For the stability factor, acquiring collected data of the stability factor, fitting the yaw angular velocity measurement data and the yaw angular velocity theoretical data in the collected data of the stability factor to obtain the stability factor, and completing the calibration of the stability factor;

[0119] For the front and rear axle lateral stiffness, the collected data of the front and rear axle lateral stiffness are obtained, the lateral speed measurement data, the lateral speed theoretical data and the current stability factor are obtained from the collected data of the front and rear axle lateral stiffness, the lateral speed measurement data and the lateral speed theoretical data are fitted to obtain the rear axle lateral stiffness, and the rear axle lateral stiffness and the current stability factor are processed to obtain the front axle lateral stiffness.

[0120] In another embodiment provided by the present invention, the target parameter of the device includes at least one of the wheel speed magnetic pole pair number, the brake coefficient, the stability factor, and the front and rear axle lateral stiffness; the matching unit 404 of the device performs matching processing based on the adaptive learning logic of the target parameter for each of the target parameters, and when a matching result indicating that the parameter of the vehicle is abnormal is obtained, performs parameter abnormality control processing, including:

[0121] When the target parameter includes the number of wheel speed magnetic pole pairs, the wheel pulse count data of each tire of the vehicle is obtained, and based on each of the wheel pulse count data, it is determined whether there is a spare tire among the tires, and if there is a spare tire among the tires, a matching result indicating that the parameters of the vehicle are abnormal is generated, and then the effective rolling radius of the spare tire is calculated, and the tire parameters of the vehicle are corrected based on the effective rolling radius of the spare tire, so as to complete the parameter abnormality control processing;

[0122] When the target parameter includes a brake coefficient, the acceleration and the master cylinder pressure value of the vehicle are acquired in real time, the brake torque is determined based on the acceleration and the master cylinder pressure value, and the longitudinal force of the vehicle is applied to check the brake torque to obtain an attenuation ratio of the brake coefficient. When the attenuation ratio matches a preset first alarm condition, a matching result indicating that the parameter of the vehicle is abnormal is generated, and brake alarm information is generated to complete parameter abnormality control processing;

[0123] When the target parameter includes a stability factor, when the vehicle is in a steady-state steering condition, measurement data and theoretical data of the yaw rate of the vehicle are obtained, the measurement data and theoretical data of the yaw rate are fitted to obtain a fitting value, and when the fitting value matches a preset stability factor update condition, a matching result characterizing that an abnormality exists in the parameter of the vehicle is generated, and the value of the stability factor is updated based on the fitting value, thereby completing parameter abnormality control processing;

[0124] When the target parameters include the lateral stiffness of the front and rear axles, the lateral stiffness change rate of each tire of the vehicle is determined. When there is a match with a preset second alarm condition in each of the lateral stiffness change rates, a matching result is generated to characterize that there is an abnormality in the parameters of the vehicle, and vehicle alarm information is generated to complete the parameter abnormality control processing.

[0125] In another embodiment provided by the present invention, the various vehicle parameters of the device also include a transmission system transmission ratio, a pedal curve, and a center of mass position.

[0126] In another embodiment provided by the present invention, the matching unit 404 of the device performs a process of acquiring the collected data of the whole vehicle parameter for each of the whole vehicle parameters, and calibrating the whole vehicle parameter based on the collected data of the whole vehicle parameter, including:

[0127] For the transmission system transmission ratio, acquiring the collected data of the transmission system transmission ratio, fitting the wheel speed signal and the motor speed signal in the collected data of the transmission system transmission ratio, obtaining the transmission system transmission ratio, and completing the calibration of the transmission system transmission ratio;

[0128] For the pedal curve, acquiring collected data of the pedal curve, fitting master cylinder pressure sensor information and brake pedal depth information in the collected data of the pedal curve to obtain the pedal curve, and completing calibration of the pedal curve;

[0129] For the center of mass position, the collected data of the center of mass position is obtained. When the vehicle does not have air springs, the four-wheel vertical forces of the first vehicle in the collected data of the center of mass position are calculated to obtain the center of mass position, and the calibration of the center of mass position is completed. When the vehicle has air springs, the four-wheel vertical forces of the second vehicle with different air spring heights are obtained from the collected data of the center of mass position, and each of the four-wheel vertical forces of the second vehicle is processed respectively to obtain the center of mass position at different air spring heights, and the calibration of the center of mass position is completed.

[0130] An embodiment of the present invention further provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned vehicle parameter processing method.

[0131] The embodiment of the present invention further provides an electronic device, the structural diagram of which is shown in FIG. Figure 5 As shown, it specifically includes a memory 601 and one or more instructions 602, wherein the one or more instructions 602 are stored in the memory 601 and are configured to be executed by one or more processors 603 to execute the one or more instructions 602 to execute the above-mentioned vehicle parameter processing method.

[0132] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0133] The specific implementation processes and derivative methods of the above-mentioned embodiments are all within the protection scope of the present invention.

[0134] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0135] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle parameter processing method, characterized in that: include: Determine the vehicle parameters; For each of the vehicle parameters, acquiring collected data of the vehicle parameter, and calibrating the vehicle parameter based on the collected data of the vehicle parameter; Determine the vehicle parameters with adaptive learning marks as target parameters; For each of the target parameters, matching processing is performed based on the adaptive learning logic of the target parameter, and when a matching result indicating that there is an abnormality in the parameters of the vehicle is obtained, parameter abnormality control processing is performed.

2. The method according to claim 1, characterized in that The various vehicle parameters include the number of wheel speed magnetic pole pairs, shaft rotational inertia, shaft friction torque, brake coefficient, stability factor, and front and rear axle lateral stiffness.

3. The method according to claim 2, characterized in that For each of the vehicle parameters, acquiring the collected data of the vehicle parameter, and calibrating the vehicle parameter based on the collected data of the vehicle parameter, comprises: Acquire the collected data of the wheel speed magnetic pole pair number, and when the collected data is parameter data provided by the original equipment manufacturer of the vehicle, use the parameter data to complete the calibration of the wheel speed magnetic pole pair number; if the collected data of the wheel speed magnetic pole pair number is data collected using a collection device, then based on the waveform data in the collected data of the wheel speed magnetic pole pair number, determine the wheel speed magnetic pole pair number of the vehicle, and complete the calibration of the wheel speed magnetic pole pair number; if the collected data of the wheel speed magnetic pole pair number is a message signal with a wheel pulse count, then based on the wheel pulse count in the message signal and the wheel effective radius of the vehicle, determine the wheel speed magnetic pole pair number of the vehicle, and complete the calibration of the wheel speed magnetic pole pair number.

4. The method according to claim 2, characterized in that: For each of the vehicle parameters, acquiring the collected data of the vehicle parameter, and calibrating the vehicle parameter based on the collected data of the vehicle parameter, comprises: For the shaft moment of inertia, acquiring the collected data of the wheel moment of inertia, acquiring the drive shaft data and the non-drive shaft data from the collected data of the shaft moment of inertia, processing the drive shaft data to calibrate the shaft moment of inertia of the drive shaft, and processing the non-drive shaft data to calibrate the shaft moment of inertia of the non-drive shaft; For the shaft friction torque, acquiring collected data of the shaft friction torque, acquiring driving shaft friction data and non-driving shaft friction data from the collected data of the shaft friction torque, processing the driving shaft friction data to calibrate the shaft friction torque of the driving shaft, and processing the non-driving shaft friction data to calibrate the shaft friction torque of the non-driving shaft; For the brake coefficient, acquiring the collected data of the brake coefficient, processing the shaft rotation inertia, the shaft friction torque, and the master cylinder pressure sensor coefficient and equipment parameters in the collected data of the brake coefficient, obtaining the friction coefficient of the brake, and completing the calibration of the brake coefficient; For the stability factor, acquiring collected data of the stability factor, fitting the yaw angular velocity measurement data and the yaw angular velocity theoretical data in the collected data of the stability factor to obtain the stability factor, and completing the calibration of the stability factor; For the front and rear axle lateral stiffness, the collected data of the front and rear axle lateral stiffness are obtained, the lateral speed measurement data, the lateral speed theoretical data and the current stability factor are obtained from the collected data of the front and rear axle lateral stiffness, the lateral speed measurement data and the lateral speed theoretical data are fitted to obtain the rear axle lateral stiffness, and the rear axle lateral stiffness and the current stability factor are processed to obtain the front axle lateral stiffness.

5. The method according to claim 1, characterized in that The target parameter includes at least one of the wheel speed magnetic pole pair number, the brake coefficient, the stability factor, and the front and rear axle lateral stiffness; for each of the target parameters, matching processing is performed based on the adaptive learning logic of the target parameter, and when a matching result indicating that the parameter of the vehicle is abnormal is obtained, parameter abnormality control processing is performed, including: When the target parameter includes the number of wheel speed magnetic pole pairs, the wheel pulse count data of each tire of the vehicle is obtained, and based on each of the wheel pulse count data, it is determined whether there is a spare tire among the tires, and if there is a spare tire among the tires, a matching result indicating that the parameters of the vehicle are abnormal is generated, and then the effective rolling radius of the spare tire is calculated, and the tire parameters of the vehicle are corrected based on the effective rolling radius of the spare tire, so as to complete the parameter abnormality control processing; When the target parameter includes a brake coefficient, the acceleration and the master cylinder pressure value of the vehicle are acquired in real time, the brake torque is determined based on the acceleration and the master cylinder pressure value, and the longitudinal force of the vehicle is applied to check the brake torque to obtain an attenuation ratio of the brake coefficient. When the attenuation ratio matches a preset first alarm condition, a matching result indicating that the parameter of the vehicle is abnormal is generated, and brake alarm information is generated to complete parameter abnormality control processing; When the target parameter includes a stability factor, when the vehicle is in a steady-state steering condition, measurement data and theoretical data of the yaw rate of the vehicle are obtained, the measurement data and theoretical data of the yaw rate are fitted to obtain a fitting value, and when the fitting value matches a preset stability factor update condition, a matching result characterizing that an abnormality exists in the parameter of the vehicle is generated, and the value of the stability factor is updated based on the fitting value, thereby completing parameter abnormality control processing; When the target parameters include the lateral stiffness of the front and rear axles, the lateral stiffness change rate of each tire of the vehicle is determined. When there is a match with a preset second alarm condition in each of the lateral stiffness change rates, a matching result is generated to characterize that there is an abnormality in the parameters of the vehicle, and vehicle alarm information is generated to complete the parameter abnormality control processing.

6. The method according to claim 2, characterized in that The various vehicle parameters also include drivetrain ratio, pedal curve and center of mass position.

7. The method according to claim 6, characterized in that For each of the vehicle parameters, acquiring the collected data of the vehicle parameter, and calibrating the vehicle parameter based on the collected data of the vehicle parameter, comprises: For the transmission system transmission ratio, acquiring the collected data of the transmission system transmission ratio, fitting the wheel speed signal and the motor speed signal in the collected data of the transmission system transmission ratio, obtaining the transmission system transmission ratio, and completing the calibration of the transmission system transmission ratio; For the pedal curve, acquiring collected data of the pedal curve, fitting master cylinder pressure sensor information and brake pedal depth information in the collected data of the pedal curve to obtain the pedal curve, and completing calibration of the pedal curve; For the center of mass position, the collected data of the center of mass position is obtained. When the vehicle does not have air springs, the four-wheel vertical forces of the first vehicle in the collected data of the center of mass position are calculated to obtain the center of mass position, and the calibration of the center of mass position is completed. When the vehicle has air springs, the four-wheel vertical forces of the second vehicle with different air spring heights are obtained from the collected data of the center of mass position, and each of the four-wheel vertical forces of the second vehicle is processed respectively to obtain the center of mass position at different air spring heights, and the calibration of the center of mass position is completed.

8. A vehicle parameter processing device, characterized in that: include: A first determination unit, used to determine various vehicle parameters of the vehicle; An acquisition unit, configured to acquire, for each of the vehicle parameters, collected data of the vehicle parameter, and calibrate the vehicle parameter based on the collected data of the vehicle parameter; A second determination unit is used to determine the vehicle parameters with adaptive learning marks as target parameters; The matching unit is used to perform matching processing on each of the target parameters based on the adaptive learning logic of the target parameters, and to perform parameter abnormality control processing when a matching result indicating that the parameters of the vehicle are abnormal is obtained.

9. A storage medium, characterized in that: The storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the vehicle parameter processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to execute the vehicle parameter processing method as described in any one of claims 1-7.

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