Tire rolling radius estimation method, electronic device, vehicle, and storage medium

Abstract

The application provides a tire rolling radius estimation method, an electronic device, a vehicle and a storage medium, and applies to the technical field of vehicle control. The method comprises the following steps: acquiring a wheel speed signal and a GPS vehicle speed signal of the vehicle; applying a recursive least square method, determining a tire rolling radius error coefficient of the vehicle based on the wheel speed signal and the GPS vehicle speed signal, the tire rolling radius error coefficient representing an error degree between a tire effective radius and a tire rolling radius; dynamically determining a corresponding target forgetting factor according to an estimated average residual error generated in a calculation process of the recursive least square method, the target forgetting factor being used for eliminating an influence of data saturation on tire rolling radius estimation; and determining a tire rolling radius estimation value at a current time according to the rolling radius error coefficient, the tire effective radius, a tire rolling radius estimation value at a previous time and the target forgetting factor. The method improves tire rolling radius estimation precision, so as to meet reliability and safety requirements of a vehicle control system.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a tire rolling radius estimation method, electronic equipment, vehicle, and storage medium. Background Technology

[0002] As a key parameter in vehicle dynamics and control systems, the accuracy of tire rolling radius directly affects the accuracy of vehicle speed estimation, slip ratio estimation, driving stability, and tire condition monitoring performance. Therefore, accurate estimation of tire rolling radius is crucial.

[0003] In related technologies, the estimation of tire rolling radius typically includes the following two methods:

[0004] The first approach involves estimating the tire rolling radius based on a geometric model of the tire, or using direct measurement. However, in practical applications, factors such as tire wear, changes in road surface adhesion conditions, and fluctuations in inflation pressure can significantly interfere with the estimation results of the tire rolling radius, leading to a decrease in estimation accuracy. Especially under high-speed driving or complex road conditions, tire deformation and slippage can cause the actual tire rolling radius to deviate from the theoretical value, thereby affecting vehicle control performance.

[0005] The second approach involves establishing a tire dynamics model and using the least squares method to dynamically estimate the tire rolling radius error coefficient based on the vehicle's wheel speed signal and GPS speed signal. Then, the tire rolling radius is estimated based on the preset effective tire radius and the tire rolling radius error coefficient. However, the estimation accuracy is still relatively low.

[0006] Therefore, there is an urgent need for an effective tire rolling radius estimation scheme to improve estimation accuracy and better meet the reliability and safety requirements of vehicle control systems. Summary of the Invention

[0007] This application provides a tire rolling radius estimation method, electronic device, vehicle, and storage medium to improve the accuracy of tire rolling radius estimation, thereby better meeting the reliability and safety requirements of vehicle control systems.

[0008] In a first aspect, this application provides a method for estimating the rolling radius of a tire, including:

[0009] Acquire vehicle wheel speed signals and GPS vehicle speed signals;

[0010] By applying the recursive least squares method, the tire rolling radius error coefficient of the vehicle is determined based on wheel speed signals and GPS vehicle speed signals. The tire rolling radius error coefficient characterizes the degree of error between the effective tire radius and the tire rolling radius.

[0011] Based on the estimated average residual generated during the recursive least squares calculation, the corresponding target forgetting factor is dynamically determined. The target forgetting factor is used to eliminate the impact of data saturation on tire rolling radius estimation.

[0012] The estimated value of the tire rolling radius at the current moment is determined based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

[0013] In one possible implementation, the estimated tire rolling radius at the current moment is determined based on the rolling radius error coefficient, the effective tire radius, the previous tire rolling radius estimate, and the target forgetting factor, including:

[0014] The product of the effective tire radius and the rolling radius error coefficient is used as the first value;

[0015] Determine the difference between the first value and the estimated tire rolling radius at the previous moment;

[0016] Determine the product of the target forgetting factor and the difference;

[0017] Add the product to the tire rolling radius estimate from the previous time step to obtain the tire rolling radius estimate for the current time step.

[0018] In one possible implementation, the corresponding target forgetting factor is dynamically determined based on the estimated average residual generated during the recursive least squares calculation, including:

[0019] Based on the pre-calibrated mapping relationship between the estimated average residual and the forgetting factor, the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process is determined.

[0020] In one possible implementation, the effective radius of the tire is determined as follows:

[0021] Obtain the vehicle's static tire radius and undeformed tire radius. The static tire radius is used to dynamically constrain the lower limit of the tire rolling radius, and the undeformed tire radius is used to dynamically constrain the upper limit of the tire rolling radius.

[0022] The effective tire radius of a vehicle is determined based on the static tire radius and the radius of the undeformed tire.

[0023] In one possible implementation, determining the effective tire radius of the vehicle based on the tire's static radius and the radius of the undeformed tire includes:

[0024] The second value is obtained by calculating the inverse cosine of the quotient of the static radius of the tire and the radius of the undeformed tire;

[0025] The second value is sine-calculated, and the product of the sine calculation result and the radius of the undeformed tire is used as the third value;

[0026] The quotient of the third value and the second value is determined as the effective radius of the vehicle's tires.

[0027] In one possible implementation, before determining the vehicle's tire rolling radius error coefficient, the method further includes:

[0028] Acquire vehicle status signals, including wheel speed signals, GPS vehicle speed signals, inertial measurement unit (IMU) signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals;

[0029] The status signal has passed the validity check.

[0030] In one possible implementation, the validity of the status signal is determined by the following method:

[0031] If the signal strength of the GPS vehicle speed signal is greater than the strength threshold and the positioning accuracy of the GPS vehicle speed signal is greater than the accuracy threshold, then the GPS vehicle speed signal is determined to pass the validity check.

[0032] If the triaxial acceleration, angular acceleration, and vehicle longitudinal acceleration estimated based on the IMU signal are all less than the corresponding threshold, then the IMU signal is determined to have passed the validity check.

[0033] If the vehicle is not detected to be in a low-speed condition, and the deviation between the vehicle speed corresponding to the GPS vehicle speed signal and the vehicle speed calculated based on the wheel speed signal is less than the deviation threshold, then the longitudinal vehicle speed signal is determined to have passed the validity check.

[0034] If the vehicle's Anti-lock Braking System (ABS), Electronic Stability Control (ESC), and Trailer Stability Assist System are all detected to be in active mode, then the activation signal of the assist function is determined to have passed the validity check.

[0035] If the vehicle's torque signal is detected to be valid, and the sum of the longitudinal forces of the axle calculated based on the torque signal is less than the longitudinal force threshold, then the axle torque signal is determined to have passed the validity check.

[0036] The status signal is considered valid if each signal in the status signal passes the validity check and the duration of the validity check is greater than the set anti-shake delay threshold.

[0037] In one possible implementation, the recursive least squares method is applied to determine the vehicle's tire rolling radius error coefficient based on wheel speed signals and GPS vehicle speed signals, including:

[0038] The wheel speed signal is low-pass filtered to obtain the filtered wheel speed signal.

[0039] The GPS vehicle speed signal is low-pass filtered to obtain the filtered GPS vehicle speed signal.

[0040] The filtered wheel speed signal and the filtered GPS vehicle speed signal are accumulated as valid samples. When the number of accumulated valid samples reaches a preset threshold, the recursive least squares method is applied to determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

[0041] Secondly, this application provides a tire rolling radius estimation device, comprising:

[0042] The acquisition module is used to acquire the vehicle's wheel speed signal and GPS vehicle speed signal;

[0043] The processing module is used to apply the recursive least squares method to determine the tire rolling radius error coefficient of the vehicle based on the wheel speed signal and the GPS vehicle speed signal. The tire rolling radius error coefficient characterizes the degree of error between the effective tire radius and the tire rolling radius.

[0044] The determination module is used to dynamically determine the corresponding target forgetting factor based on the estimated average residual generated during the recursive least squares calculation process. The target forgetting factor is used to eliminate the impact of data saturation on tire rolling radius estimation.

[0045] The determination module is also used to determine the estimated value of the tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

[0046] In one possible implementation, the determining module is specifically used to: take the product of the effective tire radius and the rolling radius error coefficient as a first value; determine the difference between the first value and the tire rolling radius estimate at the previous moment; determine the product of the target forgetting factor and the difference; and add the product to the tire rolling radius estimate at the previous moment to obtain the tire rolling radius estimate at the current moment.

[0047] In one possible implementation, the determining module is further configured to: determine the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process, based on a pre-calibrated mapping relationship between the estimated average residual and the forgetting factor.

[0048] In one possible implementation, the effective tire radius is determined by: obtaining the vehicle's static tire radius and the radius of the undeformed tire, wherein the static tire radius is used to dynamically constrain the lower limit of the tire rolling radius, and the radius of the undeformed tire radius is used to dynamically constrain the upper limit of the tire rolling radius; and determining the vehicle's effective tire radius based on the static tire radius and the radius of the undeformed tire radius.

[0049] In one possible implementation, determining the effective tire radius of a vehicle based on the static tire radius and the radius of the undeformed tire includes: performing an inverse cosine calculation on the quotient of the static tire radius and the radius of the undeformed tire to obtain a second value; performing a sine calculation on the second value, and multiplying the sine calculation result by the radius of the undeformed tire as a third value; and determining the quotient of the third value and the second value as the effective tire radius of the vehicle.

[0050] In one possible implementation, the determining module is further configured to: acquire vehicle status signals, including wheel speed signals, GPS vehicle speed signals, IMU signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals, before determining the tire rolling radius error coefficient of the vehicle; and determine that the status signals pass validity verification.

[0051] In one possible implementation, the validity of the status signal is determined as follows: if the signal strength of the GPS vehicle speed signal is greater than a strength threshold and the positioning accuracy of the GPS vehicle speed signal is greater than an accuracy threshold, then the GPS vehicle speed signal is determined to have passed the validity check; if the triaxial acceleration, angular acceleration, and longitudinal acceleration of the vehicle estimated based on the IMU signal in the IMU signal are all less than their corresponding thresholds, then the IMU signal is determined to have passed the validity check; if the vehicle is detected to be not in a low-speed condition, and the deviation between the vehicle speed corresponding to the GPS vehicle speed signal and the vehicle speed calculated based on the wheel speed signal is less than a deviation threshold, then the longitudinal vehicle speed signal is determined to have passed the validity check; if the vehicle's anti-lock braking system, electronic stability control system, and trailer stability assist system are all detected to be out of activation mode, then the assist function activation signal is determined to have passed the validity check; if the vehicle's torque signal is detected to be valid, and the sum of the longitudinal forces of the axles calculated based on the torque signal is less than a longitudinal force threshold, then the axle torque signal is determined to have passed the validity check; if each signal in the status signal passes the validity check, and the duration of passing the validity check is greater than the set anti-shake delay threshold, then the status signal is determined to have passed the validity check.

[0052] In one possible implementation, the processing module is specifically used to: perform low-pass filtering on the wheel speed signal to obtain a filtered wheel speed signal; perform low-pass filtering on the GPS vehicle speed signal to obtain a filtered GPS vehicle speed signal; accumulate the filtered wheel speed signal and the filtered GPS vehicle speed signal as valid samples, and when the number of accumulated valid samples reaches a preset threshold, apply the recursive least squares method to determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

[0053] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0054] The memory stores the instructions that the computer executes;

[0055] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0056] Fourthly, this application provides a vehicle, including a vehicle body and electronic equipment of the third aspect.

[0057] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0058] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0059] This application provides a tire rolling radius estimation method, electronic device, vehicle, and storage medium. It acquires the vehicle's wheel speed signal and GPS vehicle speed signal; applies recursive least squares method to determine the tire rolling radius error coefficient based on the wheel speed signal and GPS vehicle speed signal. The tire rolling radius error coefficient characterizes the degree of error between the effective tire radius and the tire rolling radius; dynamically determines the corresponding target forgetting factor based on the estimated average residual generated during the recursive least squares calculation process. The target forgetting factor is used to eliminate the influence of data saturation on tire rolling radius estimation; and determines the current tire rolling radius estimate based on the rolling radius error coefficient, the effective tire radius, the previous tire rolling radius estimate, and the target forgetting factor. In the online tire rolling radius estimation process, by introducing and dynamically adjusting the target forgetting factor, the real-time performance, accuracy, and robustness of the estimation are effectively improved. Specifically, by introducing a target forgetting factor and dynamically adjusting its size, the data saturation problem caused by the accumulation of historical data in the traditional recursive least squares method is effectively improved. This allows for a more sensitive response to the time-varying characteristics of vehicle driving conditions and tire states, significantly reducing estimation lag caused by excessive historical data inertia, and achieving fast and adaptive tracking of the tire rolling radius. Furthermore, using the estimated average residual as the basis for the dynamic target forgetting factor effectively suppresses the impact of outliers and abrupt changes on the tire rolling radius estimation results, improving the accuracy and anti-interference capability of tire rolling radius estimation. Moreover, after obtaining the rolling radius error coefficient, it is not directly used to calculate the tire rolling radius. Instead, it is combined with the tire rolling radius estimate from the previous moment and updated using the target forgetting factor through smoothing filtering. This effectively improves the jitter and divergence of the estimation results, enabling the output of stable, reliable, and smooth tire rolling radius estimates even under complex and dynamic driving conditions. This provides reliable state support for the vehicle control system and better meets the reliability and safety requirements of the vehicle control system. Attached Figure Description

[0060] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0061] Figure 1 A schematic flowchart illustrating the tire rolling radius estimation method provided in this application embodiment;

[0062] Figure 2 A schematic diagram of the tire radius provided in the embodiments of this application;

[0063] Figure 3 A schematic flowchart of a tire rolling radius estimation method provided in another embodiment of this application;

[0064] Figure 4 This application provides a tire rolling radius estimation architecture for its embodiments.

[0065] Figure 5 This is a schematic diagram of the tire rolling radius estimation device provided in the embodiments of this application;

[0066] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0067] Figure 7 This is a schematic diagram of the vehicle structure provided in an embodiment of this application.

[0068] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0070] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0071] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0072] In related technologies, parameter estimation methods based on the least squares method are widely used in tire characteristic analysis to improve the accuracy and robustness of tire rolling radius estimation. Specifically, based on the vehicle's wheel speed signal and GPS vehicle speed signal, the least squares method is used to dynamically estimate the tire rolling radius error coefficient. Then, the product of the preset effective tire radius and the tire rolling radius error coefficient is determined as the tire rolling radius. Although the least squares method can achieve real-time updating and dynamic tracking of the tire rolling radius error coefficient and adapt to the time-varying characteristics of tire parameters, this related technology does not dynamically evaluate the sample residuals. When the cumulative number of samples is insufficient or there is abnormal data, it will lead to a large deviation in the estimation results. The lack of a forgetting factor to eliminate the influence of historical data on the current estimation results in the algorithm being unable to adapt to the time-varying characteristics of tire conditions after long-term data saturation, thus leading to poor tire rolling radius estimation accuracy.

[0073] To address the aforementioned technical issues, the tire rolling radius estimation scheme provided in this application fully considers the impact of the estimated average residual in the least squares method on the tire rolling radius estimation results. Within the least squares estimation framework, a forgetting factor dynamically adjusted by the estimated average residual is introduced. By dynamically evaluating the estimated average residual of the samples and adjusting the forgetting factor, the data saturation problem is improved, enabling rapid tracking of the tire's time-varying characteristics. Furthermore, the forgetting factor is used for smoothing filtering updates, effectively improving the jitter and divergence of the tire rolling radius estimation results, thereby enhancing the accuracy and anti-interference capability of tire rolling radius estimation.

[0074] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0075] Figure 1 This is a schematic flowchart illustrating the tire rolling radius estimation method provided in this embodiment. The tire rolling radius estimation method in this embodiment can be executed by software and / or hardware devices. For example, the hardware device can be a tire rolling radius estimation device, which can be an electronic device or a processing chip within an electronic device. The electronic device is, for example, a controller in a vehicle.

[0076] It should be noted that during vehicle operation, since the slip ratio of the driven wheels relative to the ground is usually smaller than that of the driving wheels, using the motion characteristic parameters of the driven wheels to estimate the tire rolling radius is more stable and accurate. For front-wheel drive vehicles, the motion characteristic parameters of the rear axle driven wheels are used to estimate the tire rolling radius, and this tire rolling radius is used as the reference for the tire rolling radius of the entire vehicle. For rear-wheel drive vehicles, the motion characteristic parameters of the front axle driven wheels are used to estimate the tire rolling radius, and this tire rolling radius is used as the reference for the tire rolling radius of the entire vehicle. For four-wheel drive vehicles, the motion characteristic parameters of any wheel can be selected to estimate the tire rolling radius. Therefore, in this embodiment, the tire rolling radius can be estimated by selecting the wheel motion characteristic parameters of the corresponding axle (target axle) according to the vehicle's drive type.

[0077] like Figure 1 As shown, the tire rolling radius estimation method includes:

[0078] S101. Acquire the vehicle's wheel speed signal and GPS vehicle speed signal.

[0079] For example, wheel speed signals can be acquired by wheel speed sensors mounted on the tires of a vehicle (e.g., for front-wheel drive vehicles, wheel speed signals are acquired by wheel speed sensors on the rear axle tires), and GPS vehicle speed signals can be calculated by a GPS receiver in the vehicle, which is typically integrated into a T-Box or smart antenna.

[0080] For example, the vehicle's controller synchronously reads wheel speed signals and GPS vehicle speed signals at a fixed frequency via the Controller Area Network (CAN) bus.

[0081] S102. Using the recursive least squares method, based on wheel speed signals and GPS vehicle speed signals, determine the tire rolling radius error coefficient of the vehicle. The tire rolling radius error coefficient characterizes the degree of error between the effective tire radius and the tire rolling radius.

[0082] The tire rolling radius error coefficient is defined as the ratio of the vehicle speed calculated based on wheel speed to the GPS vehicle speed. Taking a front-wheel-drive vehicle as an example, the tire rolling radius error coefficient is defined as the ratio of the rear axle vehicle speed to the GPS vehicle speed. Since the rear axle vehicle speed can be calculated from the effective tire radius and wheel speed, the tire rolling radius error coefficient... Also refers to the effective radius of the tire. and tire rolling radius The error coefficient between them is used to characterize the effective radius of the tire. With tire rolling radius The degree of error between them quantifies the effective tire radius caused by tire pressure, load, and wear. With tire rolling radius The deviation between them. Specifically, the tire rolling radius error coefficient. .

[0083] For example, in one implementation, the recursive least squares method maintains three recursive intermediate variables, represented as follows:

[0084]

[0085]

[0086]

[0087] In the formula, Vehicle speed calculated based on wheel speed The sum of squares; GPS vehicle speed The sum of squares; It is the sum of the product of GPS vehicle speed and the vehicle speed calculated based on wheel speed.

[0088] The tire rolling radius error coefficient was fitted using the recursive least squares method. , ,in, , represents the set of vehicle speeds calculated based on wheel speed; , representing the set of GPS vehicle speeds.

[0089] For example, in the current estimation period, the set of vehicle speeds calculated based on wheel speed and the set of GPS vehicle speeds are substituted into the aforementioned tire rolling radius error coefficient. The calculation formula yields the tire rolling radius error coefficient for the current estimated cycle. .

[0090] S103. Based on the estimated average residual generated during the recursive least squares calculation, dynamically determine the corresponding target forgetting factor. The target forgetting factor is used to eliminate the influence of data saturation on the tire rolling radius estimation.

[0091] The estimated average residual generated during the recursive least squares calculation is expressed as:

[0092]

[0093] In the formula, This refers to the cumulative estimated residuals generated during the recursive least squares calculation process. is the sample size, representing the size of the set of vehicle speeds calculated based on wheel speed and the set of GPS vehicle speeds. Where:

[0094]

[0095] The estimated average residual generated during the recursive least squares calculation process reflects, to some extent, the data quality and the degree of fit between the model (recursive least squares model). For example, when the estimated average residual is large, it indicates the possible presence of outliers or interference. The system should automatically increase the forgetting factor to strongly suppress the negative impact of outliers, signal jumps, and other abnormal data. When the estimated average residual is small, the system should appropriately decrease the forgetting factor, thereby achieving an adaptive adjustment mechanism and fundamentally improving the algorithm's anti-interference ability and estimation accuracy in complex real-world environments.

[0096] It is understandable that different estimated average residuals correspond to different forgetting factors. After obtaining the estimated average residuals generated during the recursive least squares calculation, the target forgetting factor corresponding to the estimated average residuals is calculated according to the preset algorithm.

[0097] The target forgetting factor is typically a dynamically adjustable parameter between 0 and 1, used to control the decay rate of historical data in recursive updates and eliminate the impact of data saturation on tire rolling radius estimation.

[0098] S104. Determine the estimated value of the tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

[0099] The tire rolling radius estimate for the previous moment represents the smoothed tire rolling radius value output from the previous sampling or estimation period.

[0100] In this step, the target forgetting factor is used as a smoothing gain to correct the variation of the tire rolling radius, eliminate the influence of data saturation on the tire rolling radius estimation, and effectively suppress the output jitter of the tire rolling radius caused by the fluctuation of the rolling radius error coefficient estimation.

[0101] For example, the estimated tire rolling radius at the current moment is calculated based on a pre-fitted calculation formula, whose independent variables are the rolling radius error coefficient, the effective tire radius, the estimated tire rolling radius at the previous moment, and the target forgetting factor.

[0102] For example, the rolling radius error coefficient, the effective tire radius, the previous tire rolling radius estimate, and the target forgetting factor are input into a pre-trained intelligent model. Using the mapping relationship between the rolling radius error coefficient, the effective tire radius, the previous tire rolling radius estimate, the target forgetting factor, and the current tire rolling radius estimate that the intelligent model has learned, the model automatically predicts the current tire rolling radius estimate.

[0103] This embodiment of the application, by introducing a target forgetting factor and dynamically adjusting its size, effectively improves the data saturation problem caused by the accumulation of historical data in the traditional recursive least squares method. This allows for a more sensitive response to the time-varying characteristics of vehicle driving conditions and tire states, significantly reducing estimation lag caused by excessive historical data inertia, and achieving fast, adaptive tracking of the tire rolling radius. Furthermore, using the estimated average residual as the basis for the dynamic target forgetting factor effectively suppresses the impact of outliers and abrupt changes on the tire rolling radius estimation results, improving the accuracy and anti-interference capability of tire rolling radius estimation. Further, after obtaining the rolling radius error coefficient, it is not directly used to calculate the tire rolling radius. Instead, it is combined with the tire rolling radius estimate from the previous moment and updated using the target forgetting factor through smoothing filtering. This effectively improves the jitter and divergence of the estimation results, enabling the output of stable, reliable, and smooth tire rolling radius estimates even under complex and dynamic driving conditions. This provides reliable state support for the vehicle control system and better meets the reliability and safety requirements of the vehicle control system.

[0104] Specifically, in some embodiments, determining the estimated tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated tire rolling radius at the previous moment, and the target forgetting factor includes: using the product of the effective tire radius and the rolling radius error coefficient as a first value; determining the difference between the first value and the estimated tire rolling radius at the previous moment; determining the product of the target forgetting factor and the difference; and adding the product to the estimated tire rolling radius at the previous moment to obtain the estimated tire rolling radius at the current moment.

[0105] For example, the estimated tire rolling radius at the current moment satisfies the following formula:

[0106]

[0107] In the formula, This is the estimated tire rolling radius at the current moment. This is the estimated tire rolling radius at the previous moment; The target forgetting factor; This represents the preliminary estimate of the tire rolling radius at the current moment.

[0108] In other words, in step S104, the [material] is not directly [distributed / processed]. Instead of using the final tire rolling radius estimate for the current moment, a first-order smoothing filter (exponential smoothing) is used to filter the initially calculated tire rolling radius estimate for the current moment, eliminating the influence of data saturation on the tire rolling radius estimate and effectively suppressing the output jitter of the tire rolling radius caused by the fluctuation of the rolling radius error coefficient estimation.

[0109] For example, suppose the initial estimate of the tire rolling radius at the current moment is... =0.307 meters, the estimated tire rolling radius at the previous moment. =0.305 meters, target forgetting factor =0.98, so the final calculated tire rolling radius estimate for the current moment is 0.30696 meters. This tire rolling radius estimate not only incorporates the new data trend, but also maintains the smoothness and stability of the output, avoiding the tire rolling radius estimate from jumping directly from 0.305 meters to 0.307 meters.

[0110] In this embodiment, a dynamic target forgetting factor is used as a smoothing gain. When the data quality is high and the estimated average residual is small, the target forgetting factor is large, enabling the output to quickly track the real change in the tire rolling radius. When the data is disturbed and the estimated average residual is large, the target forgetting factor is small, making the output more dependent on the smooth historical estimate, effectively filtering out noise, thereby achieving an adaptive balance between tracking capability and output stability.

[0111] In some embodiments, the target forgetting factor is dynamically determined based on the estimated average residual generated during the recursive least squares calculation process, including: determining the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process based on the pre-calibrated mapping relationship between the estimated average residual and the forgetting factor.

[0112] The mapping relationship between the estimated average residual and the forgetting factor can be established through extensive simulation and real vehicle testing.

[0113] For example, this mapping relationship is presented in the form of a mapping table or a fitted function.

[0114] In one implementation, the mapping table contains the correspondence between the estimated average residual interval and the values ​​of the forgetting factor. For example, when This indicates excellent numerical quality and corresponds to a high forgetting factor. ;when This indicates good numerical quality and corresponds to a forgetting factor. ;when This indicates that the numerical quality is generally poor, corresponding to a forgetting factor. ;when This indicates poor numerical quality and corresponds to a forgetting factor. The calculated estimated average residual is compared with the above threshold interval, and the corresponding target forgetting factor is obtained by looking up the table.

[0115] In another implementation, the mapping table contains multiple sets of correspondences between estimated average residuals and forgetting factor values. For example, Corresponding forgetting factor ; Corresponding forgetting factor ; Corresponding forgetting factor .

[0116] In another implementation, the estimated average residual and the forgetting factor have a fitting functional relationship. The estimated average residual generated during the recursive least squares calculation is substituted into the fitting function to calculate the target forgetting factor.

[0117] Considering that the actual rolling radius of a tire is affected by a combination of tire pressure, load, temperature and dynamic deformation, and is not a fixed parameter, if the effective tire radius is set as a static value, the accuracy of the tire rolling radius estimation algorithm in describing the actual physical state will be reduced, especially when tire pressure changes or load shifts drastically, which may cause the estimated value to deviate from its reasonable physical range.

[0118] Based on this, in some embodiments, the effective radius of the tire is dynamically determined in the following way:

[0119] Step 1.1: Obtain the vehicle's static tire radius and undeformed tire radius. The static tire radius is used to dynamically constrain the lower limit of the tire rolling radius, and the undeformed tire radius is used to dynamically constrain the upper limit of the tire rolling radius.

[0120] The undeformed tire radius reflects the theoretical maximum radius of the tire under the current actual tire pressure. It can be directly calculated from the tire specifications and corrected for by real-time tire pressure. Specifically, the undeformed tire radius can be calculated using the following formula:

[0121]

[0122] In the formula, The radius of the undeformed tire; The hub radius is obtained through pre-calibration or by looking up a table. This is the nominal width of the tire tread, obtained through pre-calibration or by looking up a table; The aspect ratio is determined through pre-calibration or table lookup; This is the tire pressure correction factor, obtained through pre-calibration or table lookup; The deviation between the actual tire pressure and the standard tire pressure can be obtained in real time through a tire pressure monitoring system.

[0123] The static radius of a tire reflects its load-bearing radius when it is stationary on a horizontal road surface under the current actual vertical load. It can be calculated based on the radius of the undeformed tire and the real-time vertical load acting on the tire using the following formula:

[0124]

[0125] In the formula, The static radius of the tire; For tire vertical stiffness; This refers to the real-time vertical load on the tire, i.e., the tire's vertical force.

[0126] During vehicle operation, the vertical load distribution of the tires shifts due to acceleration, deceleration, and steering. Therefore, the real-time vertical load of the tires can be calculated using a vehicle dynamics model. This model must include at least the following parameters: vehicle mass, longitudinal acceleration, lateral acceleration, road gradient, and vehicle center of gravity height. Specifically, the formula for calculating the real-time vertical load of the tires is as follows:

[0127]

[0128]

[0129]

[0130]

[0131] In the formula, The real-time vertical load on the vehicle's left front tire; The real-time vertical load on the right front tire of the vehicle; The real-time vertical load on the vehicle's left rear tire; The real-time vertical load on the right rear tire of the vehicle; For vehicle weight; It is the acceleration due to gravity; This is the distance from the front axle to the vehicle's center of gravity. This is the distance from the rear axle to the vehicle's center of gravity. This is the distance between the front and rear axles of the vehicle. This refers to the rear axle track width; This refers to the front axle track width; The height of the vehicle's center of gravity. For the longitudinal acceleration of the vehicle, For the lateral acceleration of the vehicle, This refers to the road slope.

[0132] It should be noted that, , , These are fixed parameters, for example, that can be read from the fixed parameter memory of the vehicle's electronic architecture; , For real-time signals, such as those directly acquired by an onboard inertial measurement unit; , , and These are slowly changing state parameters, for example, obtained by querying a pre-calibrated parameter table related to vehicle load status (such as fuel level, seat occupancy signal, etc.); For online parameter estimation, such as longitudinal acceleration measured by IMU and vehicle longitudinal motion acceleration calculated based on wheel speed signals, the parameters are estimated in real time by a state observer (such as a Kalman filter).

[0133] It should be understood that during vehicle operation, the static radius and the radius of the undeformed tire are dynamically calculated, and the static radius is used as the dynamic lower limit of the tire rolling radius estimate, while the radius of the undeformed tire is used as the dynamic upper limit of the tire rolling radius estimate.

[0134] Step 1.2: Determine the effective radius of the vehicle's tires based on the static radius and the radius of the undeformed tire.

[0135] It should be understood that the effective radius of a tire is the equivalent radius that the tire actually participates in when rolling, and the effective radius value should be between the static radius of the tire and the radius of the undeformed tire.

[0136] For example, based on the obtained static tire radius and undeformed tire radius, the static tire radius is regarded as the dynamic lower limit of the effective tire radius, and the undeformed tire radius is regarded as the dynamic upper limit of the effective tire radius.

[0137] In one implementation, the geometric mean of the tire's static radius and the radius of the undeformed tire is taken as the effective tire radius, expressed as:

[0138]

[0139] In another implementation, the arithmetic mean of the tire's static radius and the radius of the undeformed tire is taken as the effective tire radius, expressed as:

[0140]

[0141] This embodiment utilizes dynamically changing tire physical boundaries to achieve real-time adaptive constraint on the online estimated tire rolling radius, ensuring that the estimation result is within a physically reasonable range. Compared to related technologies that use a fixed threshold to constrain the tire rolling radius, which may lead to misjudgments due to changes in tire geometry under full load or no load conditions, this embodiment improves the physical reasonableness and robustness of the estimation result.

[0142] In some embodiments, the effective tire radius of the vehicle is determined based on the tire's static radius and the radius of the undeformed tire, including:

[0143] The second value is obtained by performing an inverse cosine calculation on the quotient of the static radius of the tire and the radius of the undeformed tire; the second value is then subjected to a sine calculation, and the product of the sine calculation result and the radius of the undeformed tire is taken as the third value; the quotient of the third value and the second value is determined as the effective radius of the vehicle's tire.

[0144] For example, Figure 2 This is a schematic diagram of the tire radius provided in the embodiments of this application. Figure 2 As shown in the geometric relationship, the effective radius of the tire is actually the equivalent radius corresponding to the arc length from the wheel center to the last point of the contact patch. Therefore, the derivation formula for the effective radius of the tire can be expressed as:

[0145]

[0146] In the formula, It is half the length of the tire contact patch along the vehicle's direction of travel, that is, the distance from the geometric center of the contact patch to its longitudinal edge; The angle between the line connecting the wheel center and the last point of the contact patch, and the radial (vertical) direction of the tire.

[0147] In this embodiment, by introducing a dynamic tire effective radius calculation method based on an accurate geometric model, the calculated tire effective radius is theoretically closer to the true effective radius of the tire in the rolling state, thereby improving the accuracy of subsequent tire rolling radius estimation.

[0148] Considering that the least squares method is sensitive to outliers in the input data during the calculation process, the estimation may diverge under the interference of outliers (such as measurement noise or outliers), which makes the tire rolling radius estimation result easy to deviate from the true value and difficult to meet the robustness requirements in actual engineering.

[0149] In complex vehicle driving environments, wheel speed signals may generate impulse noise due to uneven road surfaces, GPS vehicle speed signals are prone to jumps or short-term failures in tunnels, elevated roads, and other scenarios, while IMU signals may approach or exceed their range during rapid acceleration and deceleration. Once these uncontrollable anomalous data enter the estimation algorithm, they will be given the same weight as valid data, thus interfering with the parameter estimation process once or continuously. This leads to non-physical instantaneous jitter in the estimation results, or even deviations or divergence, seriously affecting the stability and reliability of the estimation system.

[0150] Furthermore, the vehicle's dynamic operating conditions and control system status can also disrupt the fundamental physical assumptions upon which tire rolling radius estimation relies. For example, when ABS or ESC systems are activated, significant tire slippage occurs, and the definitive geometric relationship between wheel speed and vehicle speed no longer holds. Under extremely low-speed creep conditions, the signal-to-noise ratio of sensor signals drops sharply, resulting in excessively high measurement errors. Blindly estimating tire rolling radius under such conditions will significantly reduce its accuracy and robustness.

[0151] Therefore, to ensure the accuracy and robustness of tire rolling radius estimation and prevent the estimation results from oscillating and diverging, in some embodiments, before determining the tire rolling radius error coefficient of the vehicle, the method further includes: acquiring the vehicle's state signals, including wheel speed signals, GPS vehicle speed signals, IMU signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals; and determining that the state signals pass the validity verification.

[0152] Among them, the state signal can be regarded as a set of multi-source information used to evaluate the current vehicle driving state and sensor health, and to determine whether it meets the basic assumptions of the tire rolling radius estimation model.

[0153] For example, the vehicle's controller synchronously reads wheel speed signals, GPS vehicle speed signals, inertial measurement unit (IMU) signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals at a fixed frequency via the CAN bus. Then, based on preset physical rules and logical conditions, it performs real-time parallel detection and comprehensive judgment on the state signals. Only when all physical rules and logical conditions are satisfied is the data at the current moment allowed to enter the subsequent estimation algorithm.

[0154] In this embodiment, a multi-dimensional signal validity collaborative verification mechanism is introduced to ensure that the tire rolling radius estimation process is initiated only when the vehicle state signal meets the validity conditions. By detecting the signal quality in multiple dimensions, the comprehensiveness of the signal quality judgment is improved, thereby suppressing abnormal data from participating in the recursive least squares estimation process, reducing the interference of abnormal data on the estimation results, effectively preventing estimation performance degradation, result divergence or deviation caused by data quality, and significantly improving the accuracy and robustness of tire rolling radius estimation.

[0155] Specifically, in some embodiments, the validity of the status signal is determined by the following method:

[0156] 1) If the signal strength of the GPS vehicle speed signal is greater than the strength threshold and the positioning accuracy of the GPS vehicle speed signal is greater than the accuracy threshold, then the GPS vehicle speed signal is determined to have passed the validity check.

[0157] For example, the controller reads the signal quality indicator output by the GPS receiver in the vehicle in real time. This signal quality indicator includes the positioning accuracy factor, the signal strength factor, and the speed calculation flag. When the positioning accuracy factor is detected to be greater than the accuracy threshold, the signal strength factor is detected to be greater than the strength threshold, and the speed calculation flag is detected to be valid, the GPS vehicle speed signal is determined to have passed the validity check; otherwise, the GPS vehicle speed signal is determined to be invalid, i.e., it has failed the validity check.

[0158] 2) If the triaxial acceleration, angular acceleration, and longitudinal acceleration of the vehicle estimated based on the IMU signal are all less than the corresponding threshold, then the IMU signal is determined to have passed the validity check.

[0159] For example, the system detects whether the absolute values ​​of the triaxial accelerations in the IMU signal are less than their corresponding first safety threshold, whether the absolute values ​​of the triaxial angular accelerations are less than their corresponding second safety threshold, and whether the absolute value of the vehicle's longitudinal acceleration, estimated based on the IMU signal and using a vehicle kinematics model, is less than its corresponding third safety threshold (e.g., 1 m / s²). 2 If all the above conditions are met, the IMU signal is determined to have passed the validity check; otherwise, the IMU signal is determined to have exceeded the limit, i.e., it has failed the validity check.

[0160] Understandably, the triaxial acceleration and angular acceleration in the IMU signal are the original signals, while the longitudinal acceleration of the vehicle estimated based on the IMU signal is the derived signal.

[0161] This step can effectively detect IMU sensor hardware failures, instantaneous saturation, and severe interference. By verifying the derived signal (vehicle longitudinal acceleration), it can be checked whether the vehicle longitudinal acceleration calculated by the model (such as removing gravity components and coordinate transformation) is too large even if the original signal does not exceed the limit. This indicates that there is abnormal drift, calibration error, or severe model mismatch (such as the vehicle being in a non-stable attitude).

[0162] 3) If the vehicle is not detected to be in a low-speed condition, and the deviation between the vehicle speed corresponding to the GPS vehicle speed signal and the vehicle speed calculated based on the wheel speed signal is less than the deviation threshold, then the longitudinal vehicle speed signal is determined to have passed the validity check.

[0163] For example, the preset low-speed condition corresponds to a vehicle speed of -10km / h to 10km / h. If the detected GPS vehicle speed is not within the -10km / h to 10km / h range, it is determined that the vehicle is not in a low-speed condition. Taking a front-wheel-drive vehicle as an example, the rear axle speed is obtained by multiplying the effective tire radius by the rear axle wheel speed. The deviation between the GPS vehicle speed and the rear axle speed is calculated. If the deviation is greater than the deviation threshold, it may be due to slight tire lock-up, causing an excessive deviation between the GPS vehicle speed and the speed calculated based on the wheel speed signal; or the vehicle is in a low-speed condition, indicating that the longitudinal speed signal exceeds the limit, i.e., the signal quality is not up to standard.

[0164] 4) If the vehicle's ABS system, ESC system, and trailer stability assist system are all detected to be out of activation mode, then the activation signal of the assist function is deemed to have passed the validity check.

[0165] It should be understood that when the ABS system is activated, it indicates that the wheels are in a state of extreme longitudinal slip, and the relationship between wheel speed and vehicle speed is severely distorted. When the ESC system is activated, it indicates that the vehicle is in a state of extreme lateral dynamics, and the wheels may experience involuntary forced slippage. When the trailer stability assist system is activated, it typically applies asymmetrical braking force to the tractor wheels through the ESC system, which also actively induces wheel slippage. The assist function activation signal can be regarded as an integrated logical state signal used to characterize whether the vehicle's chassis control system is performing active intervention, thereby disrupting the natural state of free rolling of the tires.

[0166] For example, the controller monitors and parses status messages from the ABS system, ECS function system, and trailer stability assist system in real time via the CAN bus. Each status message contains at least a specific status bit indicating the system's current operating mode. For instance, the specific status bit in the ABS system's status message can be 0 or 1, with 0 indicating ABS is not activated and 1 indicating ABS is activated; the same applies to the ECS system's status message, where 0 indicates ECS is not activated and 1 indicates ECS is activated; and similarly, the trailer stability assist system's status bit can be 0 or 1, with 0 indicating trailer stability assist is not activated and 1 indicating trailer stability assist is activated. If the specific status bits for the ABS system, ECS system, and trailer stability assist system all indicate that the function is not activated, then the activation signal for the assist function passes the validity check.

[0167] This step, through joint monitoring of the activation status of the ABS system, ESC system, and trailer stability assist system, ensures that the subsequent tire rolling radius estimation algorithm is executed only under the natural state of free rolling of the tires, fundamentally guaranteeing the correctness of the estimation process and the reliability of the output results.

[0168] 5) If the vehicle torque signal is detected to be valid, and the sum of the longitudinal forces of the axle calculated based on the torque signal is less than the longitudinal force threshold, then the axle torque signal is determined to have passed the validity check.

[0169] For example, taking a front-wheel-drive vehicle as an example, the validity of the rear axle torque signal is detected. For instance, the controller acquires the rear axle torque signal via the CAN bus and checks it, including whether the signal value (torque value) is within a preset reasonable physical range and whether the signal change rate exceeds a maximum change rate threshold. If the torque value is within the preset reasonable physical range and the signal change rate is less than the maximum change rate threshold, the rear axle torque signal is determined to be valid. Then, based on this valid rear axle torque signal, the total longitudinal force acting on the tire contact point and used for driving or braking the vehicle is calculated using a preset vehicle drivetrain model. For a front-wheel-drive vehicle, this is the sum of the longitudinal forces of the left and right rear wheels. The axle torque signal is deemed to have passed the validity check if and only if the rear axle torque signal itself is valid and the sum of the longitudinal forces on the rear axle is less than the longitudinal force threshold (e.g., 100N). The longitudinal force threshold can be calibrated based on real vehicle test data or simulation results.

[0170] Each signal in the status signal passes the validity check, that is, the execution results of steps 1) to 5) all indicate that the validity check has been passed, and the duration of the validity check is greater than the set anti-shake delay threshold, then the status signal is determined to have passed the validity check.

[0171] For example, if the execution results of steps 1) to 5) all indicate that the validity check has been passed within M consecutive sampling periods (e.g., M=5, corresponding to 50ms), then the status signal is determined to have passed the validity check.

[0172] The above-mentioned signal anti-shake mechanism effectively avoids instantaneous jitter interference.

[0173] In some embodiments, the recursive least squares method is applied to determine the tire rolling radius error coefficient of the vehicle based on wheel speed signals and GPS vehicle speed signals, including:

[0174] Step 2.1: Perform low-pass filtering on the wheel speed signal to obtain the filtered wheel speed signal.

[0175] Step 2.2: Perform low-pass filtering on the GPS vehicle speed signal to obtain the filtered GPS vehicle speed signal.

[0176] For example, digital filters, such as first-order inertial elements or Butterworth filters, are used to filter the wheel speed signal and the GPS vehicle speed signal respectively to suppress high-frequency noise and transient interference, thereby obtaining the filtered wheel speed signal and the filtered GPS vehicle speed signal.

[0177] For example, the process of filtering the wheel speed signal using a first-order low-pass filter can be represented as follows:

[0178]

[0179] In the formula, These are the filter coefficients, which can be obtained based on signal-noise spectrum analysis or vehicle dynamics characteristics calibration. This represents the wheel speed signal at the current moment. This is the filtered wheel speed signal.

[0180] It should be noted that the vehicle speed calculated based on the wheel speed signal can also be low-pass filtered. In other words, the amplitude of the wheel speed signal and the effective tire radius can be filtered first. The products are multiplied, and the result is the vehicle speed calculated based on the wheel speed signals. This vehicle speed is then subjected to low-pass filtering. For example, the rear axle wheel speed and the effective tire radius... The product of these values ​​is the rear axle speed. The rear axle speed is then low-pass filtered to obtain the filtered rear axle speed. .

[0181] For GPS vehicle speed signals, a similar first-order low-pass filter is used for filtering. The process of filtering GPS vehicle speed signals using a first-order low-pass filter is represented as follows:

[0182]

[0183] In the formula, These are the filter coefficients; This is the current GPS vehicle speed signal; This is the filtered GPS vehicle speed signal.

[0184] Filter coefficients With filter coefficients Same, or filter coefficients It was calibrated separately based on the GPS noise characteristics.

[0185] Step 2.3: Accumulate the filtered wheel speed signal and the filtered GPS vehicle speed signal as valid samples. When the number of accumulated valid samples reaches a preset threshold, apply the recursive least squares method to determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

[0186] The filtered data ( Samples are stored in the sample pool, and the system continuously maintains a count of the number of samples in the pool. Assuming the preset sample pool capacity is 100, a trigger threshold is set. Set to 50, after the vehicle cruises smoothly, the system collects 10 valid samples every millisecond. After about 5 milliseconds, 50 valid samples are accumulated in the sample pool, that is, the number of samples reaches the trigger threshold. At this time, based on the valid samples accumulated in the sample pool, a recursive least squares estimation is performed to obtain a more robust tire rolling radius error coefficient.

[0187] For example, using all valid samples in the sample pool, calculate the following three intermediate variables:

[0188]

[0189]

[0190]

[0191] In the formula, equal Multiply by the effective radius of the tire.

[0192] The tire rolling radius error coefficient was fitted using the recursive least squares method. :

[0193]

[0194] Optionally, a sliding update method can be used for updating the sample pool. For example, at the end of the current estimation period, the oldest p samples are automatically removed, and the newer samples are retained. Thereafter, new samples are continuously added, and when the trigger threshold is reached again... This method triggers the next recursive least squares estimation, enabling continuous estimation. Alternatively, a clear and reset approach can be used, for example, clearing the sample pool and setting the sample count to 0 at the end of the current estimation period, then restarting the accumulation process.

[0195] In this embodiment, by filtering the wheel speed signal and GPS vehicle speed signal, high-frequency noise and instantaneous interference are effectively suppressed, improving the input quality of subsequent estimation algorithms. Furthermore, recursive least squares estimation is performed based on a sufficient number of valid samples, enhancing the statistical robustness and anti-interference fault tolerance of the estimation process, and improving the reliability and stability of the tire rolling radius error coefficient.

[0196] Based on the above embodiments, by Figure 3 The method for estimating tire rolling radius is explained in detail. Figure 3 A schematic flowchart of a tire rolling radius estimation method provided in another embodiment of this application is shown below. Figure 3 As shown, the tire rolling radius estimation method includes:

[0197] S301, Obtain vehicle status signals.

[0198] The status signals include vehicle wheel speed signals, GPS vehicle speed signals, IMU signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals.

[0199] For example, the controller synchronously reads wheel speed signals, GPS vehicle speed signals, IMU signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals at a fixed frequency via the CAN bus.

[0200] S302. Determine whether the status signal has passed the validity check.

[0201] For example, within M consecutive sampling periods (e.g., M=5, corresponding to 50ms), by executing steps 1) to 5), the validity of each signal in the state signal is determined, and when all signals pass the validity check, the state signal is determined to have passed the validity check.

[0202] If any of the status signals fails the validity check; or if all signals pass the validity check but the number of continuous sampling periods is less than the set number of sampling periods, then the status signal is determined to have failed the validity check.

[0203] If the status signal passes the validity check, then execute S303;

[0204] If the status signal fails the validity check, then continue executing S301.

[0205] S303. Perform low-pass filtering on the wheel speed signal to obtain the filtered wheel speed signal; perform low-pass filtering on the GPS vehicle speed signal to obtain the filtered GPS vehicle speed signal.

[0206] For example, digital filters, such as first-order inertial elements or Butterworth filters, are used to filter the wheel speed signal and the GPS vehicle speed signal respectively to suppress high-frequency noise and transient interference, thereby obtaining the filtered wheel speed signal and the filtered GPS vehicle speed signal.

[0207] For details, please refer to steps 2.1 to 2.2.

[0208] S304. Accumulate the filtered wheel speed signal and the filtered GPS vehicle speed signal as valid samples, and when the number of accumulated valid samples reaches a preset threshold, determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

[0209] For details, please refer to step 2.3, which will not be repeated here.

[0210] For the calculation of the effective radius of the tire during this process, please refer to steps 1.1 to 1.2 in the above implementation, and... , The static radius of the tire. The radius of the undeformed tire. This is the effective radius of the tire.

[0211] S305. Based on the pre-calibrated mapping relationship between the estimated average residual and the forgetting factor, determine the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process.

[0212] The estimated average residual generated during the recursive least squares calculation is expressed as:

[0213]

[0214] In the formula, This refers to the cumulative estimated residuals generated during the recursive least squares calculation process. is the sample size, representing the size of the set of vehicle speeds calculated based on wheel speed and the set of GPS vehicle speeds. Where:

[0215]

[0216] The mapping relationship between the estimated average residual and the forgetting factor can be established through extensive simulation and real vehicle testing.

[0217] For example, this mapping relationship is presented in the form of a mapping table or a fitted function.

[0218] In one implementation, the mapping table contains the correspondence between the estimated average residual interval and the values ​​of the forgetting factor. For example, when This indicates excellent numerical quality and corresponds to a high forgetting factor. ;when This indicates good numerical quality and corresponds to a forgetting factor. ;when This indicates that the numerical quality is generally poor, corresponding to a forgetting factor. ;when This indicates poor numerical quality and corresponds to a forgetting factor. The calculated estimated average residual is compared with the above threshold interval, and the corresponding target forgetting factor is obtained by looking up the table.

[0219] In another implementation, the mapping table contains multiple sets of correspondences between estimated average residuals and forgetting factor values. For example, Corresponding forgetting factor ; Corresponding forgetting factor ; Corresponding forgetting factor .

[0220] In another implementation, the estimated average residual and the forgetting factor have a fitting functional relationship. For example, it can be a linear or nonlinear function. The estimated average residual generated during the recursive least squares calculation is substituted into the fitted function to calculate the target forgetting factor.

[0221] S306. Determine the estimated value of the tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

[0222] For example, this step is achieved using the following formula:

[0223]

[0224] In the formula, This is the estimated tire rolling radius at the current moment. This is the estimated tire rolling radius at the previous moment; The target forgetting factor; This represents the preliminary estimate of the tire rolling radius at the current moment.

[0225] Next, through Figure 4 The tire rolling radius estimation architecture of this application embodiment is illustrated below.

[0226] Figure 4 The tire rolling radius estimation architecture provided in this application embodiment is... Figure 4 For front-wheel drive vehicles, it should be understood that the embodiments of this application are applicable to the estimation of tire rolling radius for all vehicle models.

[0227] like Figure 4 As shown, in this tire rolling radius estimation architecture, the first step is to determine whether the status signal passes the validity check based on GPS vehicle speed signal, IMU signal, longitudinal vehicle speed signal, auxiliary function activation signal, and rear axle torque signal. Figure 4 The validity verification shown specifically includes: signal quality judgment and anti-shake time judgment. Signal quality judgment can be found in steps 1) to 5) of the above embodiments; anti-shake time judgment involves determining whether the execution results of steps 1) to 5) all indicate passing the validity check, and whether the duration of the validity check is greater than the set anti-shake delay threshold. If the duration of the validity check is greater than the set anti-shake delay threshold, the state signal is determined to have passed the validity check, thereby excluding vehicle states unsuitable for tire rolling radius estimation.

[0228] Then, the effective samples participating in the recursive least squares estimation are accumulated. When the number of accumulated effective samples reaches a preset threshold, the recursive least squares method is applied to determine the tire rolling radius error coefficient of the vehicle based on the accumulated effective samples, and then the tire rolling radius is estimated.

[0229] In the process of estimating the tire rolling radius, the tire rolling radius is dynamically calculated and obtained as well as the tire static radius considering the load distribution. The tire rolling radius is then adaptively constrained in real time to ensure that the tire rolling radius estimation result is within a physically reasonable range.

[0230] This tire rolling radius estimation architecture fully utilizes wheel speed sensors and inertial measurement unit sensors, improving the robustness of the tire rolling radius estimation results in two ways: firstly, it excludes signals that cannot be used for tire rolling radius estimation; secondly, it calculates the rolling radius error coefficient using a recursive least squares method with a forgetting factor. Furthermore, the upper and lower limits of the tire rolling radius estimation are dynamically updated.

[0231] In summary, this application has at least the following advantages:

[0232] First, by introducing a target forgetting factor and dynamically adjusting its size, the data saturation problem caused by the accumulation of historical data in the traditional recursive least squares method is effectively improved. This allows for a more sensitive response to the time-varying characteristics of vehicle driving conditions and tire states, significantly reducing estimation lag caused by excessive historical data inertia, and achieving fast and adaptive tracking of the tire rolling radius. Furthermore, using the estimated average residual as the basis for the dynamic target forgetting factor effectively suppresses the impact of outliers and abrupt changes on the tire rolling radius estimation results, improving the accuracy and anti-interference capability of tire rolling radius estimation. Further, after obtaining the rolling radius error coefficient, it is not directly used to calculate the tire rolling radius. Instead, it is combined with the tire rolling radius estimate from the previous moment and updated using the target forgetting factor through smoothing filtering. This effectively improves the jitter and divergence of the estimation results, enabling the output of stable, reliable, and smooth tire rolling radius estimates even under complex and dynamic driving conditions. This provides reliable state support for the vehicle control system and better meets the reliability and safety requirements of the vehicle control system.

[0233] Second, by utilizing the dynamically changing physical boundaries of the tire, the estimated tire rolling radius is adaptively constrained in real time, ensuring that the estimation results are within a physically reasonable range. Compared to related technologies that use a fixed threshold to constrain the tire rolling radius, which may lead to misjudgments due to changes in tire geometry under full load or no load conditions, this embodiment improves the physical reasonableness and robustness of the estimation results.

[0234] Third, by introducing a dynamic tire effective radius calculation method based on an accurate geometric model, the calculated tire effective radius is theoretically closer to the true effective radius of the tire in the rolling state, thereby improving the accuracy of subsequent tire rolling radius estimation.

[0235] Fourth, a multi-dimensional signal validity collaborative verification mechanism is introduced to ensure that the tire rolling radius estimation process is initiated only when the vehicle status signal meets the validity conditions. Through multi-dimensional signal quality detection, the comprehensiveness of signal quality judgment is improved, thereby avoiding abnormal data from participating in the recursive least squares estimation process, reducing the interference of abnormal data on the estimation results, effectively preventing estimation performance degradation, result divergence or deviation caused by data quality, and significantly improving the accuracy and robustness of tire rolling radius estimation.

[0236] Fifth, by filtering wheel speed signals and GPS vehicle speed signals, high-frequency noise and instantaneous interference are effectively suppressed, improving the input quality of subsequent estimation algorithms. Recursive least squares estimation is performed based on a sufficient number of valid samples, enhancing the statistical robustness and anti-interference fault tolerance of the estimation process, and improving the reliability and stability of the tire rolling radius error coefficient.

[0237] Figure 5 This is a schematic diagram of the tire rolling radius estimation device provided in the embodiments of this application, as shown below. Figure 5 As shown, the tire rolling radius estimation device 50 provided in this embodiment includes: an acquisition module 51, a processing module 52, and a determination module 53. Wherein:

[0238] The acquisition module 51 is used to acquire the vehicle's wheel speed signal and GPS vehicle speed signal;

[0239] Processing module 52 is used to apply the recursive least squares method to determine the tire rolling radius error coefficient of the vehicle based on wheel speed signal and GPS vehicle speed signal. The tire rolling radius error coefficient characterizes the degree of error between the effective radius of the tire and the tire rolling radius.

[0240] The determination module 53 is used to dynamically determine the corresponding target forgetting factor based on the estimated average residual generated during the recursive least squares calculation process. The target forgetting factor is used to eliminate the influence of data saturation on the tire rolling radius estimation.

[0241] The determination module 53 is also used to determine the estimated value of the tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

[0242] In one possible implementation, the determining module 53 is specifically used to: take the product of the effective tire radius and the rolling radius error coefficient as a first value; determine the difference between the first value and the tire rolling radius estimate at the previous moment; determine the product of the target forgetting factor and the difference; and add the product to the tire rolling radius estimate at the previous moment to obtain the tire rolling radius estimate at the current moment.

[0243] In one possible implementation, the determining module 53 is further configured to: determine the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process based on the pre-calibrated mapping relationship between the estimated average residual and the forgetting factor.

[0244] In one possible implementation, the effective tire radius is determined by: obtaining the vehicle's static tire radius and the radius of the undeformed tire, wherein the static tire radius is used to dynamically constrain the lower limit of the tire rolling radius, and the radius of the undeformed tire radius is used to dynamically constrain the upper limit of the tire rolling radius; and determining the vehicle's effective tire radius based on the static tire radius and the radius of the undeformed tire radius.

[0245] In one possible implementation, determining the effective tire radius of a vehicle based on the static tire radius and the radius of the undeformed tire includes: performing an inverse cosine calculation on the quotient of the static tire radius and the radius of the undeformed tire to obtain a second value; performing a sine calculation on the second value, and multiplying the sine calculation result by the radius of the undeformed tire as a third value; and determining the quotient of the third value and the second value as the effective tire radius of the vehicle.

[0246] In one possible implementation, the determining module 53 is further configured to: acquire vehicle status signals, including wheel speed signals, GPS vehicle speed signals, IMU signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals, before determining the tire rolling radius error coefficient of the vehicle; and determine that the status signals pass the validity check.

[0247] In one possible implementation, the validity of the status signal is determined as follows: if the signal strength of the GPS vehicle speed signal is greater than a strength threshold and the positioning accuracy of the GPS vehicle speed signal is greater than an accuracy threshold, then the GPS vehicle speed signal is determined to have passed the validity check; if the triaxial acceleration, angular acceleration, and longitudinal acceleration of the vehicle estimated based on the IMU signal in the IMU signal are all less than their corresponding thresholds, then the IMU signal is determined to have passed the validity check; if the vehicle is detected to be not in a low-speed condition, and the deviation between the vehicle speed corresponding to the GPS vehicle speed signal and the vehicle speed calculated based on the wheel speed signal is less than a deviation threshold, then the longitudinal vehicle speed signal is determined to have passed the validity check; if the vehicle's anti-lock braking system, electronic stability control system, and trailer stability assist system are all detected to be out of activation mode, then the assist function activation signal is determined to have passed the validity check; if the vehicle's torque signal is detected to be valid, and the sum of the longitudinal forces of the axles calculated based on the torque signal is less than a longitudinal force threshold, then the axle torque signal is determined to have passed the validity check; if each signal in the status signal passes the validity check, and the duration of passing the validity check is greater than the set anti-shake delay threshold, then the status signal is determined to have passed the validity check.

[0248] In one possible implementation, the processing module 52 is specifically used to: perform low-pass filtering on the wheel speed signal to obtain a filtered wheel speed signal; perform low-pass filtering on the GPS vehicle speed signal to obtain a filtered GPS vehicle speed signal; accumulate the filtered wheel speed signal and the filtered GPS vehicle speed signal as valid samples, and when the number of accumulated valid samples reaches a preset threshold, apply the recursive least squares method to determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

[0249] The tire rolling radius estimation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0250] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0251] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0252] It should be noted that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways; and it should be understood that the division of the various modules of the above device is only a logical functional division, and in actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can all be implemented in software through processing element calls; they can all be implemented in hardware; or some modules can be implemented by processing element calls to software, and some modules can be implemented in hardware. For example, the risk assessment module can be a separately established processing element, or it can be integrated into a chip of the above device. Alternatively, it can be stored as program code in the memory of the above device, and the risk assessment module's function can be called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through hardware integrated logic circuits in the processor element or software instructions.

[0253] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0254] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Video Discs, DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0255] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the electronic device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0256] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0257] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0258] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0259] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0260] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0261] This application also provides a vehicle, such as... Figure 7 As shown in the structural diagram of the vehicle provided in this application embodiment, the vehicle 70 includes a vehicle body and the electronic device 60 in the above embodiment.

[0262] For example, the vehicle integrates the electronic devices described in the foregoing embodiments, such as a body domain controller, chassis domain controller, autonomous driving domain controller, or a controller dedicated to vehicle state estimation. Through this electronic device, the vehicle achieves online, adaptive, and robust estimation of the tire rolling radius and applies it to the vehicle's advanced control functions, thereby improving the overall vehicle intelligence and performance.

[0263] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0264] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0265] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0266] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0267] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0268] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0269] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0270] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0271] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0272] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for estimating the rolling radius of a tire, characterized in that, include: Acquire vehicle wheel speed signals and GPS vehicle speed signals; The recursive least squares method is applied to determine the tire rolling radius error coefficient of the vehicle based on the wheel speed signal and the GPS vehicle speed signal. The tire rolling radius error coefficient characterizes the degree of error between the effective tire radius and the tire rolling radius. Based on the estimated average residual generated during the recursive least squares calculation process, the corresponding target forgetting factor is dynamically determined. The target forgetting factor is used to eliminate the influence of data saturation on tire rolling radius estimation. The estimated value of the tire rolling radius at the current moment is determined based on the rolling radius error coefficient, the effective tire radius, the estimated value of the tire rolling radius at the previous moment, and the target forgetting factor.

2. The tire rolling radius estimation method according to claim 1, characterized in that, The step of determining the estimated tire rolling radius at the current moment based on the rolling radius error coefficient, the effective tire radius, the estimated tire rolling radius at the previous moment, and the target forgetting factor includes: The product of the effective radius of the tire and the rolling radius error coefficient is used as the first value; Determine the difference between the first value and the estimated tire rolling radius at the previous moment; Determine the product of the target forgetting factor and the difference; The product is added to the tire rolling radius estimate from the previous moment to obtain the tire rolling radius estimate for the current moment.

3. The tire rolling radius estimation method according to claim 1, characterized in that, The step of dynamically determining the corresponding target forgetting factor based on the estimated average residual generated during the recursive least squares calculation includes: Based on the pre-calibrated mapping relationship between the estimated average residual and the forgetting factor, the target forgetting factor corresponding to the estimated average residual generated during the recursive least squares calculation process is determined.

4. The tire rolling radius estimation method according to any one of claims 1 to 3, characterized in that, The effective radius of the tire is determined in the following way: Obtain the static radius and undeformed tire radius of the vehicle. The static tire radius is used to dynamically constrain the lower limit of the tire rolling radius, and the undeformed tire radius is used to dynamically constrain the upper limit of the tire rolling radius. The effective tire radius of the vehicle is determined based on the static tire radius and the radius of the undeformed tire.

5. The tire rolling radius estimation method according to claim 4, characterized in that, Determining the effective tire radius of the vehicle based on the static tire radius and the radius of the undeformed tire includes: The second value is obtained by calculating the inverse cosine of the quotient of the static radius of the tire and the radius of the undeformed tire; The second value is sine-calculated, and the product of the sine calculation result and the radius of the undeformed tire is used as the third value; The quotient of the third value and the second value is determined as the effective tire radius of the vehicle.

6. The tire rolling radius estimation method according to any one of claims 1 to 3, characterized in that, Before determining the tire rolling radius error coefficient of the vehicle, the method further includes: The vehicle status signals are acquired, including wheel speed signals, GPS vehicle speed signals, inertial measurement unit (IMU) signals, longitudinal vehicle speed signals, auxiliary function activation signals, and axle torque signals. The status signal is confirmed to have passed the validity check.

7. The tire rolling radius estimation method according to claim 6, characterized in that, The validity of the status signal is determined by the following method: If the signal strength of the GPS vehicle speed signal is greater than the strength threshold and the positioning accuracy of the GPS vehicle speed signal is greater than the accuracy threshold, then the GPS vehicle speed signal is determined to have passed the validity check. If the triaxial acceleration, angular acceleration, and longitudinal acceleration of the vehicle estimated based on the IMU signal are all less than the corresponding threshold, then the IMU signal is determined to have passed the validity check. If it is detected that the vehicle is not in a low-speed condition, and the deviation between the vehicle speed corresponding to the GPS vehicle speed signal and the vehicle speed calculated based on the wheel speed signal is less than the deviation threshold, then it is determined that the longitudinal vehicle speed signal passes the validity check. If it is detected that the vehicle's anti-lock braking system, electronic stability control system, and trailer stability assist system are not in active mode, then the activation signal of the assist function is determined to have passed the validity check. If the torque signal of the vehicle is detected to be valid, and the sum of the longitudinal forces of the axle calculated based on the torque signal is less than the longitudinal force threshold, then the axle torque signal is determined to have passed the validity check. The status signal is determined to have passed the validity check when each of the status signals passes the validity check and the duration of the validity check is greater than the set anti-shake delay threshold.

8. The tire rolling radius estimation method according to any one of claims 1 to 3, characterized in that, The application of recursive least squares method, based on the wheel speed signal and the GPS vehicle speed signal, determines the tire rolling radius error coefficient of the vehicle, including: The wheel speed signal is subjected to low-pass filtering to obtain the filtered wheel speed signal; The GPS vehicle speed signal is subjected to low-pass filtering to obtain the filtered GPS vehicle speed signal; The filtered wheel speed signal and the filtered GPS vehicle speed signal are accumulated as valid samples. When the number of accumulated valid samples reaches a preset threshold, the recursive least squares method is applied to determine the tire rolling radius error coefficient of the vehicle based on the accumulated valid samples.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the tire rolling radius estimation method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, The vehicle includes a vehicle body and the electronic equipment as described in claim 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the tire rolling radius estimation method as described in any one of claims 1 to 8.

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