Tire grounding mark length estimation method based on vehicle-mounted tire acceleration sensor

Abstract

The invention relates to the technical field of automobile tire performance estimation, and discloses a tire grounding mark length estimation method based on a vehicle-mounted tire acceleration sensor, which comprises the following steps: S1, fixing a three-axis acceleration sensor on the inner surface of a tire crown, and then engaging a tire body of a tire with a rim to obtain a tire grounding mark length estimation result; installation of the tire and the three-axis acceleration sensor is completed, and radial acceleration, tangential acceleration signals and lateral acceleration signals of the tire are collected in real time; and S2, calculating the instantaneous rotation angular velocity of the tire according to the offset mean value of the radial acceleration. The three-axis acceleration sensor is directly mounted on the inner surface of the tire crown, and the key parameters are directly extracted from the motion characteristics of the tire body without depending on an external vehicle speed sensor or other auxiliary equipment, so that the dependence on external equipment is reduced, and the accuracy and reliability of grounding mark length estimation are effectively improved; meanwhile, the complexity of the system is reduced, and actual loading application is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile tire performance estimation, and in particular to a tire contact patch length estimation method based on an on-vehicle tire acceleration sensor. Background Art

[0002] With the continuous development of society, the number of cars on the road is also increasing. Tires, as the contact point between the car and the road, not only bear the car's weight but also influence various aspects of its performance, including safety and handling. The length of the tire contact patch plays an important role in analyzing tire rolling radius and dynamic characteristics. It is one of the most useful indicators for estimating tire performance and can be used to directly estimate tire stress, health, and road adhesion.

[0003] At present, relevant researchers have proposed many methods for estimating tire contact patch length, but there are relatively few methods that can estimate the contact patch length in real time when the vehicle is in motion. In addition, real-time estimation of the contact patch length in a dynamic state is challenging, and the relevant methods are still some distance away from practical application.

[0004] A search revealed publication number CN117063053A, a system and method for identifying tire contact length from radial acceleration signals. This method detects a tire acceleration waveform in the radial direction from the sampled output of an acceleration sensor mounted on the tire, integrating the acceleration waveform in the radial direction to generate a velocity waveform. Ground contact data is calculated based on at least a first and second peak value in the velocity waveform. The tire contact length is calculated based on at least the number of samples calculated during ground contact, the sampling rate of the output of the acceleration sensor mounted on the tire, and the vehicle's speed. This method requires incorporating vehicle speed to estimate the tire contact patch length.

[0005] Announcement No. CN119178399A, a method and device for testing tire contact patch length, this method uses the strain waveform data of the tire movement, combined with a flexible carcass model, to obtain the tire's contact patch data and circumferential strain curve, and then obtains the tire's contact patch angle, thereby determining the tire's contact patch length in combination with the tire's geometric radius. By exploring the relationship between the contact patch angle and the contact patch length during tire movement, and exploring the coupling relationship between the radial deformation and tangential deformation of the tire carcass ring, the accuracy of the contact patch length is improved. This method measures the tire's strain waveform data and combines it with a flexible carcass model to ultimately estimate the contact patch length. The accuracy of the estimation is closely related to the accuracy of the flexible carcass model.

[0006] Currently, the estimation of tire contact patch length is mainly divided into static measurement estimation and measurement estimation under tire rolling conditions. Static measurement estimation can be obtained by directly fixing the tire on a transparent plate and performing image processing. It is generally a laboratory measurement and it is difficult to reflect the contact patch length under dynamic conditions. External dynamic parameters often have errors and are difficult to accurately synchronize with the data of the tire sensor, which will introduce additional errors. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor, which solves the problem that external dynamic parameters often have errors and are difficult to accurately synchronize with the data of the tire sensor, resulting in additional errors.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor comprises the following steps: S1. Fixing a triaxial acceleration sensor to the inner surface of the tire crown, and then engaging the tire carcass with the rim to complete the installation of the tire and the triaxial acceleration sensor, and collecting tire radial acceleration, tangential acceleration signals, and lateral acceleration signals in real time; S2. Calculating the instantaneous rotational angular velocity of the tire according to the biased mean value of the radial acceleration, and obtaining the tire ground contact linear velocity based on the angular velocity and tire geometric parameters; S3. Detecting peak points of the radial acceleration signal when the tire enters a landing point and leaves a departure point, where the landing point and the departure point are the starting and ending positions of the tire-road contact area, and calculating a first contact patch length estimate based on a difference in the number of samples at the peak points, a sampling rate, and a contact line velocity; S4. Performing Marr wavelet filtering on the tangential acceleration signal, detecting the peak sample number difference of the filtered signal, and calculating a second contact patch length estimate based on the sample number difference, the sampling rate, and the contact line velocity; S5. Determine the vehicle's driving state based on the lateral acceleration signal, and dynamically correct the first and second estimated values to obtain a final contact patch length; S6. Transmitting the final contact patch length and tire status parameters to an onboard controller for vehicle control and early warning.

[0009] Preferably, in step S2, the instantaneous rotation angular velocity is calculated by the formula Calculate, where is the mean radial acceleration bias, is the rotation radius of the inner surface of the tire crown, and the tire ground contact line speed is calculated by the following formula calculate.

[0010] Preferably, in step S3, the first contact patch length estimate is calculated using the following formula: ; in, and is the sample number of the radial acceleration peak point, is the sampling rate.

[0011] Preferably, in step S4, the basis function used in the Marr wavelet filtering is defined as: ; Where, is the outer waveform basis function of the wavelet, is the wavelet internal waveform basis function, where ,and and is a scale parameter that is adaptively adjusted according to vehicle operating conditions.

[0012] Preferably, in step S4, the second contact patch length estimate is calculated using the following formula: ; in, and is the sample number of the peak point of the tangential acceleration after filtering.

[0013] Preferably, in step S5, the dynamic correction is calculated by the following formula: ; in, and is the confidence weight based on the real vehicle data fitting, and satisfies , the confidence weight is obtained by fitting a large amount of real vehicle test data, is the correction factor that changes with the lateral acceleration value.

[0014] Preferably, in step S3, the detection of the radial acceleration peak point is achieved by a local area peak search algorithm and is calculated by the following formula: ; in, and The preset upper and lower limits of the amplitude.

[0015] Preferably, the three-axis acceleration sensor integrates temperature and tire pressure detection modules, and transmits data in real time to the vehicle controller via the automotive-grade low-power Bluetooth protocol.

[0016] Preferably, in step S5, when the amplitude of the lateral acceleration signal exceeds a preset bump threshold, the vehicle is determined to be in a bumpy state, and steps S3 to S4 are retriggered to obtain updated first and second contact patch length estimates, which replace the original estimates for dynamic correction.

[0017] Preferably, in step S6, the vehicle controller adjusts the vehicle braking, driving and suspension control parameters in real time according to the final contact patch length in combination with the tire dynamics model.

[0018] The present invention provides a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor. It has the following beneficial effects: 1. The present invention directly mounts a three-axis acceleration sensor on the inner surface of the tire crown to collect the radial, tangential, and lateral acceleration signals of the tire in real time without relying on external vehicle speed sensors or other auxiliary equipment. Key parameters are directly extracted from the motion characteristics of the tire body, the bias characteristics of the radial acceleration are used to calculate the tire rotational angular velocity, and the contact patch length is estimated by combining the peak time difference and the sampling rate. This realizes a fully autonomous data processing closed loop, avoids errors caused by multi-sensor data synchronization, eliminates the need for reference of information from other on-board sensors, reduces dependence on external equipment, effectively improves the accuracy and reliability of contact patch length estimation, and reduces the complexity of the system, facilitating actual vehicle installation applications.

[0019] 2. The present invention uses filtering technology to separate noise and extract key feature points in the touchdown area. Combined with the peak detection results of radial acceleration, it forms a dual verification mechanism and further introduces lateral acceleration signals to dynamically judge the vehicle's driving status. The weight ratio of different signals is adaptively adjusted or data re-collection is triggered to ensure high accuracy under complex road conditions. Abnormal data is automatically eliminated and recalculated on bumpy roads. The influence of lateral force is compensated by correction coefficients when driving on curves. The multi-signal coordination and dynamic correction mechanism improve the robustness and accuracy of the system under extreme working conditions.

[0020] 3. The present invention integrates a three-axis acceleration sensor with temperature and tire pressure detection modules, and uses a low-power wireless communication protocol to transmit data to an on-board controller in real time, thereby building a complete closed loop from data acquisition and processing to vehicle control. The on-board controller optimizes braking, driving, and suspension parameters based on the real-time contact patch length, while providing high-precision input for tire health monitoring and road adhesion status assessment. This not only reduces installation and maintenance costs, but also provides a technical foundation for the deep integration of intelligent tire technology and vehicle active safety functions through seamless docking with the on-board system, helping to achieve a safer and more efficient driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1Schematic diagram of the method flow of the tire contact patch length estimation method based on the vehicle-mounted tire acceleration sensor of the present invention; Figure 2 Schematic diagram of a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to the present invention; Figure 3 Schematic diagram of a measured acceleration curve of a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to the present invention; Figure 4 Schematic diagram of a Marr wavelet curve used for filtering in the tire footprint length estimation method based on a vehicle-mounted tire acceleration sensor of the present invention; Figure 5 Schematic diagram of a filtered tangential acceleration curve of a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to the present invention; Figure 6 The figure is a schematic diagram of the system framework processing flow of the tire contact patch length estimation method based on the vehicle-mounted tire acceleration sensor of the present invention.

[0022] Among them, 1. Road surface; 2. Tire; 3. Rim; 4. Three-axis acceleration sensor; 5. Approach point; 6. Departure point. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Please see the attached Figure 1 -Attached Figure 6 The embodiment of the present invention provides a tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor, comprising the following steps: S1. Fix the triaxial acceleration sensor 4 to the inner surface of the crown of the tire 2. Then, the carcass of the tire 2 engages with the rim 3 to complete the installation of the tire 2 and the triaxial acceleration sensor 4. The radial acceleration, tangential acceleration, and lateral acceleration signals of the tire 2 are collected in real time. The triaxial acceleration sensor 4 is fixed to the inner surface of the tire crown using a high-temperature resistant adhesive or an embedded card slot structure to ensure synchronization with tread deformation. The sampling frequency of the triaxial acceleration sensor 4 is ≥1kHz, covering the ground deformation frequency of the tire 2 in the range of 0-200Hz. The rim 3 provides mechanical support, and the tire body engages with the rim 3 through the bead to prevent the triaxial acceleration sensor 4 from falling off due to centrifugal force. S2. Calculate the instantaneous rotational angular velocity of tire 2 based on the biased mean value of the radial acceleration, and obtain the ground contact linear velocity of tire 2 based on the angular velocity and the geometric parameters of tire 2; S3. Detecting peak points of the radial acceleration signal when tire 2 enters approach point 5 and leaves approach point 6, where approach point 5 and take-off point 6 are the starting and ending points of the contact area between tire 2 and road surface 1, and calculating a first contact patch length estimate based on the difference in the number of samples at the peak points, the sampling rate, and the contact line velocity. The contact zone is defined as the approach point 5 corresponding to the start of contact between tire 2 and road surface 1, and the departure point 6 corresponding to the end of contact, marked by the radial acceleration mutation point; S4. Performing Marr wavelet filtering on the tangential acceleration signal, detecting the peak sample number difference of the filtered signal, and calculating a second contact patch length estimate based on the sample number difference, the sampling rate, and the contact line velocity; S5. Determine the vehicle's driving state based on the lateral acceleration signal, and dynamically correct the first and second estimated values to obtain a final contact patch length; S6. Transmit the final contact patch length and tire 2 status parameters to the vehicle controller for vehicle control and early warning.

[0025] By fixing the triaxial accelerometer to the inner surface of the tire crown, the sensor synchronizes with tire deformation, capturing sudden changes in radial, tangential, and lateral acceleration during contact, providing raw data for subsequent calculations. The rim-engaging design enhances sensor installation stability and avoids the risk of falling off during high-speed rotation. The instantaneous rotational angular velocity is calculated using the mean radial acceleration bias, and the ground contact line velocity is directly derived by combining the tire geometric radius. This provides key kinematic parameters for ground contact patch length estimation, avoids reliance on external vehicle speed sensors, and reduces system complexity. By detecting the radial acceleration peaks at approach and departure points, marking the start and end moments of tire-road contact, and combining the sampling rate with the linear velocity to calculate a first estimate, the clear definition of the contact area improves detection accuracy and reduces environmental noise interference; Marr wavelet filtering effectively separates road noise from tangential acceleration signals, enhances peak characteristics in the contact area, and provides redundant verification through secondary estimates, compensating for the limitations of a single signal source and improving the robustness of the estimation results. Dynamically judge the curve or bump state based on lateral acceleration, adaptively adjust the weight ratio of radial and tangential estimation values, and trigger data re-collection in the event of bumps, ensuring the accuracy and real-time performance of estimation values under complex working conditions; The final contact patch length and tire status parameters are fed back to the on-board controller via low-latency wireless transmission, and the vehicle dynamics system is linked to optimize braking, drive and suspension control strategies in real time, improving driving safety and handling stability.

[0026] In step S2, the instantaneous rotation angular velocity is calculated by the formula Calculate, where is the mean radial acceleration bias, is the rotation radius of the inner surface of the tire 2 crown, and the ground contact line speed of tire 2 is calculated by the following formula calculate.

[0027] In step S3, the first contact patch length estimate is calculated using the following formula: ; in, and is the sample number of the radial acceleration peak point, is the sampling rate.

[0028] The time difference in the touchdown area is quantified by combining the peak point sample difference with the sampling rate, and high-resolution length estimation is achieved by combining the linear velocity. Based on the centrifugal force physical model and geometric relationship, motion parameters are directly derived from sensor data to avoid external input errors and simplify the calculation process.

[0029] In step S4, the basis function used in the Marr wavelet filtering is defined as: ; Where, is the outer waveform basis function of the wavelet, is the wavelet internal waveform basis function, where ,and and is a scale parameter that is adaptively adjusted according to vehicle operating conditions.

[0030] The external basis function suppresses high-frequency noise, the internal basis function enhances the characteristics of the contact area, and the adaptive scale parameter improves the adaptability of the filtering algorithm to different vehicle speeds.

[0031] In step S4, the second contact patch length estimate is calculated using the following formula: ; in, and is the sample number of the peak point of the tangential acceleration after filtering.

[0032] The filtered tangential acceleration peak sample difference is used to provide independent verification and reduce the accidental error of a single signal source.

[0033] In step S5, the dynamic correction is calculated using the following formula: ; in, and is the confidence weight based on the real vehicle data fitting, and satisfies , the confidence weight is obtained by fitting a large amount of real vehicle test data, is the correction factor that changes with the lateral acceleration value.

[0034] By fusing multi-source data through confidence weights and correction coefficients, and combining actual vehicle tests to optimize weight distribution, the estimation accuracy under complex working conditions is improved.

[0035] In step S3, the detection of the radial acceleration peak point is achieved by a local area peak search algorithm and is calculated using the following formula: ; in, and The preset upper and lower limits of the amplitude.

[0036] The preset amplitude upper and lower limits constrain the local peak search range, eliminate interference signals in non-ground areas, and improve detection reliability.

[0037] The three-axis acceleration sensor 4 integrates temperature and tire pressure detection modules, and transmits data to the vehicle controller in real time through the automotive-grade low-power Bluetooth protocol.

[0038] Integrated temperature and tire pressure detection provides multi-dimensional tire status input, and the automotive-grade low-power Bluetooth protocol ensures data real-time and transmission stability.

[0039] In step S5, when the amplitude of the lateral acceleration signal exceeds the preset bump threshold, the vehicle is determined to be in a bumpy state, and steps S3 to S4 are retriggered to obtain updated first and second contact patch length estimates, which replace the original estimates for dynamic correction.

[0040] Automatically re-collect data when lateral acceleration exceeds the threshold to avoid signal distortion caused by bumps and ensure the validity of the estimated value under extreme road conditions.

[0041] In step S6, the vehicle controller adjusts the vehicle braking, driving and suspension control parameters in real time according to the final contact patch length and in combination with the tire 2 dynamic model.

[0042] Dynamically optimizes braking pressure, driving force distribution and suspension stiffness based on the ground contact patch length to improve the vehicle's active safety performance.

[0043] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor, characterized in that: The following steps are involved: S1, fixing the triaxial acceleration sensor (4) to the inner surface of the crown of the tire (2), and then the tire (2) carcass is engaged with the rim (3), completing the installation of the tire (2) and the triaxial acceleration sensor (4), and collecting the radial acceleration, tangential acceleration signal and lateral acceleration signal of the tire (2) in real time; S2, calculating the instantaneous rotational angular velocity of the tire (2) based on the biased mean value of the radial acceleration, and obtaining the ground contact linear velocity of the tire (2) based on the angular velocity and the geometric parameters of the tire (2); S3, detecting the peak points of the radial acceleration signal when the tire (2) enters the approach point (5) and leaves the departure point (6), the approach point (5) and the departure point (6) being the starting and ending positions of the contact area between the tire (2) and the road surface (1), and calculating a first contact footprint length estimation value based on the peak point sample number difference, the sampling rate, and the ground contact line speed; S4. Performing Marr wavelet filtering on the tangential acceleration signal, detecting the peak sample number difference of the filtered signal, and calculating a second contact patch length estimate based on the sample number difference, the sampling rate, and the contact line velocity; S5. Determine the vehicle's driving state based on the lateral acceleration signal, and dynamically correct the first and second estimated values to obtain a final contact patch length; S6, transmitting the final contact patch length and tire (2) status parameters to an onboard controller for vehicle control and early warning.

2. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, characterized in that: In step S2, the instantaneous rotation angular velocity is calculated by the formula Calculate, where is the mean radial acceleration bias, is the rotation radius of the inner surface of the tire (2) crown, and the ground contact line speed of the tire (2) is calculated by the following formula calculate.

3. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, wherein: In step S3, the first contact patch length estimate is calculated using the following formula: ； in, and is the sample number of the radial acceleration peak point, is the sampling rate.

4. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, wherein: In step S4, the basis function used in the Marr wavelet filtering is defined as: ； Where, is the outer waveform basis function of the wavelet, is the wavelet internal waveform basis function, where ,and and is a scale parameter that is adaptively adjusted according to vehicle operating conditions.

5. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, wherein: In step S4, the second contact patch length estimate is calculated using the following formula: ； in, and is the sample number of the peak point of the tangential acceleration after filtering.

6. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, characterized in that: In step S5, the dynamic correction is calculated using the following formula: ； in, and is the confidence weight based on the real vehicle data fitting, and satisfies , the confidence weight is obtained by fitting a large amount of real vehicle test data, is the correction factor that changes with the lateral acceleration value.

7. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, characterized in that: In step S3, the detection of the radial acceleration peak point is achieved by a local area peak search algorithm and is calculated using the following formula: ； in, and The preset upper and lower limits of the amplitude.

8. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, wherein: The three-axis acceleration sensor (4) integrates temperature and tire pressure detection modules, and transmits data in real time to the vehicle controller via an automotive-grade low-power Bluetooth protocol.

9. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, wherein: In step S5, when the amplitude of the lateral acceleration signal exceeds a preset bump threshold, the vehicle is determined to be in a bumpy state, and steps S3 to S4 are retriggered to obtain updated first and second contact patch length estimates, which replace the original estimates for dynamic correction.

10. The tire contact patch length estimation method based on a vehicle-mounted tire acceleration sensor according to claim 1, characterized in that: In step S6, the vehicle controller adjusts the vehicle braking, driving and suspension control parameters in real time based on the final contact patch length and in combination with the tire (2) dynamics model.

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