Tire pressure monitoring system positioning correction method, vehicle, and storage medium

Abstract

The application relates to a tire pressure monitoring system positioning correction method, a vehicle and a storage medium, and belongs to the technical field of vehicle control. The method comprises the following steps: acquiring a vehicle steering angle and a yaw angular velocity; inputting the vehicle steering angle and the yaw angular velocity into a vehicle kinematics model to determine a theoretical pulse number difference value between wheels. The method can input the vehicle steering angle and the yaw angular velocity into the vehicle kinematics model, determine the theoretical pulse number difference value between the wheels under the current posture by using the vehicle motion law, judge whether there is a positioning conflict according to an actual pulse number difference value and a kinematics constraint range determined based on the theoretical pulse number difference value, and correct the tire position of the tire with the positioning conflict based on the current pulse sequence and a radio frequency signal of a tire pressure monitoring sensor, so that the continuous accuracy of tire pressure monitoring system positioning can be ensured, the driving safety risk caused by position confusion during tire pressure alarm is avoided, and the hardware does not need to be changed, so that the method is convenient to implement and has strong universality.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a tire pressure monitoring system positioning and correction method, a vehicle, and a storage medium. Background Technology

[0002] Current mainstream Tire Pressure Monitoring Systems (TPMS) rely on matching the timing of the pulse count (or tooth count) generated by wheel speed sensors with the radio frequency signals emitted by the TPMS sensors to determine the wheel position corresponding to each sensor. Ideally, the number of pulses generated per wheel speed sensor revolution is fixed. However, in actual use, the tire rolling radius dynamically changes due to factors such as tire pressure, wear, load, and temperature, causing unexpected changes in the actual number of pulses generated by a single wheel over the same distance traveled (i.e., pulse count variation). Furthermore, manufacturing errors in the sensor tooth ring, installation gaps, and signal interference can also introduce pulse counting errors. When the pulse count of a particular wheel varies significantly, the system's original pulse-position mapping relationship fails, potentially leading to the misinterpretation of the TPMS signal from the left front wheel as the right rear wheel. This results in incorrect tire pressure monitoring system positioning, causing confusion in tire pressure alarm location information. Drivers may be unable to accurately identify which tire is actually underinflated, delaying intervention and creating safety hazards. Summary of the Invention

[0003] This application provides a tire pressure monitoring system positioning and calibration method, a vehicle, and a storage medium to solve the technical problem of how to accurately calibrate the tire pressure monitoring system.

[0004] In a first aspect, this application provides a method for calibrating the positioning of a tire pressure monitoring system, the method comprising: Obtain the vehicle's steering angle and yaw rate; The vehicle steering angle and yaw rate are input into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematics model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture; The range of kinematic constraints is determined based on the theoretical pulse number difference. Determine whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range; The tire positions where positioning conflicts exist are corrected based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor.

[0005] Optionally, the vehicle steering angle and the yaw rate are input into the vehicle kinematic model to determine the theoretical pulse number difference between the wheels, including: The first theoretical speed difference between the wheels is determined based on the vehicle's steering angle, wheelbase, track width, and vehicle kinematics model. The second theoretical speed difference is determined based on the yaw rate and the wheel track. Obtain the dynamic confidence coefficient; wherein the dynamic confidence coefficient is used to characterize the weighting coefficients of the first theoretical speed difference and the second theoretical speed difference; The theoretical pulse number difference is determined based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference.

[0006] Optionally, determining the theoretical pulse number difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference includes: The target theoretical speed difference is determined based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference. The theoretical pulse number difference is determined based on the target theoretical speed difference and the preset wheel speed pulse conversion coefficient.

[0007] Optionally, obtain the dynamic confidence coefficient, including: Get the current vehicle speed and the current road surface adhesion coefficient; The dynamic confidence coefficient is determined based on the current vehicle speed and the current road surface adhesion coefficient; wherein, the dynamic confidence coefficient includes a first weighting coefficient of the first theoretical speed difference and a second weighting coefficient of the second theoretical speed difference, the sum of the first weighting coefficient and the second weighting coefficient is 1, the first weighting coefficient is negatively correlated with the current vehicle speed, and the first weighting coefficient is positively correlated with the current road surface adhesion coefficient.

[0008] Optionally, determining the kinematic constraint range based on the theoretical pulse number difference includes: The target deviation threshold is determined based on the current road surface condition and a preset deviation mapping relationship; wherein, the deviation mapping relationship is the mapping relationship between the road surface condition and the deviation threshold. The theoretical pulse interval is determined based on the difference between the target deviation threshold and the theoretical pulse number, and the theoretical pulse interval is used as the kinematic constraint range.

[0009] Optionally, determining whether a positioning conflict exists based on the actual pulse number difference and the kinematic constraint range includes: The actual pulse count difference is obtained, and the current timing coefficient matching threshold is obtained; wherein, the timing coefficient matching threshold is used to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence; Determine whether the actual pulse number difference is within the range of the kinematic constraints; If the actual pulse count difference is not within the kinematic constraint range, and the duration of the difference exceeds a preset duration, then it is determined that there is a positioning conflict in the wheel. If the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is greater than the first deviation threshold, then the timing coefficient matching threshold is lowered so as to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence according to the lowered timing coefficient matching threshold; If the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is less than or equal to the first deviation threshold, then the radio frequency signal and pulse sequence of the tire pressure monitoring sensor are matched according to the current timing coefficient matching threshold.

[0010] Optionally, before determining whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range, the method further includes: Within a preset time window, acquire the pulse count sample value for each wheel; Calculate the mean and variance of the pulse number sample values ​​within the preset time window; A pulse number variation trend model for the wheel is constructed based on the mean and variance. Identify whether there are pulse number anomalies based on the pulse number change trend model; If the pulse count anomaly is present and it is determined that there is a systematic wheel misalignment, the step of determining the kinematic constraint range based on the theoretical pulse count difference continues.

[0011] Optionally, the tire position where the positioning conflict exists is corrected based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor, including: Identify the target pulse sequence in the current pulse sequence that contains the positioning conflict; The time-series correlation coefficient of the target pulse sequence is determined based on the radio frequency signal of the tire pressure monitoring sensor; The desired matching degree of the target pulse sequence is determined based on the vehicle steering angle and the yaw rate; The target wheel corresponding to the target pulse sequence is determined based on the temporal correlation coefficient and the expected matching degree. The target wheel is corrected based on the target pulse sequence.

[0012] Secondly, this application provides a tire pressure monitoring system positioning correction device, the device comprising: The acquisition module is used to acquire the vehicle's steering angle and yaw rate; The first determining module is used to input the vehicle steering angle and the yaw rate into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematics model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture; The second determining module is used to determine the kinematic constraint range based on the theoretical pulse number difference; The conflict determination module is used to determine whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range. The correction module is used to correct the tire position where the positioning conflict exists based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor.

[0013] Thirdly, this application provides a vehicle including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in the memory, it implements the tire pressure monitoring system positioning correction method according to any embodiment of the first aspect.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the tire pressure monitoring system positioning correction method as described in any embodiment of the first aspect.

[0015] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application obtains the vehicle steering angle and yaw rate; inputs the vehicle steering angle and yaw rate into the vehicle kinematic model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematic model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture; determines the kinematic constraint range based on the theoretical pulse number difference; determines whether there is a positioning conflict based on the actual pulse number difference and the kinematic constraint range; and corrects the tire position with positioning conflict based on the current pulse sequence and the radio frequency signal of the tire pressure monitoring sensor. This method inputs the vehicle's steering angle and yaw rate into the vehicle's kinematic model, uses the vehicle's motion laws to determine the theoretical pulse number difference between the wheels in the current posture, and then judges whether there is a positioning conflict based on the actual pulse number difference and the kinematic constraint range determined based on the theoretical pulse number difference. The position of the tire with positioning conflict is corrected based on the current pulse sequence and the radio frequency signal of the tire pressure monitoring sensor, thereby ensuring the continuous accuracy of the tire pressure monitoring system's positioning, avoiding the driving safety risks caused by position confusion when the tire pressure alarm is triggered, and requiring no hardware modification, making it convenient to implement and highly versatile. Attached Figure Description

[0016] 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.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0019] Figure 1 A system architecture diagram of a tire pressure monitoring system positioning correction method provided in one embodiment of this application; Figure 2 A schematic flowchart illustrating a tire pressure monitoring system positioning correction method according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a tire pressure monitoring system positioning correction device provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of a vehicle provided in one embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0022] To address the technical problem of accurately calibrating the tire pressure monitoring system in related technologies, this application provides a tire pressure monitoring system positioning calibration method, a vehicle, and a storage medium, which can ensure the accuracy of the tire pressure monitoring system positioning and avoid driving safety risks caused by position confusion when the tire pressure alarm is triggered.

[0023] The first embodiment of this application provides a tire pressure monitoring system positioning correction method, which can be applied to, for example... Figure 1 The system architecture shown includes at least a data acquisition module 101 and a data processing module 102, which establish a communication connection. Specifically, this system architecture can be a system for calibrating the tire pressure monitoring system, or a vehicle equipped with such a system. The type of vehicle is not limited; for example, it can be a gasoline-powered vehicle, a pure electric vehicle, a hybrid vehicle, or a fuel cell vehicle, etc.

[0024] Next, based on the system architecture, the positioning and correction method of the tire pressure monitoring system will be described in detail, such as... Figure 2 The tire pressure monitoring system positioning and calibration method includes: Step 201: Obtain the vehicle's steering angle and yaw rate.

[0025] The vehicle steering angle can be the front wheel steering angle, which can be calculated from the steering ratio based on the steering wheel angle. The yaw rate can be collected by a yaw rate sensor or estimated from the difference in speed between the left and right wheels and the track width; there are no restrictions on this.

[0026] Step 202: Input the vehicle steering angle and yaw rate into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematics model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture.

[0027] The vehicle kinematic model can be a kinematic model based on Ackerman geometry. It can estimate the theoretical pulse number difference between the wheels of the vehicle in the current posture in real time. For example, it can estimate the theoretical pulse number difference between the left and right front wheels of the front axle based on the vehicle's steering angle and yaw rate, and estimate the theoretical pulse number difference between the left and right rear wheels of the rear axle based on the vehicle's steering angle and yaw rate. It should be understood that the theoretical pulse number difference can be the theoretical pulse number difference of the front axle, the theoretical pulse number difference of the rear axle, or both of the theoretical pulse number differences of the front axle and the rear axle, without any restrictions.

[0028] The theoretical pulse number difference calculated based on the vehicle kinematics model is a theoretically reasonable value for the difference in wheel pulses under the current vehicle posture, providing an objective basis for subsequent judgment and avoiding the limitations of simply comparing signal features.

[0029] In one embodiment, the vehicle steering angle and yaw rate are input into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels, including: determining a first theoretical speed difference between the wheels based on the vehicle steering angle, wheelbase, track width, and the vehicle kinematics model; determining a second theoretical speed difference based on the yaw rate and track width; obtaining a dynamic confidence coefficient; wherein the dynamic confidence coefficient is used to characterize the weighting coefficients of the first and second theoretical speed differences; and determining the theoretical pulse number difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference.

[0030] In this embodiment, the first theoretical speed difference Δv_steer between wheels is determined based on the vehicle steering angle δ, wheelbase L, track width W, and vehicle kinematics model. The second theoretical speed difference Δv_yaw is determined based on the yaw rate and track width. Then, the first theoretical speed difference and the second theoretical speed difference are weighted and summed according to the obtained dynamic confidence coefficient to determine the theoretical pulse number difference ΔP_theory.

[0031] Specifically, the theoretical pulse number difference is determined based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference, including: determining the target theoretical speed difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference; and determining the theoretical pulse number difference based on the target theoretical speed difference and the preset wheel speed pulse conversion coefficient.

[0032] In this embodiment, the first and second theoretical speed differences are first weighted and summed based on the obtained dynamic confidence coefficients. For example, a first weight coefficient is assigned to the first theoretical speed difference, and a second weight coefficient is assigned to the second theoretical speed difference. The sum of the first and second weight coefficients is 1. Then, the weighted summation determines the target theoretical speed difference ΔV_theory. Next, a preset wheel speed pulse conversion coefficient N is obtained. Based on the target theoretical speed difference ΔV_theory and the preset wheel speed pulse conversion coefficient N, the theoretical pulse number difference ΔP_theory is determined. For example, ΔP_theory = ΔV_theory × N. It should be understood that the theoretical pulse number difference is linearly proportional to the target theoretical speed difference; the larger the target theoretical speed difference, the larger the theoretical pulse number difference, and vice versa.

[0033] Specifically, taking the estimation of the theoretical pulse count difference between the front axle as an example, that is, calculating the theoretical pulse count difference between the left front wheel and the right front wheel, the estimation process is as follows: Based on vehicle steering angle estimation: The vehicle has a wheelbase of L, a track width of W, a steering angle of δ, and a speed of v. According to the Ackermann geometric kinematic model, the turning radius R≈L / tan(δ).

[0034] The rotational speed of the outer front wheel (assuming it's turning left, then it's the right front wheel) is v_outer = v × (R + W / 2) / R.

[0035] The rotational speed of the inner front wheel (assuming it's turning left, then it's the left front wheel) is v_inner = v × (RW / 2) / R.

[0036] The theoretical speed ratio Ratio_steer = v_outer / v_inner, and the first theoretical speed difference Δv_steer = v_outer - v_inner.

[0037] Based on yaw rate estimation: Since the yaw rate ω of the vehicle body can be directly estimated from the wheel speed difference: ω≈(v_right-v_left) / track width W, the second theoretical speed difference Δv_yaw=ω×W. v_right is the wheel speed of the right front wheel, and v_left is the wheel speed of the left front wheel.

[0038] Fusion estimation: The first and second theoretical wheel speed differences can be fused as follows: Theoretical wheel speed difference Δv_theory = α × Δv_steer + (1-α) × Δv_yaw, where α can be a dynamic confidence coefficient adjusted according to vehicle speed and road surface adhesion conditions. For example, a higher α can be selected at low speeds, emphasizing the vehicle steering angle; while a lower α can be selected at high speeds or under low road surface adhesion conditions, emphasizing the yaw rate.

[0039] The theoretical pulse number difference ΔP_theory = ΔV_theory × N, where N is the wheel speed pulse conversion coefficient.

[0040] In this embodiment, the dynamic confidence coefficient is used to characterize the weighting coefficient of the first theoretical speed difference and the second theoretical speed difference. By setting the dynamic confidence coefficient, it can be adjusted according to the actual situation so that the theoretical pulse number difference is more in line with the actual situation.

[0041] In one embodiment, obtaining the dynamic confidence coefficient includes: obtaining the current vehicle speed and the current road surface adhesion coefficient; determining the dynamic confidence coefficient based on the current vehicle speed and the current road surface adhesion coefficient; wherein the dynamic confidence coefficient includes a first weighting coefficient of the first theoretical speed difference and a second weighting coefficient of the second theoretical speed difference, the sum of the first weighting coefficient and the second weighting coefficient is 1, the first weighting coefficient is negatively correlated with the current vehicle speed, and the first weighting coefficient is positively correlated with the current road surface adhesion coefficient.

[0042] In this embodiment, the current vehicle speed and the current road surface adhesion coefficient are first obtained. The current road surface adhesion coefficient can be estimated in real time by the ABS / ESP controller. A dynamic confidence coefficient can be determined based on the current vehicle speed and the current road surface adhesion coefficient. Specifically, a mapping relationship between vehicle speed, the current road surface adhesion coefficient, and the dynamic confidence coefficient can be preset. This allows for the determination of a real-time, dynamic confidence coefficient based on the current vehicle speed and the current road surface adhesion coefficient. The dynamic confidence coefficient includes a first weighting coefficient α of the first theoretical speed difference and a second weighting coefficient (1-α) of the second theoretical speed difference. The first weighting coefficient is negatively correlated with the current vehicle speed; that is, the faster the current vehicle speed, the smaller the first weighting coefficient, and the slower the current vehicle speed, the larger the first weighting coefficient. The first weighting coefficient is positively correlated with the current road surface adhesion coefficient; that is, the larger the current road surface adhesion coefficient, the larger the first weighting coefficient, and the smaller the current road surface adhesion coefficient, the smaller the first weighting coefficient.

[0043] In this embodiment, the first theoretical speed difference is obtained based on the steering angle, and the first weighting coefficient is the coefficient of the first theoretical speed difference. Since the theoretical speed difference mainly depends on the steering angle at low speeds, the lower the vehicle speed, the higher the first weighting coefficient, meaning the first weighting coefficient is negatively correlated with the current vehicle speed. The second weighting coefficient is the coefficient of the second theoretical speed difference obtained based on the yaw rate. At low adhesion coefficients, the second weighting coefficient is larger (i.e., the first weighting coefficient is smaller), meaning the first weighting coefficient is positively correlated with the current road adhesion coefficient. By setting a dynamic confidence coefficient, the fusion weight of the steering angle and yaw rate estimation results can be dynamically adjusted according to vehicle speed and road adhesion conditions. Under different driving conditions, the first or second theoretical speed difference with higher confidence is prioritized, effectively improving the calculation accuracy of the fused theoretical speed difference, thereby improving the calculation accuracy of the theoretical pulse number difference and enhancing the robustness and positioning accuracy of the system in complex scenarios.

[0044] Step 203: Determine the kinematic constraint range based on the theoretical pulse number difference.

[0045] The kinematic constraint range represents the tolerable interval, that is, the range of pulse number differences that the system considers reasonable under the current vehicle motion state.

[0046] In one embodiment, determining the kinematic constraint range based on the difference in theoretical pulse counts includes: determining a target deviation threshold based on the current road surface condition and a preset deviation mapping relationship; wherein the deviation mapping relationship is a mapping relationship between the road surface condition and the deviation threshold; determining a theoretical pulse interval based on the difference in theoretical pulse counts based on the target deviation threshold, and using the theoretical pulse interval as the kinematic constraint range.

[0047] In this embodiment, a target deviation threshold is determined based on the current road surface condition and a preset deviation mapping relationship. Different road surface conditions can correspond to different target deviation thresholds. The target deviation threshold is, for example, Margin. Margin represents a dynamic margin, which can include minor errors caused by tire side slip characteristics and road surface unevenness. It is the reasonable pulse deviation allowed under the current road surface condition. Based on the difference between the target deviation threshold and the theoretical pulse number, the theoretical pulse interval can be determined as [ΔP_theory-Margin, ΔP_theory+Margin]. The theoretical pulse interval can be used as the kinematic constraint range. By defining the kinematic constraint range, a reasonable allowable deviation is set for the current road surface condition, which can provide a reasonable basis for judging whether a positioning conflict exists.

[0048] Specifically, the target deviation threshold can be related to the theoretical pulse number difference ΔP_theory and the road surface condition. For example, the larger the theoretical pulse number difference ΔP_theory, the larger the target deviation threshold; the smaller the theoretical pulse number difference ΔP_theory, the smaller the target deviation threshold. The smoother the road surface, the smaller the target deviation threshold; the bumpier the road surface, the larger the target deviation threshold. For instance, the target deviation threshold can be divided into a proportional term and a fixed offset term. The proportional term changes synchronously with the theoretical pulse number difference. The larger the turning radius and the larger the theoretical pulse number difference ΔP_theory, the larger the allowable target deviation threshold is, and it is proportionally amplified to match the wheel speed deviation naturally generated under steering conditions. The fixed offset term is a basic tolerance value set for road surface disturbances. When the road surface is smooth, the target deviation threshold Margin = 2% × ΔP_theory + 1%. That is, on a smooth road surface without significant bumps or impacts, the fixed offset term is 1%, retaining only a small basic tolerance. The overall tolerance range is relatively strict, only accepting normal deviations such as tire side slip and minor transmission errors. When the road surface is bumpy, the target deviation threshold Margin = 2% × ΔP_theory + 3%. That is, when the road surface has undulations, potholes, speed bumps, etc., causing the wheels to slip and bounce up and down, additional random fluctuations will occur. Therefore, the fixed offset term is increased, the overall tolerance range is widened, and normal pulse disturbances from the road surface are accepted.

[0049] In this embodiment, the target deviation threshold is combined with the proportional term and the fixed offset term. On the one hand, it adaptively adjusts the deviation size according to the theoretical pulse difference to adapt to different steering conditions. On the other hand, it switches the basic tolerance value according to the road surface smoothness and bumpy state. On bumpy roads, the tolerance range is actively widened to effectively shield the random pulse fluctuations caused by road impact and wheel bounce, reducing positioning conflict misjudgment. At the same time, on smooth roads, the tolerance threshold is tightened to ensure the accuracy of anomaly detection.

[0050] Step 204: Determine whether there is a positioning conflict based on the actual pulse number difference and the kinematic constraint range.

[0051] The actual pulse count difference can be obtained by collecting the pulse count of each of the four wheels using wheel speed sensors. Then, the difference in the actual pulse count between the left and right front wheels on the front axle, and the difference in the actual pulse count between the left and right rear wheels on the rear axle are calculated. It should be understood that the actual pulse count difference includes the difference in the actual pulse count of the front axle and the difference in the actual pulse count of the rear axle. When determining the positioning conflict, the judgment is made based on the difference in the actual pulse count of the front axle and the difference in the theoretical pulse count of the front axle, and based on the difference in the actual pulse count of the rear axle and the difference in the theoretical pulse count of the rear axle.

[0052] There is a certain deviation between the actual pulse count difference and the theoretical pulse count difference. Some deviations can be considered reasonable variations, while others are unreasonable variations. The distinction between reasonable and unreasonable variations can be determined by whether the actual pulse count difference falls within the kinematic constraints. For example, although the outer wheel may rotate a few more times than the inner wheel when turning, this is entirely in accordance with physical laws, and the actual pulse count difference ΔP_actual falls within the kinematic constraints, which the system considers a reasonable variation. However, if the actual pulse count difference ΔP_actual continuously and significantly exceeds the kinematic constraints—for example, when driving straight (ΔP_theory ≈ 0), the system detects that the left rear wheel has 5% more pulses than the right rear wheel (ΔP_actual > Margin)—this cannot be explained by any vehicle motion state, and the system determines it to be an unreasonable variation, confirming a positioning conflict.

[0053] In one embodiment, determining whether a positioning conflict exists based on the actual pulse number difference and the kinematic constraint range includes: obtaining the actual pulse number difference and obtaining the current timing coefficient matching threshold; wherein, the timing coefficient matching threshold is used to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence; determining whether the actual pulse number difference is within the kinematic constraint range; if the actual pulse number difference is not within the kinematic constraint range, and the duration of being outside the kinematic constraint range exceeds a preset duration, then it is determined that there is a positioning conflict with the wheel; if the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is greater than a first deviation threshold, then the timing coefficient matching threshold is lowered, so as to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence according to the lowered timing coefficient matching threshold; if the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is less than or equal to the first deviation threshold, then the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence is matched according to the current timing coefficient matching threshold.

[0054] In this embodiment, the actual pulse count difference and the current timing coefficient matching threshold are obtained; it is determined whether the actual pulse count difference is within the kinematic constraint range; if the actual pulse count difference is not within the kinematic constraint range, and the duration of the non-kinematic constraint range exceeds a preset duration, then it is determined that there is a positioning conflict in the wheel. The preset duration is, for example, 5 seconds, which can exclude instantaneous interference.

[0055] In this embodiment, the timing coefficient matching threshold is the matching threshold between the pulse sequence and the radio frequency signal of the tire pressure monitoring sensor. Traditional positioning algorithms typically use a fixed timing coefficient matching threshold (e.g., 0.9) to determine which wheel's pulse sequence matches the radio frequency signal sent by the tire pressure monitoring sensor. In this embodiment, the timing coefficient matching threshold can be adaptively adjusted. For example, if the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is greater than a first deviation threshold, it indicates that although the actual pulse number difference is within the kinematic constraint range, the deviation is too large. To avoid misjudgment and improve matching efficiency, the timing coefficient matching threshold can be appropriately lowered. The matching of the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence can be performed based on the lowered timing coefficient matching threshold, making matching easier and faster. If the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is less than or equal to the first deviation threshold, it indicates that the actual pulse number difference is within the kinematic constraint range and the deviation is small. In this case, the timing coefficient matching threshold does not need to be adjusted. The radio frequency signal of the tire pressure monitoring sensor is matched with the pulse sequence according to the current timing coefficient matching threshold to ensure matching accuracy.

[0056] Specifically, the system detects a pulse count variation, but ΔP_actual remains within the kinematic constraints. In this case, the system assumes the baseline has changed (e.g., a smaller spare tire has been installed) and no longer rigidly adheres to the historical baseline. It lowers the timing coefficient matching threshold (e.g., from 0.9 to 0.85) to more easily match the current mutated pulse sequence with the RF signal, avoiding misjudgments caused by baseline drift. If ΔP_actual is small and within the kinematic constraints, the system maintains a high threshold (0.9) to ensure matching accuracy.

[0057] Step 205: Correct the tire positions that have positioning conflicts based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor.

[0058] This method inputs the vehicle's steering angle and yaw rate into the vehicle's kinematic model, uses the vehicle's motion laws to determine the theoretical pulse number difference between the wheels in the current posture, and then judges whether there is a positioning conflict based on the actual pulse number difference and the kinematic constraint range determined based on the theoretical pulse number difference. The position of the tire with positioning conflict is corrected based on the current pulse sequence and the radio frequency signal of the tire pressure monitoring sensor, thereby ensuring the continuous accuracy of the tire pressure monitoring system's positioning, avoiding the driving safety risks caused by position confusion when the tire pressure alarm is triggered, and requiring no hardware modification, making it convenient to implement and highly versatile.

[0059] In one embodiment, before determining the kinematic constraint range based on the theoretical pulse number difference, the method further includes: acquiring pulse number sampling values ​​for each wheel within a preset time window; calculating the mean and variance of the pulse number sampling values ​​within the preset time window; constructing a pulse number variation trend model for the wheel based on the mean and variance; identifying whether there is a pulse number anomaly based on the pulse number variation trend model; and if there is a pulse number anomaly and it is determined that there is a systematic offset of the wheel, continuing to execute the step of determining the kinematic constraint range based on the theoretical pulse number difference.

[0060] In this embodiment, before determining the kinematic constraint range based on the theoretical pulse number difference, pulse number variation detection can be performed to identify whether there are pulse number anomalies. Specifically, within a preset time window, the pulse number sampling value of each wheel can be obtained. The preset time window is a time window before the current moment. The pulse number sampling values ​​within the preset time window can be obtained from the database. A continuous data segment is used to characterize the continuous state of the wheel pulses, filtering out short-term interferences such as bumps and instantaneous slippage. Then, the mean and variance of the pulse number sampling values ​​within the preset time window are calculated. The mean reflects the average number of pulse number sampling values ​​within the window, representing the steady-state benchmark of the pulses during that period. The variance reflects the fluctuation amplitude of the pulse number sampling values ​​within the window, which can distinguish between random jitter and steady-state changes. A pulse count variation trend model for the wheel is constructed based on the mean and variance. This model is then used to identify any pulse count anomalies. If anomalies are found, it is determined whether they represent a systematic wheel offset. If a systematic offset exists, subsequent steps are performed, such as determining the kinematic constraint range based on the theoretical pulse count difference, and determining whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range. Systematic offset refers to a stable unidirectional drift in the pulse count, meaning it doesn't fluctuate within the normal range but rather shifts towards a larger or smaller value overall.

[0061] Preset time windows can include short-term and long-term time windows. Short-term time windows calculate short-term characteristics (representing "transient" or "recent behavior"), such as taking pulse count samples from each wheel within 10 seconds and calculating the mean (reflecting short-term average speed) and variance (reflecting short-term jitter amplitude, such as a sudden increase in variance when going over speed bumps). Long-term time windows calculate long-term characteristics (representing "steady state" or "historical baseline"), such as taking data from the vehicle's entire lifespan since the current power-on or historical data from the previous 24 hours. Similarly, the mean and variance of pulse count samples within the long-term time window are calculated to construct a pulse count trend model. For example, recording the long-term average pulse count samples for each wheel allows for real-time tracking and prediction of the pulse count trend model for each wheel's pulse count samples using weighted recursive least squares (RLS), rather than just looking at the current value. For example, the pulse count trend model could be y(t) = a×t + b + e(t). Where y(t) is the pulse count sample value at the current moment, t is time, a is the trend term (variation rate), b is the baseline term, and e is the random error. In the pulse count change trend model, newly acquired data points can be assigned higher weights, while older data points can be assigned lower weights (i.e., the "forgetting factor"). This allows for rapid tracking of the gradual increase in pulse count caused by a slow decrease in tire pressure, without being overly affected by momentary vibrations from a few seconds ago. Using the RLS algorithm, a (trend term) in the current pulse count change trend model can be estimated in real time. If the value of a is consistently greater than a certain threshold and is positive, it indicates that the pulse count of the wheel is steadily increasing (e.g., due to a continuous decrease in tire pressure leading to a smaller rolling radius). In this case, a systematic offset can be considered, and the degree of offset can be quantified by the magnitude of a and the cumulative offset Δy = a × Δt. The theoretical pulse count for the next moment can also be predicted using the pulse count change trend model. (t+1), and then compare it with the actual measured value y(t+1) to obtain the residual r=y- If the residual r fluctuates drastically in a short period of time but has a mean of 0, it indicates random disturbance; if the residual r deviates from 0 for a long period of time, it confirms the existence of systematic bias.

[0062] In one embodiment, correcting the position of a tire with a positioning conflict based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor includes: identifying a target pulse sequence with a positioning conflict in the current pulse sequence; determining the temporal correlation coefficient of the target pulse sequence based on the radio frequency signal from the tire pressure monitoring sensor; determining the expected matching degree of the target pulse sequence based on the vehicle steering angle and yaw rate; determining the target wheel corresponding to the target pulse sequence based on the temporal correlation coefficient and the expected matching degree; and correcting the target wheel based on the target pulse sequence.

[0063] In this embodiment, when a positioning conflict exists, the target pulse sequence with the conflict is identified. The temporal correlation coefficient of the target pulse sequence is determined based on the radio frequency signal from the tire pressure monitoring sensor, i.e., the temporal correlation coefficient between the target pulse sequence and each of the four wheels. The expected matching degree of the target pulse sequence is determined based on the vehicle's steering angle and yaw rate, i.e., the expected matching degree after matching the target pulse sequence with the four wheels. Then, a weighted score is applied to the temporal correlation coefficient and the expected matching degree to determine the target wheel corresponding to the target pulse sequence. The target wheel is then corrected based on the target pulse sequence. For example, a pulse sequence originally on the left front wheel might change to resemble the left front wheel sequence due to a smaller spare tire being installed on the right rear wheel. However, when turning right, the speed displayed by this sequence is significantly faster (consistent with the kinematic characteristics of the right rear wheel). The module ultimately corrects and matches this sequence to the right rear wheel. After correcting the target wheel, the corrected result is output, and this correction event is recorded as a new "benchmark" for updating long-term statistical features.

[0064] In one specific embodiment, the correction steps are described in detail. Taking the original pulse sequence on the left front as an example, because the right rear wheel was fitted with a smaller spare tire, its pulse count changed to resemble the left front sequence. The system incorrectly bound the right rear wheel pulse sequence to the left front TPMS sensor, resulting in an error in the positioning binding relationship. The system determined that there was a positioning conflict.

[0065] After calculating the mean and variance of the pulse count samples within a preset time window, a pulse count change trend model of the wheel is constructed based on the mean and variance. Based on the pulse count change trend model, a systematic shift in the right front wheel speed pulse sequence is identified.

[0066] Comparing the actual pulse difference with the kinematic constraint range, if the actual difference continuously exceeds the range and exceeds the preset time, it is determined that there is a positioning conflict at the corresponding position of the right front wheel speed pulse sequence; the right front wheel speed pulse sequence is marked as the target pulse sequence, and the processing object of this correction is locked.

[0067] Based on the TPMS radio frequency signal, the time-series correlation coefficient of the target pulse sequence is calculated. The target pulse sequence is cross-correlated with the TPMS radio frequency signals of the four tires respectively, resulting in four sets of time-series correlation coefficients: For example, the correlation coefficient calculated for the left front wheel is 0.86, for the right rear wheel it is 0.92, for the left rear wheel it is 0.70, and for the right front wheel it is 0.75. Due to a systematic shift in the pulse sequence, the originally incorrectly bound left front channel still has a high correlation, but the truly matched right rear channel has the highest similarity.

[0068] Next, based on the steering angle and yaw rate, the expected matching degree of the target pulse sequence is calculated. For example, real-time vehicle steering angle and yaw rate are collected and substituted into the vehicle kinematics model. In this scenario, when the vehicle is turning right, the theoretical number of pulses on the left wheel is greater than that on the right wheel, and the difference between the inner and outer wheels of the front axle is the greatest. The theoretical fit between the target pulse sequence and the four wheels is evaluated respectively, and four sets of expected matching degrees are output: For example, the expected matching degree for the left front is 0.30, the expected matching degree for the right back is 0.95, the expected matching degree for the left back is 0.25, and the expected matching degree for the right front is 0.78.

[0069] The temporal relevance coefficient A and the expected matching degree B both range from [0, 1]. The weight of the temporal relevance coefficient is w1 (e.g., 0.6), and the weight of the expected matching degree is w2 (e.g., 0.4). Therefore, the weighted composite score = A × w1 + B × w2. Combining the two indicators, the weighted composite score for the left front is 0.86 × 0.6 + 0.3 × 0.4 = 0.636; the weighted composite score for the right back is 0.92 × 0.6 + 0.95 × 0.4 = 0.932; the weighted composite score for the left back is 0.70 × 0.6 + 0.25 × 0.4 = 0.52; and the weighted composite score for the right front is 0.75 × 0.6 + 0.78 × 0.4 = 0.762.

[0070] By comparing the weighted composite scores of each vehicle, the right rear wheel scores the highest, thus determining that the actual target wheel corresponding to the target pulse sequence is the right rear wheel. Then, the original erroneous binding relationship is released, the binding entry between the target pulse sequence and the left front wheel is cleared from the system's internal mapping table, and the target pulse sequence is bound to the right rear wheel, completing the position association between the hardware signal and the wheel speed sequence. The corrected tire position information is output to the vehicle's instrument panel and body controller, and the interface synchronously updates the wheel position corresponding to the tire pressure. The system locally records this correction event, triggering conditions, and binding relationships before and after correction for algorithm self-learning and fault tracing.

[0071] If relying solely on the temporal correlation coefficient, it is susceptible to the influence of pulse system offset, retains historical erroneous matching relationships, and cannot correct positioning errors. If relying solely on the expected matching degree, it deviates from the actual sensor signal and is susceptible to model errors. In this embodiment, the temporal correlation coefficient and the expected matching degree are fused, which can take into account both the actual characteristics of the signal and the physical motion law of the vehicle. The double verification greatly improves the accuracy of the correction results. In addition, in this embodiment, the target pulse sequence is locked first to achieve fixed-point correction, without the need to traverse the entire wheel, thus reducing the amount of computation.

[0072] In the embodiments described above, this application does not rely on an absolutely fixed number of pulses, and can tolerate slow changes in the number of pulses caused by normal factors such as tire pressure and load, significantly reducing the false alarm rate and exhibiting high robustness. It can automatically detect and distinguish between "reasonable variations" and "abnormal conflicts," triggering repositioning only when truly necessary, avoiding unnecessary system resets. By introducing a vehicle kinematic model to calculate the theoretical pulse number difference between wheels, the reliability of positioning results under complex conditions (such as continuous lane changes and low-adhesion road surfaces) is greatly improved. Furthermore, the tire pressure monitoring system positioning correction method of this application can be superimposed on existing tire pressure monitoring system architectures without requiring changes to hardware sensors, resulting in low implementation costs and ease of promotion.

[0073] Based on the same technical concept, the second embodiment of this application provides a tire pressure monitoring system positioning correction device, such as... Figure 3 The device includes: The acquisition module 301 is used to acquire the vehicle's steering angle and yaw rate; The first determining module 302 is used to input the vehicle steering angle and the yaw rate into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematics model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture; The second determining module 303 is used to determine the kinematic constraint range based on the theoretical pulse number difference; The conflict determination module 304 is used to determine whether a positioning conflict exists based on the actual pulse number difference and the kinematic constraint range. The correction module 305 is used to correct the tire position where the positioning conflict exists based on the current pulse sequence and the radio frequency signal of the tire pressure monitoring sensor.

[0074] This device can input the vehicle's steering angle and yaw rate into the vehicle's kinematic model, determine the theoretical pulse number difference between the wheels under the current posture using the vehicle's motion laws, and then determine whether there is a positioning conflict based on the actual pulse number difference and the kinematic constraint range determined based on the theoretical pulse number difference. It also corrects the tire position of the tire with positioning conflict based on the current pulse sequence and the radio frequency signal of the tire pressure monitoring sensor, thereby ensuring the continuous accuracy of the tire pressure monitoring system's positioning, avoiding the driving safety risks caused by position confusion when the tire pressure alarm is triggered, and requiring no hardware modification, making it convenient to implement and highly versatile.

[0075] Optionally, the first determining module 302 specifically includes: The first determining unit is used to determine the first theoretical speed difference between the wheels based on the vehicle steering angle, wheelbase, track width, and vehicle kinematic model; The second determining unit is used to determine the second theoretical speed difference based on the yaw rate and the wheel track. The first acquisition unit is used to acquire the dynamic confidence coefficient; wherein the dynamic confidence coefficient is used to characterize the weighting coefficients of the first theoretical speed difference and the second theoretical speed difference; The third determining unit is used to determine the theoretical pulse number difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference.

[0076] Optionally, the third determining unit is specifically used to: determine the target theoretical speed difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference; and determine the theoretical pulse number difference based on the target theoretical speed difference and the preset wheel speed pulse conversion coefficient.

[0077] Optionally, the first acquisition unit is specifically used to acquire the current vehicle speed and the current road surface adhesion coefficient; and to determine the dynamic confidence coefficient based on the current vehicle speed and the current road surface adhesion coefficient; wherein the dynamic confidence coefficient includes a first weighting coefficient of the first theoretical speed difference and a second weighting coefficient of the second theoretical speed difference, the sum of the first weighting coefficient and the second weighting coefficient is 1, the first weighting coefficient is negatively correlated with the current vehicle speed, and the first weighting coefficient is positively correlated with the current road surface adhesion coefficient.

[0078] Optionally, the second determining module 303 specifically includes: The fourth determining unit is used to determine the target deviation threshold based on the current road surface condition and a preset deviation mapping relationship; wherein the deviation mapping relationship is a mapping relationship between the road surface condition and the deviation threshold. The fifth determining unit is used to determine the theoretical pulse interval based on the difference between the target deviation threshold and the theoretical pulse number, and to use the theoretical pulse interval as the kinematic constraint range.

[0079] Optionally, the conflict determination module 304 specifically includes: The second acquisition unit is used to acquire the actual pulse number difference and the current timing coefficient matching threshold; wherein, the timing coefficient matching threshold is used to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence; The first judgment unit is used to determine whether the actual pulse number difference is within the kinematic constraint range; The sixth determining unit is used to determine that there is a positioning conflict in the wheel if the actual pulse number difference is not within the kinematic constraint range and the duration of the difference is not within the kinematic constraint range exceeds a preset duration. The first matching unit is configured to lower the timing coefficient matching threshold if the actual pulse number difference is within the kinematic constraint range and the deviation between the actual pulse number difference and the theoretical pulse number difference is greater than a first deviation threshold, so as to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence according to the lowered timing coefficient matching threshold. The second matching unit is used to match the radio frequency signal and pulse sequence of the tire pressure monitoring sensor according to the current timing coefficient matching threshold if the actual pulse number difference is within the kinematic constraint range and the deviation between the actual pulse number difference and the theoretical pulse number difference is less than or equal to the first deviation threshold.

[0080] Optionally, before determining the kinematic constraint range based on the theoretical pulse number difference, the second determining module 303 is further configured to: acquire the pulse number sample value of each wheel within a preset time window; calculate the mean and variance of the pulse number sample values ​​within the preset time window; construct a pulse number change trend model of the wheel based on the mean and variance; identify whether there is a pulse number anomaly based on the pulse number change trend model; and if there is a pulse number anomaly and the pulse number anomaly indicates that the wheel has a systematic offset, the second determining module 303 is configured to perform the step of determining the kinematic constraint range based on the theoretical pulse number difference.

[0081] Optionally, the calibration module 305 specifically includes: The seventh determining unit is used to determine the target pulse sequence in the current pulse sequence that contains the positioning conflict; The eighth determining unit is used to determine the time-series correlation coefficient of the target pulse sequence based on the radio frequency signal of the tire pressure monitoring sensor; The ninth determining unit is used to determine the expected matching degree of the target pulse sequence based on the vehicle steering angle and the yaw rate; The tenth determining unit is used to determine the target wheel corresponding to the target pulse sequence based on the temporal correlation coefficient and the expected matching degree; A correction unit is used to correct the target wheel according to the target pulse sequence.

[0082] like Figure 4 As shown in the figure, this application embodiment provides a vehicle, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114. Memory 113 is used to store computer programs; In one embodiment of this application, the processor 111, when executing the program stored in the memory 113, implements the tire pressure monitoring system positioning correction method provided in any of the foregoing method embodiments.

[0083] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0084] The communication interface is used for communication between the aforementioned terminal and other devices.

[0085] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0086] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0087] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the tire pressure monitoring system positioning correction method as provided in any of the foregoing method embodiments.

[0088] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0090] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also mean including the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0091] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. In the description, suffixes such as "module," "part," or "unit" used to denote elements are used solely for illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0092] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for calibrating the positioning of a tire pressure monitoring system, characterized in that, The method includes: Obtain the vehicle's steering angle and yaw rate; The vehicle steering angle and yaw rate are input into the vehicle kinematics model to determine the theoretical pulse number difference between the wheels; wherein, the vehicle kinematics model is used to calculate the theoretical pulse number difference between the wheels of the vehicle in the current posture; The range of kinematic constraints is determined based on the theoretical pulse number difference. Determine whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range; The tire positions where positioning conflicts exist are corrected based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor.

2. The method according to claim 1, characterized in that, The vehicle steering angle and yaw rate are input into the vehicle kinematic model to determine the theoretical pulse number difference between the wheels, including: The first theoretical speed difference between the wheels is determined based on the vehicle's steering angle, wheelbase, track width, and vehicle kinematics model. The second theoretical speed difference is determined based on the yaw rate and the wheel track. Obtain the dynamic confidence coefficient; wherein the dynamic confidence coefficient is used to characterize the weighting coefficients of the first theoretical speed difference and the second theoretical speed difference; The theoretical pulse number difference is determined based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference.

3. The method according to claim 2, characterized in that, Determining the theoretical pulse number difference based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference includes: The target theoretical speed difference is determined based on the dynamic confidence coefficient, the first theoretical speed difference, and the second theoretical speed difference. The theoretical pulse number difference is determined based on the target theoretical speed difference and the preset wheel speed pulse conversion coefficient.

4. The method according to claim 2, characterized in that, To obtain the dynamic confidence coefficient, including: Get the current vehicle speed and the current road surface adhesion coefficient; The dynamic confidence coefficient is determined based on the current vehicle speed and the current road surface adhesion coefficient; wherein, the dynamic confidence coefficient includes a first weighting coefficient of the first theoretical speed difference and a second weighting coefficient of the second theoretical speed difference, the sum of the first weighting coefficient and the second weighting coefficient is 1, the first weighting coefficient is negatively correlated with the current vehicle speed, and the first weighting coefficient is positively correlated with the current road surface adhesion coefficient.

5. The method according to claim 1, characterized in that, The kinematic constraint range is determined based on the theoretical pulse number difference, including: The target deviation threshold is determined based on the current road surface condition and a preset deviation mapping relationship; wherein, the deviation mapping relationship is the mapping relationship between the road surface condition and the deviation threshold. The theoretical pulse interval is determined based on the difference between the target deviation threshold and the theoretical pulse number, and the theoretical pulse interval is used as the kinematic constraint range.

6. The method according to claim 1, characterized in that, Determining whether a positioning conflict exists based on the actual pulse count difference and the kinematic constraint range includes: The actual pulse count difference is obtained, and the current timing coefficient matching threshold is obtained; wherein, the timing coefficient matching threshold is used to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence; Determine whether the actual pulse number difference is within the range of the kinematic constraints; If the actual pulse count difference is not within the kinematic constraint range, and the duration of the difference exceeds a preset duration, then it is determined that there is a positioning conflict in the wheel. If the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is greater than the first deviation threshold, then the timing coefficient matching threshold is lowered so as to match the radio frequency signal of the tire pressure monitoring sensor with the pulse sequence according to the lowered timing coefficient matching threshold; If the actual pulse number difference is within the kinematic constraint range, and the deviation between the actual pulse number difference and the theoretical pulse number difference is less than or equal to the first deviation threshold, then the radio frequency signal and pulse sequence of the tire pressure monitoring sensor are matched according to the current timing coefficient matching threshold.

7. The method according to claim 1, characterized in that, Before determining the kinematic constraint range based on the theoretical pulse number difference, the method further includes: Within a preset time window, acquire the pulse count sample value for each wheel; Calculate the mean and variance of the pulse number sample values ​​within the preset time window; A pulse number variation trend model for the wheel is constructed based on the mean and variance. Identify whether there are pulse number anomalies based on the pulse number change trend model; If the pulse count anomaly exists and it is determined that there is a systematic wheel misalignment, continue with the step of determining the kinematic constraint range based on the theoretical pulse count difference.

8. The method according to claim 1, characterized in that, Correcting the tire position for the positioning conflict based on the current pulse sequence and the radio frequency signal from the tire pressure monitoring sensor includes: Identify the target pulse sequence in the current pulse sequence that contains the positioning conflict; The time-series correlation coefficient of the target pulse sequence is determined based on the radio frequency signal of the tire pressure monitoring sensor; The desired matching degree of the target pulse sequence is determined based on the vehicle steering angle and the yaw rate; The target wheel corresponding to the target pulse sequence is determined based on the temporal correlation coefficient and the expected matching degree. The target wheel is corrected based on the target pulse sequence.

9. A vehicle, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the tire pressure monitoring system positioning correction method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the tire pressure monitoring system positioning correction method as described in any one of claims 1-8.

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