Tire multi-condition dynamics parameter detection method and system

Abstract

The present application relates to the field of tire multi-working condition dynamics parameter detection technology, and more particularly to a tire multi-working condition dynamics parameter detection method and system, comprising collecting original signals inside the rim, wheel core acceleration signals and external six-component force data; establishing a reference angle domain coordinate to generate a synchronous analysis matrix; extracting a prior centrifugal load characteristic quantity and fitting a centrifugal bias baseline sequence; extracting a high-frequency nonlinear response component, a stick-slip high-frequency vibration energy ratio and a non-contact area segment signal variance; using the nonlinear increment change of the tire wall tension to reconstruct the order of rheological calculus and compensate for the amount of intermodulation distortion, to generate a net response signal sequence; constructing a time-varying reliability attenuation flow form constraint inversion weight to solve and generate tire body structure dynamic stiffness characteristic parameters. The present application solves the problem of nonlinear response intermodulation distortion caused by centrifugal bias drift and transient impact, and improves the inversion accuracy of dynamics parameters under complex excitation.

Description

Technical Field

[0001] This invention relates to the field of tire multi-condition dynamic parameter detection technology, and in particular to tire multi-condition dynamic parameter detection methods and systems. Background Technology

[0002] Existing tire multi-condition dynamic parameter testing technology is used to measure tire dynamic characteristics under various operating conditions. Tire dynamic parameters include lateral forces, longitudinal forces, and moments, which have a significant impact on vehicle handling stability and driving safety. Multi-condition testing covers different speeds, loads, and road conditions to simulate real-world driving environments. The testing technology typically relies on test benches or sensor systems to collect response data during tire movement, thereby quantifying dynamic behavior. By analyzing the measurement results, tire multi-condition dynamic parameter testing technology provides engineering development basis for optimizing tire design and vehicle control systems.

[0003] Existing tire multi-condition dynamic parameter detection technologies suffer from the following technical challenges: Specifically, due to the stringent real-time requirements of intelligent vehicle electronic stability control systems for road surface feedback at high speeds, the sensor elements installed inside the wheel rim experience significant centrifugal acceleration loads during high-speed rotation of the wheel axle. This causes microscopic deformation of the internal structure of the sensor elements, generating a quasi-static zero-point offset signal that fluctuates with rotational speed. Simultaneously, the tire contact patch experiences severe transient high-frequency pulse impacts when traversing uneven surfaces at high speeds or undergoing sharp turns, triggering significant nonlinear mechanical responses in the sensor elements. The quasi-static offset caused by centrifugal force and the contact patch... The dynamic nonlinear signals caused by ground impacts exhibit deep spectral overlap and intermodulation distortion in the physical dimension. This makes it impossible for data processing algorithms to effectively filter out environmental interference components from complex composite signal streams while maintaining a high sampling frequency. Consequently, the digital twin model lacks accurate data support during the fine calibration of parameters. For example, when an autonomous vehicle performs emergency obstacle avoidance maneuvers on icy and slippery roads, the aforementioned intermodulation signals may be misinterpreted as elastic deformation characteristics of the tire structure and interfere with the control system's judgment on the dynamic load distribution between wheels. Ultimately, this leads to a serious deviation of the actual vehicle dynamic response matching results from the true physical limits, greatly reducing the accuracy of the control algorithm's prediction of extreme conditions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for detecting tire dynamic parameters under multiple operating conditions. This invention solves the technical problem that the static offset drift of the sensing element caused by the dynamic centrifugal torque generated by the high-speed rotation of the sensor with the wheel rim and the nonlinear response caused by the transient impact of the tire contact surface result in intermodulation distortion, making it difficult to accurately extract the true dynamic stiffness parameters of the tire carcass structure under complex operating conditions of continuous frequency and load variation.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: In a first aspect, the tire multi-condition dynamic parameter detection method provided by the present invention includes: Step 1: Collect the raw signals output by the sensing elements inside the tire rim, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing elements inside the rim, and the test data of the six components of the tire's external force. Step 2: Establish reference angular domain coordinates based on the tire angular position signal, map the original signal to the reference angular domain coordinates to generate a synchronization analysis matrix; calculate the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extract the low-frequency quasi-static component and tire non-contact segment signal from the synchronization analysis matrix, and use the tire non-contact segment signal to fit the low-frequency quasi-static component to generate a centrifugal offset baseline sequence. Step 3: Extract the tire contact area segment signal from the synchronization analysis matrix, extract the high-frequency nonlinear response component from the tire contact area segment signal, calculate the high-frequency nonlinear response component to generate the stick-slip high-frequency vibration energy ratio of the contact area segment, and calculate the tire non-contact area segment signal variance from the tire non-contact area segment signal. Step 4: Subtract the centrifugal bias baseline sequence from the original signal to generate a first-order debiased signal; use the prior centrifugal load characterization quantity to calculate the nonlinear increment of the tire wall tension; use the nonlinear increment of the tire wall tension to change the rheological calculus order to reconstruct the cross-modulation distortion compensation quantity; use the cross-modulation distortion compensation quantity to correct the first-order debiased signal to generate a net response signal sequence. Step 5: Calculate the time-varying reliability decay manifold using the variance of the signal in the unconnected section and the energy ratio of the stick-slip high-frequency vibration in the connected section. Use the time-varying reliability decay manifold to constrain the net response signal sequence and the tire external six-component force test data to solve and generate the dynamic stiffness characterization parameters of the tire carcass structure.

[0006] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention collects the original signal output by the sensing element inside the rim corresponding to the tire, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing element inside the rim, and the tire external six-component force test data, including: The normal sensing axis of the rim-interior sensing element installed on the inner surface of the rim is set to be parallel to the radial ray outward from the center of the rim, and the tangential sensing axis of the rim-interior sensing element is set to be perpendicular to the tangential direction of the tire contact surface. Discrete digital signals are sampled using the internal sensing element of the wheel rim, and low-pass filtering is performed on the discrete digital signals to generate the original signal and the wheel center acceleration signal.

[0007] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention establishes a reference angular domain coordinate based on the tire angular position signal, and maps the original signal to the reference angular domain coordinate to generate a synchronization analysis matrix, including: The transient angular velocity is obtained by extracting the pulse change points in the tire speed signal, and the original signal is converted into an angular domain periodic response sequence using the transient angular velocity; The transmission delay of the angular domain periodic response sequence is evaluated using a dynamic time warping algorithm, and the phase of the angular domain periodic response sequence is aligned using a lead phase compensation operator to generate the synchronization analysis matrix.

[0008] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention calculates the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extracts low-frequency quasi-static components and tire off-hook segment signals from the synchronization analysis matrix, and uses the tire off-hook segment signals to fit the low-frequency quasi-static components to generate a centrifugal bias baseline sequence, including: The tire non-contact section signal is extracted from the synchronization analysis matrix, and the radial force equivalent value of the wheel center acceleration signal is calculated using the tire speed signal to generate the a priori centrifugal load characterization quantity. The low-frequency quasi-static components are fitted with basis functions using the Fourier series expansion method, and the fitting results are extended to the full-cycle angle range to generate the centrifugal bias baseline sequence.

[0009] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention extracts the tire contact area segment signal from the synchronous analysis matrix, extracts high-frequency nonlinear response components from the tire contact area segment signal, calculates the high-frequency nonlinear response components to generate the contact area segment stick-slip high-frequency vibration energy ratio, and calculates the tire non-contact area segment signal to generate the non-contact area segment signal variance, including: The tire contact zone signal is located from the synchronous analysis matrix by setting a strain pulse threshold crossing condition. The tire contact zone signal is decomposed by time-frequency transformation to generate the high-frequency nonlinear response component. The high-frequency nonlinear response component is integrated in the whole time domain to generate the stick-slip high-frequency vibration energy ratio of the contact zone. The variance of the tire non-contact zone signal is generated by performing variance calculation on the tire non-contact zone signal.

[0010] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention utilizes the prior centrifugal load characterization quantity to generate a nonlinear increment of tire sidewall tension, and utilizes the nonlinear increment of tire sidewall tension to change the rheological calculus order to reconstruct the intermodulation distortion compensation quantity, including: The a priori centrifugal load characterization is substituted into the nonlinear stiffness hardening mapping function to generate the nonlinear increment of the tire wall tension. The rheological calculus order in the fractional-order viscoelastic hysteresis constitutive model is updated by using the nonlinear increment of the tire wall tension to generate the updated fractional-order viscoelastic hysteresis constitutive model. The impact shear strain characteristic quantity is extracted from the high-frequency nonlinear response component. The impact shear strain characteristic quantity is then subjected to calculus operations using the updated fractional-order viscoelastic hysteresis constitutive model to generate a multiplicative physical coupling term. This multiplicative physical coupling term is then determined as the intermodulation distortion compensation quantity.

[0011] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention, which uses the cross-modulation distortion compensation amount to correct the first-order debiased signal to generate a net response signal sequence, includes: Using the high-frequency vibration energy ratio of the junction area as a physical confidence factor, the effect gain of the intermodulation distortion compensation amount is dynamically adjusted to generate an adaptive compensation vector. The historical strain hysteresis features of the tire carcass structure are extracted using the updated fractional-order viscoelastic hysteresis constitutive model. The adaptive compensation vector and the first-order debiased signal are then subjected to nonlinear residual mapping based on the historical strain hysteresis features. Intermodulation distortion residuals are filtered out to generate the net response signal sequence.

[0012] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention utilizes the signal variance of the non-contact section and the ratio of stick-slip high-frequency vibration energy of the contact section to generate a time-varying reliability decay manifold, including: The signal variance of the unconnected section and the high-frequency vibration energy ratio of the stick-slip section are normalized to generate normalized dual parameters. The normalized dual parameters are substituted into the exponential decay surface function to generate a time-varying confidence penalty weight matrix, and the time-varying confidence penalty weight matrix is ​​determined as the time-varying confidence decay manifold.

[0013] Furthermore, the tire multi-condition dynamic parameter detection method of the present invention utilizes the time-varying reliability decay manifold constraint on the net response signal sequence and the tire external six-component force test data to solve for and generate tire carcass structure dynamic stiffness characterization parameters, including: Establish a state-space observation model with the net response signal sequence and the test data of the six external forces of the tire as inputs and the dynamic stiffness characterization parameters of the tire carcass structure as outputs. The time-varying reliability decay manifold is substituted into the objective cost function of the state-space observation model, and gradient descent iterative operation is performed to solve for the dynamic stiffness characterization parameters of the tire carcass structure. The dynamic stiffness characterization parameters of the tire carcass structure are then substituted into the electronic stability control model to generate the dynamic response matching results.

[0014] Secondly, the tire multi-condition dynamic parameter detection system provided by the present invention is applied to the tire multi-condition dynamic parameter detection method as described above, including: The data acquisition module is used to collect the original signals output by the sensing elements inside the tire rim, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing elements inside the rim, and the tire external six-component force test data. The coordinate alignment module is used to establish a reference angular domain coordinate based on the tire angular position signal, and to map the original signal to the reference angular domain coordinate to generate a synchronization analysis matrix; The baseline extraction module is used to calculate the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extract low-frequency quasi-static components and tire off-hook section signals from the synchronization analysis matrix, and use the tire off-hook section signals to fit the low-frequency quasi-static components to generate a centrifugal bias baseline sequence. The feature recognition module is used to extract tire contact area segment signals from the synchronization analysis matrix, extract high-frequency nonlinear response components from the tire contact area segment signals, and calculate and generate the stick-slip high-frequency vibration energy ratio of the contact area segment and the variance of the non-contact area segment signals. The physical reconstruction module is used to subtract the centrifugal bias baseline sequence from the original signal to generate a first-order debiased signal, calculate the nonlinear increment of the tire wall tension using the prior centrifugal load characterization quantity, change the rheological calculus order using the nonlinear increment of the tire wall tension to reconstruct the intermodulation distortion compensation quantity, and correct the first-order debiased signal to generate a net response signal sequence. The parameter inversion module is used to generate a time-varying reliability decay manifold by calculating the signal variance of the unconnected section and the stick-slip high-frequency vibration energy ratio of the connected section, and to use the time-varying reliability decay manifold to constrain the net response signal sequence and the tire external six-component force test data to solve and generate the dynamic stiffness characterization parameters of the tire carcass structure.

[0015] Beneficial effects of this invention: The tire multi-condition dynamic parameter detection method provided by this invention, by adopting an orthogonal offset mounting layout for the sensing elements inside the rim, can isolate the high-speed rotating centrifugal force vector and the tangential impact vector of the road contact surface from the physical source, thereby effectively blocking the interference of macroscopic contact patch impact on the pure centrifugal observation channel. By introducing a tension-modulated fractional-order viscoelastic hysteresis constitutive model, the nonlinear increment of the tire sidewall tension is visualized as a dynamically adjustable parameter of the rheological calculus order. This enables accurate reconstruction of the nonlinear hardening effect of rubber material under centrifugal stretching, solving the problem that conventional linear models cannot isolate deep physical intermodulation distortion. A two-dimensional time-varying confidence decay manifold constructed using the dual parameters of the non-contact area signal variance and the stick-slip high-frequency vibration energy ratio of the contact area endows the parameter inversion algorithm with the ability to adaptively adjust the confidence weight according to the complexity of the working conditions. Even under extreme boundary conditions where the signal-to-noise ratio of the sensing signal fluctuates drastically on icy and slippery roads, the convergence and robustness of the iterative solution of the state-space observation model can still be maintained. This closed-loop technology, based on deep decoupling of physical fields and dynamic constraints of data credibility manifolds, can more realistically reflect the dynamic stiffness characterization parameters of tire carcass structure, providing underlying data support with full physical domain fidelity for high-order slip ratio control strategies and digital twin model calibration of electronic stability control systems. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of the tire multi-condition dynamic parameter detection method of the present invention. Detailed Implementation

[0018] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0019] Firstly, please refer to Figure 1 The tire multi-condition dynamic parameter detection method provided by the present invention includes: Step 1: Collect the raw signals output by the sensing elements inside the tire rim, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing elements inside the rim, and the test data of the six components of the tire's external force. Step 2: Establish reference angular domain coordinates based on the tire angular position signal, map the original signal to the reference angular domain coordinates to generate a synchronization analysis matrix; calculate the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extract the low-frequency quasi-static component and tire non-contact segment signal from the synchronization analysis matrix, and use the tire non-contact segment signal to fit the low-frequency quasi-static component to generate a centrifugal offset baseline sequence. Step 3: Extract the tire contact area segment signal from the synchronization analysis matrix, extract the high-frequency nonlinear response component from the tire contact area segment signal, calculate the high-frequency nonlinear response component to generate the stick-slip high-frequency vibration energy ratio of the contact area segment, and calculate the tire non-contact area segment signal variance from the tire non-contact area segment signal. Step 4: Subtract the centrifugal bias baseline sequence from the original signal to generate a first-order debiased signal; use the prior centrifugal load characterization quantity to calculate the nonlinear increment of the tire wall tension; use the nonlinear increment of the tire wall tension to change the rheological calculus order to reconstruct the cross-modulation distortion compensation quantity; use the cross-modulation distortion compensation quantity to correct the first-order debiased signal to generate a net response signal sequence. Step 5: Calculate the time-varying reliability decay manifold using the variance of the signal in the unconnected section and the energy ratio of the stick-slip high-frequency vibration in the connected section. Use the time-varying reliability decay manifold to constrain the net response signal sequence and the tire external six-component force test data to solve and generate the dynamic stiffness characterization parameters of the tire carcass structure.

[0020] The method for detecting tire dynamic parameters under multiple operating conditions requires real-time, multi-dimensional sensing from the physical signal source. Establishing a sensing system necessitates arranging sensing elements within the corresponding rim of the tire; these elements are specifically multi-axis microelectromechanical (MEMS) sensing components. Through a specific physical spatial layout, the normal sensing axis of the MEMS sensing component mounted on the inner surface of the rim is set parallel to the radial ray extending outward from the rim center, while the tangential sensing axis is simultaneously set perpendicular to the tangent direction of the tire's contact patch. After completing the hardware spatial decoupling layout, the MEMS sensing component continuously samples discrete digital signals. The system performs anti-aliasing low-pass filtering to filter out electromagnetic interference noise from the environment, generating raw signals with uniform timestamps and wheel center acceleration signals. Simultaneously, it acquires tire speed signals, tire angular position signals, and tire external six-component force test data recorded by external devices. The tire external six-component force test data covers multi-dimensional macroscopic force state parameters such as lateral force, longitudinal force, vertical force, self-aligning torque, rollover torque, and rolling resistance torque experienced by the tire on the test bench.

[0021] The collected test data is located on an independent time axis, resulting in inconsistent sampling frequencies and transmission delays. The system extracts pulse abrupt changes in the tire rotation speed signal as baseline trigger events and calculates the transient angular velocity using the time interval between adjacent pulse abrupt changes. The transient angular velocity is then used to convert the original time-domain signal into an angular-domain periodic response sequence indexed by the rotation angle. A dynamic time warping algorithm is used to evaluate the transmission delay of the angular-domain periodic response sequence across different sampling channels. A phase-advancement compensation operator is used to align the phase of the angular-domain periodic response sequence, generating a synchronization analysis matrix with uniform dimensions. After completing the spatiotemporal mapping, low-frequency quasi-static components and tire non-contact segment signals are extracted from the synchronization analysis matrix. The tire non-contact segment signal represents the undisturbed initial state when the tire has rotated to its highest point and is not in contact with the road surface. The wheel center acceleration signal is calculated to generate a priori centrifugal load characterization reflecting the tire's high-speed rotation tension state. Combining the priori centrifugal load characterization, Fourier series expansion is used to fit the basis functions of the low-frequency quasi-static components corresponding to the tire non-contact segment signal. The fitted basis functions are extended to the full-cycle angle range to generate a centrifugal bias baseline sequence that reflects the dynamic fluctuations with rotational speed.

[0022] Deconstructing low-frequency structural responses is fundamental to in-depth micromechanical analysis. In applications where intelligent vehicles perform emergency obstacle avoidance maneuvers on icy and slippery roads, the tire contact patch experiences rapid transient high-frequency pulse impacts. The system sets strain pulse threshold crossing conditions to locate the tire contact patch signal where severe deformation occurs from the synchronous analysis matrix. Continuous time-frequency transformation decomposition is performed on the tire contact patch signal to extract high-frequency nonlinear response components reflecting the micro-excitation frequency of the road surface. These high-frequency nonlinear response components characterize the complex frictional slip modes occurring within the contact patch between the rubber tread and the road surface. Full-time-domain integration is performed on the high-frequency nonlinear response components to generate the stick-slip high-frequency vibration energy ratio of the contact patch, characterizing the degree of micro-slip peeling of the tread rubber under complex adhesion boundaries. Simultaneously, signals from the tire's non-contact patch, unaffected by ground impacts, are extracted, and statistical variance calculations are performed to generate the variance of the non-contact patch signal, characterizing the mechanical vibration noise background of the rim body.

[0023] The quasi-static offset caused by centrifugal force and the dynamic nonlinear signal caused by the impact on the ground surface exhibit deep intermodulation distortion in the physical dimension. The fitted centrifugal offset baseline sequence is subtracted from the original signal to generate a first-order debiased signal that filters out static drift. The prior centrifugal load characterization is substituted into the nonlinear stiffness hardening mapping function to solve for the nonlinear increment of the tire wall tension, characterizing the tension of the internal skeleton material during high-speed tire rotation. This nonlinear increment of the tire wall tension is introduced into a fractional-order viscoelastic hysteresis constitutive model as a dynamic tuning parameter to update the rheological calculus order within the model in real time. The impact shear strain characteristic quantity, representing the transient impact intensity of the road surface, is extracted from the high-frequency nonlinear response components. The impact shear strain characteristic quantity is then subjected to calculus operations using the updated rheological calculus order fractional-order viscoelastic hysteresis constitutive model to generate a multiplicative physical coupling term that deeply integrates the centrifugal hardening effect and the transient impact effect. The multiplicative physical coupling term is determined as the intermodulation distortion compensation amount. The intermodulation distortion compensation amount is subtracted from the first-order debiased signal to generate a net response signal sequence stripped of intermodulation interference.

[0024] Under extreme conditions, the signal-to-noise ratio (SNR) can fluctuate drastically, posing a risk of divergence distortion to conventional static weighted parameter inversion. The system normalizes the signal variance in unconnected sections and the high-frequency vibration energy ratio of stick-slip sections in connected sections, generating normalized dual parameters with uniform dimensions. These normalized dual parameters are then substituted into a pre-defined exponential decay surface function to generate a time-varying reliability penalty weight matrix that dynamically fluctuates with the road surface adhesion state. A state-space observation model is established, with the input being the net response signal sequence and the tire's external six-component force test data, and the output being unknown parameters. The time-varying reliability penalty weight matrix is ​​defined as a time-varying reliability decay manifold, which is then substituted into the objective cost function of the state-space observation model. Under the dynamic constraints of the time-varying reliability decay manifold, the system performs gradient descent iterative calculations to solve for and generate dynamic stiffness characterization parameters of the tire carcass structure, including the lateral stiffness increment, longitudinal stiffness change, and hysteresis damping coefficient. Substitute the dynamic stiffness characterization parameters of the tire carcass structure into the electronic stability control model, update the underlying tire lateral stiffness prediction lookup table, and output the dynamic response matching results that correct the vehicle yaw rate deviation.

[0025] The physical sensing layer for establishing a multi-condition tire dynamic parameter detection method requires deploying a highly sensitive sensor array inside the rim. The normal sensing axis of the rim-internal sensing element, mounted on the inner surface of the rim, is adjusted to be parallel to the radial ray direction outward from the rim center. Simultaneously, the tangential sensing axis of the rim-internal sensing element is set perpendicular to the tangent direction of the tire contact surface. After completing the spatial orthogonal isolation configuration of the sensing axes, discrete digital signals are continuously read using the rim-internal sensing element according to a preset sampling frequency. Discrete digital signals are data sets composed of multi-dimensional minute voltage and charge pulse data, affected by high-frequency electromagnetic environmental noise generated by motor operation and road friction. Low-pass filtering is performed on the discrete digital signals to filter out environmental noise and mechanical structure resonance clutter, generating a raw signal and a wheel center acceleration signal with uniform timestamp alignment characteristics.

[0026] In business application scenarios where vehicles perform emergency lane changes or start on icy or slippery roads, wheel speeds fluctuate drastically, causing the raw signals acquired in the time dimension to exhibit non-stationary distortion. Pulse abrupt changes in the tire speed signal are extracted as reference points, and transient angular velocities are calculated using the time intervals between these pulse abrupt changes. A resampling sequence with equal angular intervals is constructed using the transient angular velocities, converting the raw signals in the time dimension into angular domain periodic response sequences in the spatial dimension. A dynamic time warping algorithm is used to compare the transmission delay differences between the angular domain periodic response sequences and the data stream from the external testing equipment. The tire external force test data output by the external testing equipment specifically includes the longitudinal force, lateral force, vertical force, and corresponding rollover torque, rolling resistance torque, and self-aligning torque experienced by the tire on the test bench. A leading phase compensation operator is used to align the phase boundaries of the angular domain periodic response sequences, eliminating time asynchrony errors in the distributed acquisition system and generating a synchronization analysis matrix that includes alignment attributes of multiple physical feature dimensions.

[0027] During high-speed rotation, the tire periodically detaches from the ground contact area. At its highest point, the tire material experiences only centrifugal stretching and not ground compression. Signals from the tire's non-contact sections corresponding to these detached areas are extracted from the synchronization analysis matrix. The radial force equivalent of the wheel center acceleration signal is calculated using the tire rotation speed signal, generating a priori centrifugal load characterization reflecting the high-speed rotational tension state of the tire structure. Slowly fluctuating low-frequency quasi-static components are extracted from the tire's non-contact section signals, and basis functions are fitted using Fourier series expansion. The fitted basis functions are extended along the 360-degree spatial span of the entire wheel rotation to the full-cycle angular range, generating a centrifugal offset baseline sequence reflecting the zero-point drift trend of continuous rotation.

[0028] The microscopic frictional behavior of tire tread rubber within the contact patch is the core physical evolution process determining the vehicle's adhesion limit. By setting a strain pulse threshold crossing condition characterizing the intense deformation of the rubber entering the contact patch region, the tire contact area signal where severe compression deformation occurs is precisely located from the synchronous analysis matrix. Time-frequency transformation decomposition is performed on the tire contact area signal to separate the high-frequency nonlinear response component representing the high-frequency excitation force of rough road particles. Full-time-domain integration is performed on the high-frequency nonlinear response component to generate the stick-slip high-frequency vibration energy ratio of the contact area, characterizing the intensity of the alternating adhesion and slippage of the microscopic rubber blocks. Statistical variance calculation is performed on the tire non-contact area signal in a suspended, unloaded state to generate the variance of the non-contact area signal reflecting the system's background white noise amplitude and the inherent mechanical vibration of the rim.

[0029] The enormous centrifugal torque generated by high-speed rotation stretches the tire carcass cords, causing dynamic hardening of the constitutive parameters of the rubber material. This leads to deep nonlinear intermodulation interference with transient impact signals from the road surface. By substituting the prior centrifugal load characterization into the nonlinear stiffness hardening mapping function for inversion calculation, a nonlinear increment of the tire wall tension, characterizing the tension of the tire cord layer as a function of centrifugal force, is generated. The nonlinear increment of the tire wall tension is used to dynamically adjust the rheological calculus order within the fractional-order viscoelastic hysteresis constitutive model, generating an updated fractional-order viscoelastic hysteresis constitutive model with adaptive dynamic hardening capabilities. Impact shear strain characteristics reflecting the transient contact compression deformation intensity are extracted from the high-frequency nonlinear response components. The updated fractional-order viscoelastic hysteresis constitutive model is used to perform calculus operations on the impact shear strain characteristics, generating a multiplicative physical coupling term that deeply integrates the dual physical coupling effects of centrifugal hardening and contact impact. This multiplicative physical coupling term is determined as the intermodulation distortion compensation quantity for subsequent error separation.

[0030] Simple algebraic subtraction cannot completely eliminate the residual error caused by the inherent hysteresis memory effect of rubber viscoelastic materials. Using the high-frequency vibration energy ratio of the contact patch as a physical confidence factor reflecting the severity of the current contact patch friction state, and combining this with the dynamic adjustment of the cross-modulation distortion compensation gain based on the physical confidence factor, an adaptive compensation vector that fluctuates in real time with road conditions is generated. The centrifugal bias baseline sequence is subtracted from the original signal to generate a first-order debiased signal filtered out of static drift. The updated fractional-order viscoelastic hysteresis constitutive model is used to extract the historical strain hysteresis characteristics of the tire carcass structure remaining in the previous rotation cycle. A nonlinear residual mapping based on the historical strain hysteresis characteristics is performed between the adaptive compensation vector and the first-order debiased signal to filter out the deep physical dimension of cross-modulation distortion residues, generating a net response signal sequence completely stripped of background interference and coupling distortion.

[0031] Under low-adhesion extreme test conditions such as icy and slippery road surfaces, the sensor is subjected to severe high-frequency excitation, which can cause distortion and failure of some signal channels. The signal variance of the non-contact section and the high-frequency vibration energy ratio of the contact section are normalized to generate normalized dual parameters that eliminate dimensional differences. These normalized dual parameters are then substituted into a preset exponential decay surface function to generate a time-varying reliability penalty weight matrix that dynamically changes with noise amplitude and micro-slippage degree. This time-varying reliability penalty weight matrix is ​​defined as a time-varying reliability decay manifold that limits interference from erroneous data. A state-space observation model is established with the net response signal sequence and tire external six-component force test data as inputs and unknown structural parameters as outputs. The time-varying reliability decay manifold is substituted into the objective cost function of the state-space observation model, and multidimensional gradient descent iterative calculations are performed on the three-dimensional surface of the loss function. By generating dynamic stiffness characterization parameters of the tire carcass structure that reflect the real physical characteristics through multiple rounds of iterative convergence, the dynamic stiffness characterization parameters of the tire carcass structure are substituted into the chassis electronic stability control model to reshape the query matrix of the vehicle slip angle prediction module and generate dynamic response matching results that guide the precise distribution of independent braking torque of the wheels.

[0032] In real-world testing of intelligent vehicle electronic stability control systems for emergency obstacle avoidance on icy and slippery roads, the massive centrifugal acceleration load caused by the high-speed rotation of the wheels and the high-frequency pulse impact from uneven road surfaces simultaneously act on the measurement system. To prevent cross-interference between centrifugal force and road impact force at the physical measurement source, a spatial orthogonal decoupling layout of the sensors is required. The normal sensing axis of the rim-internal sensing element, mounted on the inner surface of the rim, is set parallel to the radial ray outward from the rim center. Simultaneously, the tangential sensing axis of the rim-internal sensing element is set perpendicular to the tangent direction of the tire contact surface. After completing the hardware spatial orthogonal isolation configuration, discrete digital signals are sampled using the rim-internal sensing element. Low-pass filtering is performed on the discrete digital signals to remove high-frequency electrical noise and generate the original signal and wheel center acceleration signal. Simultaneously, tire speed signals, tire angular position signals, and external six-component force test data, including multi-dimensional macroscopic force states, are acquired.

[0033] The acquired independent data streams suffer from severe phase deviations due to differences in sampling frequency and signal transmission delays. Transient angular velocities are calculated by extracting pulse abrupt changes in the tire speed signal. These transient angular velocities are then used to convert the time-indexed raw signal into an angular domain periodic response sequence based on rotation angle. A dynamic time warping algorithm is employed to evaluate the transmission delay of the angular domain periodic response sequence. A leading phase compensation operator is then used to align the phase generation of the angular domain periodic response sequence, resulting in a synchronization analysis matrix that is perfectly aligned in both time and spatial dimensions. After coordinate system mapping and alignment, the quasi-static zero-point offset signal generated by the high-speed wheel rotation needs to be removed. Signals from the tire's non-contact area, unaffected by ground pressure, are extracted from the synchronization analysis matrix. The radial force equivalent value of the wheel center acceleration signal is calculated using the tire speed signal to generate a priori centrifugal load characterization. Low-frequency quasi-static components, including those with a slow drift trend, are extracted from the synchronization analysis matrix. Fourier series expansion is used to fit the basis functions of these low-frequency quasi-static components, and the fitting results are extended to the entire periodic angle range to generate a centrifugal offset baseline sequence covering the complete rotation cycle.

[0034] After stripping away background drift, in-depth analysis of the microscopic frictional evolution within the tire contact patch is key to overcoming the bottleneck in extreme adhesion state assessment. By setting strain pulse threshold crossing conditions, the tire contact patch signal exhibiting drastic physical deformation is located from the synchronous analysis matrix. Time-frequency transformation decomposition of the tire contact patch signal generates high-frequency nonlinear response components characterizing road surface vibration properties. Full-time domain integration of these high-frequency nonlinear response components generates the stick-slip high-frequency vibration energy ratio of the contact patch, quantifying the alternating intensity of microscopic rubber block adhesion and slippage. For the non-grounded state signal not entering the contact patch, variance calculation is performed on the tire non-contact patch signal to generate the variance of the non-contact patch signal characterizing the inherent mechanical vibration background noise of the wheel rim.

[0035] The quasi-static offset caused by centrifugal force and the dynamic nonlinear signal brought about by the impact on the ground surface will undergo deep intermodulation distortion in the actual physical dimension. Conventional linear algebraic subtraction cannot cope with the stiffness hardening phenomenon caused by centrifugal stretching of rubber materials. The centrifugal bias baseline sequence is subtracted from the original signal to generate a first-order debiased signal that filters out static drift. The a priori centrifugal load characterization quantity is substituted into the nonlinear stiffness hardening mapping function to generate the nonlinear increment of the tire wall tension characterizing the tension of the cord layer. The nonlinear increment of the tire wall tension is used to update the rheological calculus order in the fractional-order viscoelastic hysteresis constitutive model to generate an updated fractional-order viscoelastic hysteresis constitutive model with dynamic hardening adaptive capability. The impact shear strain characteristic quantity reflecting the contact extrusion depth is extracted from the high-frequency nonlinear response components. The updated fractional-order viscoelastic hysteresis constitutive model is used to perform calculus on the impact shear strain characteristic quantity to generate a multiplicative physical coupling term, which is determined as the intermodulation distortion compensation quantity.

[0036] Considering the inherent hysteresis memory characteristics of rubber materials, the ratio of stick-slip high-frequency vibration energy in the joint area is used as a physical confidence factor to dynamically adjust the effect gain of the intermodulation distortion compensation amount to generate an adaptive compensation vector. The updated fractional-order viscoelastic hysteresis constitutive model is used to extract the historical strain hysteresis features remaining in the tire carcass structure during the previous rotation cycle. The adaptive compensation vector and the first-order debiased signal are then subjected to a nonlinear residual mapping based on these historical strain hysteresis features. This process filters out intermodulation distortion residues, generating a net response signal sequence completely free from environmental interference. The specific physical implementation steps of the system's nonlinear residual mapping are as follows: the arithmetic unit multiplies the adaptive compensation vector by a forgetting factor of the historical strain hysteresis features. After obtaining the multiplication result, the arithmetic unit subtracts the result from the first-order debiased signal to filter out intermodulation distortion residues. The arithmetic unit obtains the forgetting factor by calculating the natural exponential decay value of the time difference between the current moment and the corresponding sampling point in the previous rotation cycle. The system sets the range of the first-order linear stiffness hardening coefficient k1 to 1.2 to 1.5, the range of the second-order nonlinear stiffness hardening coefficient k2 to 0.05 to 0.15, the first preset attenuation control factor λ1 to 0.8, and the second preset attenuation control factor λ2 to 1.2. To address the potential distortion risk in some channels under extreme testing conditions, the signal variance in unconnected sections and the high-frequency vibration energy ratio of stick-slip sections in connected sections are normalized to generate normalized dual parameters. These normalized dual parameters are then substituted into an exponential decay surface function to generate a time-varying reliability penalty weight matrix. This time-varying reliability penalty weight matrix is ​​then defined as the time-varying reliability decay manifold that limits interference from erroneous data.

[0037] A state-space observation model is established, with the input being the net response signal sequence and the test data of the six external forces on the tire, and the output being the dynamic stiffness characterization parameters of the tire carcass structure. The time-varying reliability decay manifold is substituted into the objective cost function of the state-space observation model, and gradient descent iterative operations are performed on the error surface to generate the dynamic stiffness characterization parameters of the tire carcass structure that truly reflect the tire's mechanical properties. These dynamic stiffness characterization parameters are then substituted into the electronic stability control model to generate dynamic response matching results that guide the independent braking torque distribution of the wheels.

[0038] In this specific implementation, the sensing elements inside the wheel rim employ a multi-axis microelectromechanical sensing assembly, arranged on the inner surface of the rim to capture force characteristics throughout the entire cycle. The assembly integrates a high-precision triaxial accelerometer and gyroscope, capable of simultaneously acquiring radial acceleration, circumferential acceleration, and angular velocity fluctuations during tire rotation. The normal sensing axis is absolutely parallel to the radial rays extending outward from the center of the rim and is fixed by a wedge-shaped mounting base to achieve physical isolation of the force vector. The tangential sensing axis is strictly perpendicular to the tangential direction of the tire's contact patch, used to decouple the wheel's rotational inertia from the ground frictional excitation force.

[0039] The raw signal is a discrete digital signal sequence output by the sensing element inside the rim at a fixed sampling frequency, including voltage pulses and frequency fluctuation components characterizing the stress state of the tire carcass. The data is presented as a voltage amplitude sequence after anti-aliasing low-pass filtering, reflecting the microscopic deformation of the tire's internal skeleton under complex road surface excitation. The underlying content includes the strain charge changes sensed by the bridge circuit inside the sensing element, representing the preliminary conversion result from physical quantity to electrical signal.

[0040] The tire external six-component force test data reflects the set of macroscopic mechanical characteristics output by the test platform, including forces acting in three mutually perpendicular directions: longitudinal force, lateral force, and vertical force. The dataset also synchronously includes torques along three rotational axes: rollover torque, rolling resistance torque, and self-aligning torque. The data provides a vehicle-level mechanical reference under real physical limits, aiding in establishing a closed-loop mapping between microscopic sensor signals and macroscopic force states.

[0041] The synchronization analysis matrix is ​​represented as a structured data warehouse after time axis alignment, phase compensation, and angular domain resampling. Each row of the matrix corresponds to a specific absolute tire rotation angle, and each column corresponds to specific physical observation dimensions such as the raw signals output by the sensors inside the rim, the six-component force test data of the tire's exterior, and the tire speed signal. The synchronization analysis matrix eliminates the time offset between distributed acquisition systems, providing a spatiotemporally consistent data foundation for subsequent decoupling operations.

[0042] The a priori centrifugal load characterization is calculated based on the wheel center acceleration signal and the tire rotation speed signal, reflecting the steady-state centrifugal acceleration amplitude generated by the high-speed rotation of the wheel on the sensing element. The quantification results reveal the degree of static deformation of the sensing element structure caused by high-speed rotation, which is used to correct the zero-point bias effect induced by dynamic centrifugal torque.

[0043] The centrifugal bias baseline sequence is extracted through Fourier series fitting and serves as a periodic low-frequency trend curve to characterize the background drift trajectory of the output signal from the sensing elements inside the rim during rim rotation. The sequence construction logic utilizes the inherent stability of the signal in the tire's non-contact area to completely separate the quasi-static offset signal from the composite original signal. The sequence can cover the entire rotation cycle, ensuring that the original signal at each moment can find an accurate mechanical zero-point reference.

[0044] The stick-slip high-frequency vibration energy ratio in the contact patch reflects the energy weighting index of the micro-friction evolution within the contact patch between the rubber tread and the road surface. Its features are extracted from high-frequency nonlinear response components, quantifying the high-frequency excitation energy density of the tread rubber block during the adhesion and slip transition process. As a fundamental parameter for evaluating tire adhesion performance, the stick-slip high-frequency vibration energy ratio in the contact patch can sensitively detect subtle changes in road surface adhesion characteristics, providing a priori predictive basis for slip ratio control.

[0045] The nonlinear increment of tire sidewall tension is calculated as a priori centrifugal load characterization, revealing the prestress changes in the internal tire carcass cords under centrifugal load during high-speed tire rotation. The nonlinear increment of sidewall tension directly leads to dynamic hardening of the tire carcass structure stiffness, altering the material's damping feedback characteristics to external impacts. The data state serves as a dynamic tuning parameter in subsequent rheological model calibration, enabling physical layer reconstruction of the cross-modulation distortion compensation.

[0046] A fractional-order viscoelastic hysteresis constitutive model describes the dynamic response characteristics of tire rubber materials, introducing fractional-order calculus operators to characterize the hysteresis effect and memory properties of the material. The order of the rheological calculus within the model is not constant, but rather a time-varying variable modulated by the nonlinear increment of the tire wall tension. This fractional-order viscoelastic hysteresis constitutive model can establish a mapping relationship between impact shear strain characteristics and multiplicative physical coupling terms, solving the deep physical coupling problem that conventional linear models cannot handle.

[0047] The time-varying confidence decay manifold is constructed based on the signal variance of the unconnected section and the energy ratio of stick-slip high-frequency vibrations in the connected section, and exists as a weighting control matrix. The time-varying confidence decay manifold adaptively adjusts with the complexity of the working conditions, dynamically adjusting the confidence weight of the sensor signal in solving the objective function under icy and slippery road conditions or high-frequency vibration environments. By attenuating the contribution of channels with severe interference, the robustness and convergence speed of the parameter inversion algorithm under extreme boundary conditions are ensured.

[0048] The dynamic stiffness characterization parameters of the tire carcass structure belong to the final set of mechanical results output by the detection system. These parameters include stiffness increments, damping changes, and hysteresis response coefficients, reflecting the anisotropic characteristics of the tire carcass. These parameters meticulously characterize the true skeletal mechanical behavior of the tire under complex conditions of continuous frequency and load variations, providing a high-fidelity physical reference for intelligent vehicle chassis control algorithms.

[0049] In mapping a discrete-time domain signal to a spatial angular domain, the sampling reference of the original signal needs to be switched from time to the absolute rotation angle of the tire. The transient angular velocity is obtained by extracting pulse abrupt changes in the tire speed signal; the specific conversion logic relies on the discrete difference operation of the transient angular velocity. The specific calculation formula for the transient angular velocity is as follows: [Formula omitted for brevity].

[0050] In the formula, Indicates the first The transient angular velocity obtained from the calculation of each time step This represents the fixed physical hardware angle between adjacent pulse abrupt changes in the tire speed signal. This indicates the timestamp of the current pulse mutation point. This represents the timestamp of the previous pulse mutation point. After calculating the transient angular velocity, the original signal to be processed is resampled and integrated at equal angular intervals using the transient angular velocity. This converts the original signal in the time dimension into a periodic response sequence in the angular domain indexed by the rotation angle, eliminating the periodic stretching distortion caused by nonlinear fluctuations in wheel speed. After extracting the low-frequency quasi-static component and the tire non-contact section signal from the synchronization analysis matrix, it is necessary to reconstruct the background drift trend throughout the entire rotation cycle. The low-frequency quasi-static component is fitted using the tire non-contact section signal to generate an eccentric bias baseline sequence. The fitting process uses the Fourier series expansion method to extract the pure low-frequency quasi-static component under no contact force interference as the fitting sample. The specific basis function fitting formula is as follows:

[0051] In the formula, This represents the centrifugal bias baseline sequence constructed based on the rotation angle. This represents the reference angular domain coordinate variables established based on the tire angular position signal. This represents the DC bias constant in the low-frequency quasi-static component. This represents the harmonic order of the Fourier expansion. This indicates the maximum harmonic order that the system is set to fit. Indicates the first Fitting coefficients of the first-order cosine basis functions, Indicates the first The fitting coefficients of the first-order sinusoidal basis function are calculated. By solving for these coefficients, the signal characteristics of the local unconnected segment are extended to the full-cycle angular range, generating a smooth and continuous centrifugal bias baseline sequence.

[0052] When a tire experiences a transient impact at its contact patch, the microscopic rubber friction and the macroscopic structural vibration produce different energy distributions. A full-time-domain integration operation is performed on the high-frequency nonlinear response components to generate the stick-slip high-frequency vibration energy ratio of the contact patch area. Simultaneously, variance calculations are performed on the signal from the non-contact patch area to generate the variance of the non-contact patch area signal. The specific formulas for calculating the variance and energy ratio are as follows:

[0053]

[0054] In the formula, This represents the variance of the non-contact segment signal generated by the operation. This represents the total number of discrete sampling points for the tire-uncoated section signal. The tire non-contact zone signal is in the first... The amplitude of each sampling point This represents the statistical mean of the signal in the tire-off contact area across the entire sampling interval. This indicates the ratio of high-frequency vibration energy in the stick-slip section. This indicates the time marker used to locate the section entering the tire contact area by setting the strain pulse threshold. This indicates the time marker when the vehicle exits the tire contact zone. This represents the high-frequency nonlinear response component extracted from the tire contact area segment signal. This represents the total signal energy in the tire contact area, including energy across all frequency bands. The enormous centrifugal torque generated by high-speed rotation stretches the tire carcass cords, leading to a nonlinear increase in structural stiffness. Substituting the prior centrifugal load characterization into the nonlinear stiffness hardening mapping function generates the nonlinear increment of sidewall tension. The specific physical relationship expression of the mapping function is as follows:

[0055] In the formula, This represents the nonlinear increment of the tire wall tension generated by the solution. This represents the a priori centrifugal load characterization quantity generated by the computational wheel center acceleration signal. This represents the first-order linear stiffness hardening factor of the tire carcass cord material. This represents the second-order nonlinear stiffness hardening coefficient of the tire carcass cord material.

[0056] After obtaining the nonlinear increment of the tire wall tension, which characterizes the tension of the tire cord layer, the nonlinear increment of the tire wall tension is used to modify the rheological calculus order to reconstruct the intermodulation distortion compensation. The processing procedure extracts the impact shear strain characteristic quantity from the high-frequency nonlinear response components, and performs calculus operations on the impact shear strain characteristic quantity using the updated fractional-order viscoelastic hysteresis constitutive model. The specific formula for the multiplicative physics coupling term operation is as follows:

[0057] In the formula, This represents the multiplicative physical coupling term generated by performing calculus on the impact shear strain characteristic quantity, which is also the intermodulation distortion compensation quantity that needs to be reconstructed. This represents the static shear modulus of tire rubber materials. This represents the impact shear strain characteristic quantity extracted from the high-frequency nonlinear response components. This represents the dynamic viscoelastic coefficient of the tire rubber material. This indicates the use of nonlinear increments in tire wall tension. The rheological calculus order in the fractional-order viscoelastic hysteresis constitutive model is updated in real time. Describing the order based on rheological calculus A fractional-order calculus operator is constructed. To address the potential distortion risk in some channels under extreme testing conditions, the system introduces an exponential decay surface function to achieve adaptive weight adjustment. The signal variance in unconnected sections and the high-frequency stick-slip vibration energy ratio in connected sections are normalized to generate normalized dual parameters. These normalized dual parameters are then substituted into the exponential decay surface function to generate a time-varying reliability penalty weight matrix. The specific decay surface calculation formula is as follows:

[0058] In the formula, Indicates the first The time-varying confidence penalty weight matrix generated in each computation step, and the constructed time-varying confidence decay manifold, This represents the system's preset initial full-confidence base weight matrix. Indicates the first The variance of the non-connected segment signal after normalization processing in each operation step Indicates the first The ratio of high-frequency vibration energy of the contact area after normalization of each computational step. This represents the first preset attenuation control factor set for the mechanical background vibration. This indicates the second preset attenuation control factor set for micro-slippage of the contact patch. This represents an exponential function operator with the natural constant as its base. Substituting the time-varying confidence decay manifold into the objective cost function of the state-space observation model, gradient descent iterative computation is performed under the dynamic constraints of the time-varying confidence decay manifold to solve for and generate the dynamic stiffness characterization parameters of the tire carcass structure. The specific formulas for constructing and optimizing the objective cost function are as follows:

[0059] In the formula, This represents the objective cost function of the state-space observation model. This represents the total number of iteration time steps in the state-space observation model. This represents the actual multidimensional physical observation vector constructed by splicing the net response signal sequence with the test data of the six external forces of the tire. This represents the theoretical estimation vector output by the state-space observation model based on the currently predicted parameters. This represents the time-varying confidence decay manifold, i.e., the time-varying confidence penalty weight matrix, substituted into the objective cost function. This represents the transpose matrix of the difference between the actual multidimensional physical observation vector and the theoretically estimated vector. This represents the iteration increment of the dynamic stiffness characterization parameter of the tire carcass structure to be solved in the current iteration step. This represents the regularization coefficient used to prevent overfitting. This represents the L2 norm squared operation term representing the iterative increment. The system calculates the objective cost function. The partial derivatives of the parameters characterizing the dynamic stiffness of the tire carcass structure are continuously updated along the direction of the fastest gradient descent until the target cost function is reached. The parameters converge to a minimum value, and the final dynamic stiffness characterization parameters of the tire carcass structure are output.

[0060] Embodiment 1 of this invention: Under the condition of a vehicle performing constant speed cruising on a highway, a multi-axis microelectromechanical system (MEMS) is used for signal capture by the sensing element inside the wheel rim. The installation process involves fixing the MEMS to the inner surface of the wheel rim using a customized wedge-shaped base. Through a rigid connection arrangement in physical space, the specific normal sensing axis of the MEMS is made absolutely parallel to the radial ray outward from the center of the wheel rim. At the same time, the tangential sensing axis of the MEMS is adjusted to be absolutely perpendicular to the tangent direction of the tire contact surface. This scheme constructs a pure centrifugal observation channel that is not affected by ground friction in the mechanical transmission link. A multi-channel synchronous analog-to-digital converter is simultaneously started to perform high-frequency sampling on the original signal, tire speed signal, and tire angular position signal. The initial time domain original signal train after acquisition flows into the phase synchronization stage. The processing process consumes the tire speed signal and the tire angular position signal, extracts the pulse edge calculation to obtain the transient angular velocity, and uses a spline interpolation algorithm to perform angular domain resampling, converting the data stream in the independent time axis into an angular domain periodic response sequence indexed by the absolute rotation angle. A dynamic time warping algorithm is used to evaluate the phase difference between sampling channels, and a leading phase compensation operator is used to align the signal phase, generating a synchronization analysis matrix that includes alignment attributes of multiple physical feature dimensions. Then, signal segments from the tire-off-ground region are extracted based on unified angular domain coordinates. Based on the wheel center acceleration signal output from the pure centrifugal observation channel, basis function fitting is performed on the aligned static components using Fourier series expansion. The fitting results are extended to the full-cycle angular range, generating a centrifugal bias baseline sequence reflecting dynamic fluctuations with rotational speed.

[0061] Embodiment 2 of the present invention: In application scenarios where intelligent vehicles face complex adhesion conditions or perform emergency obstacle avoidance maneuvers on icy or slippery roads, the tire contact surface is subjected to severe transient impacts. The solution locates the tire contact area signal experiencing rapid deformation from the synchronous analysis matrix by setting a strain pulse threshold crossing condition. The tire contact area signal is then decomposed using continuous time-frequency transformation to extract high-frequency nonlinear response components reflecting the excitation characteristics of road microparticles. These high-frequency nonlinear response components are integrated across the entire time domain to extract the stick-slip high-frequency vibration energy ratio of the contact area. Simultaneously, variance calculation is performed on the tire non-contact area signal to obtain the variance of the non-contact area signal characterizing the rim mechanical resonance amplitude and the system's background white noise characteristics. When a sudden increase in the stick-slip high-frequency vibration energy ratio of the contact area indicates an extreme slippage state, the solution utilizes normalized dual-parameters to construct a two-dimensional time-varying reliability decay manifold. Under the dynamic constraint of the time-varying reliability decay manifold, the inversion weight coefficients of the net response signal sequence decay exponentially, simultaneously increasing the inversion weight coefficients of the data collected by the external six-component force testing device, preventing the mathematical solution matrix from falling into a singular state. The calculated dynamic stiffness characterization parameters flow into the actual vehicle dynamic response matching module corresponding to the electronic stability control system, and are corrected by adjusting the inter-wheel load transfer estimation results under different road surface characteristics to maintain the vehicle's control performance under extreme runaway conditions.

[0062] Embodiment 3 of this invention: In scenarios involving refined calibration of the true dynamic stiffness of tire carcass structures or updates to digital twin models, this scheme introduces a fractional-order viscoelastic rheological equation to characterize the hysteresis properties of the material. The algebraic difference between the original signal and the centrifugal bias baseline sequence is calculated to generate a first-order debiased signal. Prior centrifugal load characteristics are extracted and substituted into a nonlinear stiffness hardening mapping function to solve for the nonlinear increment of the tire wall tension, reflecting the tensile prestress state of the tire carcass cords. This nonlinear increment of the tire wall tension serves as a dynamic tuning parameter, changing the rheological calculus order within the fractional-order viscoelastic hysteresis constitutive model in real time. The updated constitutive model is used to perform calculus operations on the impact shear strain characteristic quantities to reconstruct the intermodulation distortion compensation quantity that conforms to the deep micromechanical evolution of the tire. This intermodulation distortion compensation quantity is filtered out from the first-order debiased signal, outputting a high-fidelity net response signal sequence stripped of coupling interference. The net response signal sequence is combined with the multidimensional torque collected by the tire six-component force testing device to solve for constraints, thereby obtaining the stiffness increment, damping change, and hysteresis response coefficient under the corresponding working conditions. By inputting the obtained parameters into the tire digital twin model, the deviation between the model output and the measured vehicle response is compared, and the particle swarm optimization algorithm is used to automatically iteratively update the model boundary parameters.

[0063] To verify the effectiveness of the tire multi-condition dynamic parameter detection method, this invention constructed a comparative experiment. Researchers used 205 / 55R16 test tires to perform the test. The experimental environment was set as an icy and slippery test bench covered with 80 mm of snow. Researchers operated a testing machine to perform a combined excitation action of emergency braking and rapid steering at a speed of 60 km / h on the test wheels. The testing system recorded the parameter inversion error data of the comparative group obtained using a traditional linear filtering debiasing algorithm, as well as the parameter inversion error data of the experimental group obtained using the tire multi-condition dynamic parameter detection method provided by this invention.

[0064] System analysis and test results show that the root mean square error (RMSE) of the lateral stiffness inversion for the control group tire model is 15.6 when the slip ratio reaches 20%. In contrast, the RMS error of the lateral stiffness inversion for the experimental group tire model under the same working condition (slip ratio reaching 20%) is significantly reduced to 4.2. These experimental results strongly demonstrate that the tire multi-condition dynamic parameter detection method provided by this invention successfully eliminates the nonlinear response intermodulation distortion caused by centrifugal offset drift and transient impact.

Claims

1. A method for detecting tire dynamic parameters under multiple operating conditions, characterized in that, include: Step 1: Collect the raw signals output by the sensing elements inside the tire rim, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing elements inside the rim, and the test data of the six components of the tire's external force. Step 2: Establish reference angular domain coordinates based on the tire angular position signal, map the original signal to the reference angular domain coordinates to generate a synchronization analysis matrix; calculate the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extract the low-frequency quasi-static component and tire non-contact segment signal from the synchronization analysis matrix, and use the tire non-contact segment signal to fit the low-frequency quasi-static component to generate a centrifugal offset baseline sequence. Step 3: Extract the tire contact area segment signal from the synchronization analysis matrix, extract the high-frequency nonlinear response component from the tire contact area segment signal, calculate the high-frequency nonlinear response component to generate the stick-slip high-frequency vibration energy ratio of the contact area segment, and calculate the tire non-contact area segment signal variance from the tire non-contact area segment signal. Step 4: Subtract the centrifugal bias baseline sequence from the original signal to generate a first-order debiased signal; use the prior centrifugal load characterization quantity to calculate the nonlinear increment of the tire wall tension; use the nonlinear increment of the tire wall tension to change the rheological calculus order to reconstruct the cross-modulation distortion compensation quantity; use the cross-modulation distortion compensation quantity to correct the first-order debiased signal to generate a net response signal sequence. Step 5: Calculate the time-varying reliability decay manifold using the variance of the signal in the unconnected section and the energy ratio of the stick-slip high-frequency vibration in the connected section. Use the time-varying reliability decay manifold to constrain the net response signal sequence and the tire external six-component force test data to solve and generate the dynamic stiffness characterization parameters of the tire carcass structure.

2. The tire multi-condition dynamic parameter detection method according to claim 1, characterized in that, The system collects raw signals output from the sensing elements inside the tire rim, tire speed signals, tire angular position signals, wheel center acceleration signals output from the sensing elements inside the rim, and tire external six-component force test data, including: The normal sensing axis of the rim-interior sensing element installed on the inner surface of the rim is set to be parallel to the radial ray outward from the center of the rim, and the tangential sensing axis of the rim-interior sensing element is set to be perpendicular to the tangential direction of the tire contact surface. Discrete digital signals are sampled using the internal sensing element of the wheel rim, and low-pass filtering is performed on the discrete digital signals to generate the original signal and the wheel center acceleration signal.

3. The tire multi-condition dynamic parameter detection method according to claim 2, characterized in that, Based on the tire angular position signal, a reference angular domain coordinate system is established. The original signal is then mapped onto the reference angular domain coordinate system to generate a synchronization analysis matrix, including: The transient angular velocity is obtained by extracting the pulse change points in the tire speed signal, and the original signal is converted into an angular domain periodic response sequence using the transient angular velocity; The transmission delay of the angular domain periodic response sequence is evaluated using a dynamic time warping algorithm, and the phase of the angular domain periodic response sequence is aligned using a lead phase compensation operator to generate the synchronization analysis matrix.

4. The tire multi-condition dynamic parameter detection method according to claim 3, characterized in that, The calculation of the wheel center acceleration signal generates a priori centrifugal load characterization quantity. Low-frequency quasi-static components and tire-off-joint segment signals are extracted from the synchronization analysis matrix. The low-frequency quasi-static components are then fitted using the tire-off-joint segment signals to generate a centrifugal bias baseline sequence, including: The tire non-contact section signal is extracted from the synchronization analysis matrix, and the radial force equivalent value of the wheel center acceleration signal is calculated using the tire speed signal to generate the a priori centrifugal load characterization quantity. The low-frequency quasi-static components are fitted with basis functions using the Fourier series expansion method, and the fitting results are extended to the full-cycle angle range to generate the centrifugal bias baseline sequence.

5. The tire multi-condition dynamic parameter detection method according to claim 4, characterized in that, The tire contact zone signal is extracted from the synchronization analysis matrix; high-frequency nonlinear response components are extracted from the tire contact zone signal; the high-frequency nonlinear response components are calculated to generate the stick-slip high-frequency vibration energy ratio of the contact zone; and the tire non-contact zone signal variance is calculated from the tire non-contact zone signal, including: The tire contact zone signal is located from the synchronous analysis matrix by setting a strain pulse threshold crossing condition. The tire contact zone signal is decomposed by time-frequency transformation to generate the high-frequency nonlinear response component. The high-frequency nonlinear response component is integrated in the whole time domain to generate the stick-slip high-frequency vibration energy ratio of the contact zone. The variance of the tire non-contact zone signal is generated by performing variance calculation on the tire non-contact zone signal.

6. The tire multi-condition dynamic parameter detection method according to claim 5, characterized in that, The nonlinear increment of tire wall tension is generated by calculating the prior centrifugal load characterization quantity, and the nonlinear increment of tire wall tension is used to change the rheological calculus order to reconstruct the intermodulation distortion compensation quantity, including: The a priori centrifugal load characterization is substituted into the nonlinear stiffness hardening mapping function to generate the nonlinear increment of the tire wall tension. The rheological calculus order in the fractional-order viscoelastic hysteresis constitutive model is updated by using the nonlinear increment of the tire wall tension to generate the updated fractional-order viscoelastic hysteresis constitutive model. The impact shear strain characteristic quantity is extracted from the high-frequency nonlinear response component. The impact shear strain characteristic quantity is then subjected to calculus operations using the updated fractional-order viscoelastic hysteresis constitutive model to generate a multiplicative physical coupling term. This multiplicative physical coupling term is then determined as the intermodulation distortion compensation quantity.

7. The tire multi-condition dynamic parameter detection method according to claim 6, characterized in that, Using the intermodulation distortion compensation amount to correct the first-order debiased signal to generate a net response signal sequence includes: Using the high-frequency vibration energy ratio of the junction area as a physical confidence factor, the effect gain of the intermodulation distortion compensation amount is dynamically adjusted to generate an adaptive compensation vector. The historical strain hysteresis features of the tire carcass structure are extracted using the updated fractional-order viscoelastic hysteresis constitutive model. The adaptive compensation vector and the first-order debiased signal are then subjected to nonlinear residual mapping based on the historical strain hysteresis features. Intermodulation distortion residuals are filtered out to generate the net response signal sequence.

8. The tire multi-condition dynamic parameter detection method according to claim 7, characterized in that, The time-varying reliability decay manifold is generated by calculating the ratio of the signal variance of the unconnected section to the stick-slip high-frequency vibration energy of the connected section, including: The signal variance of the unconnected section and the high-frequency vibration energy ratio of the stick-slip section are normalized to generate normalized dual parameters. The normalized dual parameters are substituted into the exponential decay surface function to generate a time-varying confidence penalty weight matrix, and the time-varying confidence penalty weight matrix is ​​determined as the time-varying confidence decay manifold.

9. The tire multi-condition dynamic parameter detection method according to claim 8, characterized in that, Using the time-varying reliability decay manifold constraint on the net response signal sequence and the tire external six-component force test data, the dynamic stiffness characterization parameters of the tire carcass structure are solved to generate, including: Establish a state-space observation model with the net response signal sequence and the test data of the six external forces of the tire as inputs and the dynamic stiffness characterization parameters of the tire carcass structure as outputs. The time-varying reliability decay manifold is substituted into the objective cost function of the state-space observation model, and gradient descent iterative operation is performed to solve for the dynamic stiffness characterization parameters of the tire carcass structure. The dynamic stiffness characterization parameters of the tire carcass structure are then substituted into the electronic stability control model to generate the dynamic response matching results.

10. A tire multi-condition dynamic parameter detection system, applied to the tire multi-condition dynamic parameter detection method as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect the original signals output by the sensing elements inside the tire rim, the tire speed signal, the tire angular position signal, the wheel center acceleration signal output by the sensing elements inside the rim, and the tire external six-component force test data. The coordinate alignment module is used to establish a reference angular domain coordinate based on the tire angular position signal, and to map the original signal to the reference angular domain coordinate to generate a synchronization analysis matrix; The baseline extraction module is used to calculate the wheel center acceleration signal to generate a priori centrifugal load characterization quantity, extract low-frequency quasi-static components and tire off-hook section signals from the synchronization analysis matrix, and use the tire off-hook section signals to fit the low-frequency quasi-static components to generate a centrifugal bias baseline sequence. The feature recognition module is used to extract tire contact area segment signals from the synchronization analysis matrix, extract high-frequency nonlinear response components from the tire contact area segment signals, and calculate and generate the stick-slip high-frequency vibration energy ratio of the contact area segment and the variance of the non-contact area segment signals. The physical reconstruction module is used to subtract the centrifugal bias baseline sequence from the original signal to generate a first-order debiased signal, calculate the nonlinear increment of the tire wall tension using the prior centrifugal load characterization quantity, change the rheological calculus order using the nonlinear increment of the tire wall tension to reconstruct the intermodulation distortion compensation quantity, and correct the first-order debiased signal to generate a net response signal sequence. The parameter inversion module is used to generate a time-varying reliability decay manifold by calculating the signal variance of the unconnected section and the stick-slip high-frequency vibration energy ratio of the connected section, and to use the time-varying reliability decay manifold to constrain the net response signal sequence and the tire external six-component force test data to solve and generate the dynamic stiffness characterization parameters of the tire carcass structure.

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