A chassis domain control stability control system and control method based on intelligent tires

Abstract

The application discloses a kind of based on intelligent tire's chassis field control stability control system and control method.The system includes: demand input module, chassis field control unit and execution unit.Demand input module gathers driver demand and vehicle state signal.Chassis field control unit is communicated with demand input module, its inside integration: intelligent tire control module, obtains tire real-time state parameter and generates normalization parameter package;Comprehensive calculation control module, stability control strategy is executed based on parameter package and demand signal, and control instruction is output;Arbitration management module, priority arbitration and conflict coordination are carried out to instruction;Diagnosis management module, fault diagnosis and monitoring are carried out;Execution control module, after arbitration instruction is issued to execution unit.The execution unit includes brake, drive, steering and suspension system, receives and executes instruction.The application significantly improves the stability control precision of vehicle, effectively reduces the accident rate of wet and low adhesion road.

Description

Technical Field

[0001] This invention belongs to the field of automotive chassis dynamics control technology, and more specifically, relates to a chassis domain control stability control system and control method based on intelligent tires. Background Technology

[0002] With the rapid development of automotive intelligence and electrification, the demand for tire condition sensing capabilities in vehicle chassis control systems is becoming increasingly urgent. As the only component of a vehicle in contact with the road surface, the tire's mechanical state (longitudinal force, lateral force, vertical force), as well as parameters such as road adhesion coefficient, temperature distribution, and wear degree, directly determine the vehicle's stability, safety, and handling limits. However, traditional tire condition monitoring and chassis stability control methods suffer from the following technical bottlenecks: 1. Limited tire condition sensing capabilities prevent direct acquisition of key mechanical parameters: Traditional tire monitoring systems primarily rely on indirect estimation methods. For example, tire pressure monitoring systems (TPMS) can only provide tire pressure and rough temperature data, failing to reflect key mechanical states such as tire force and deformation. Longitudinal force estimation based on wheel speed differences has an error rate as high as 15%–20%, and lateral force estimation depends on vehicle dynamics models, making it highly susceptible to road surface disturbances. Traditional electronic stability control (ESC) systems require waiting for significant wheel slippage (slip rate > 5%) before recognizing changes in adhesion, resulting in control intervention delays typically exceeding 200 milliseconds.

[0003] Second, the control strategy relies on a static model and cannot adapt to environmental changes. Existing chassis control algorithms use factory-calibrated tire model parameters (such as the Magic Formula stiffness coefficient and friction coefficient), which cannot reflect the characteristic drift of tires caused by changes in temperature, wear, and load during use. Studies have shown that when the tire temperature changes by ±30℃, the lateral stiffness deviation can reach 25%, resulting in a yaw rate control error exceeding 15° / s under extreme conditions. In addition, the identification of the road adhesion coefficient (μ value) relies on ABS braking pulse probing, requiring 3 to 5 wheel speed cycles to identify road surface changes, which can easily lead to over-braking or insufficient braking force on roads with alternating ice / snow and asphalt surfaces.

[0004] Third, each subsystem makes independent decisions, lacking collaborative optimization and data fusion: In traditional vehicles, subsystems such as Electronic Stability Program (ESP), Traction Control System (TCS), and Electric Power Steering (EPS) make independent decisions. The underlying tire physical parameters are difficult to correct in real time and transmit to the upper-level control algorithm. Tire state information is not shared across systems, leading to 20%–30% redundant adjustments in drive torque distribution. When multiple systems simultaneously request tire force (e.g., braking during emergency steering), the lack of a unified coordination mechanism based on tire adhesion limits can easily cause control interference or even instability.

[0005] IV. High control risks under extreme operating conditions and hardware failure: When tires enter the nonlinear adhesion region (slip ratio > 10%), traditional linear model-based control algorithms fail, leading to uncontrollable yaw or roll of the vehicle. On low-friction surfaces (μ < 0.3), the lateral acceleration tracking error of traditional PID control can reach 0.4 m / s², exceeding the safety threshold (0.2 m / s²). Furthermore, traditional chassis systems lack redundant compensation mechanisms based on the actual mechanical state of the tires when faced with a single actuator (such as single-wheel brake caliper lock-up or steering motor failure) or sensor malfunction, making them highly susceptible to instantaneous loss of vehicle control.

[0006] The aforementioned shortcomings collectively lead to a vicious cycle of "sensing lag—conservative decision-making—delayed execution" in traditional chassis control systems under complex operating conditions. According to statistics from the Insurance Association, approximately 38% of serious traffic accidents are directly related to the chassis control system's failure to promptly identify changes in tire adhesion. Therefore, there is an urgent need for a stability control solution capable of real-time and accurate perception of multi-dimensional tire conditions and deep integration with the chassis domain controller to overcome the performance limitations of traditional technologies. Summary of the Invention

[0007] The purpose of this invention is to propose a chassis domain control stability control system and method based on intelligent tires. This addresses the technical problems of traditional chassis control systems, such as perception lag, model parameter mismatch, lack of cross-subsystem coordination, and control failure under extreme conditions, caused by the inability to directly acquire the multi-dimensional mechanical state of the tires. The invention enables real-time acquisition of the multi-dimensional tire state and precise compensation for the effects of temperature and wear on tire characteristics, overcoming the limitations of traditional static models. Through dynamic optimization of adhesion limit management and distributed force distribution, the invention significantly improves the vehicle's stability control accuracy and effectively reduces the accident rate on wet and low-adhesion road surfaces. Optimized control based on the actual tire state avoids localized excessive wear and thermal runaway, effectively extending tire lifespan.

[0008] To achieve the above objectives, in a first aspect, the present invention proposes a chassis domain control stability control system based on intelligent tires, comprising: Demand input module, chassis domain control unit, and execution unit; The demand input module is used to collect driver demand signals and vehicle status signals; The chassis domain control unit is communicatively connected to the demand input module, and includes: The intelligent tire control module is used to acquire real-time tire status parameters and generate a normalized tire status parameter package based on the real-time status parameters; the normalized tire status parameter package includes at least: tire three-dimensional force, road surface peak adhesion coefficient, tire temperature gradient matrix and tire health index; The integrated calculation and control module is used to receive and execute stability control strategies based on the normalized tire state parameter package, driver demand signal and vehicle state signal to generate control commands; The arbitration management module is used to perform priority arbitration and conflict coordination on the control commands generated by the integrated calculation and control module; The diagnostic management module is used for fault diagnosis and status monitoring of the system; The execution control module is used to send the arbitrated control commands to the execution unit; The execution unit includes at least a braking system, a drive system, a steering system, and a suspension system, and is used to receive and execute control commands issued by the execution control module.

[0009] Optionally, the intelligent tire control module obtains the real-time status parameters through any of the following methods: Method 1: Real-time acquisition of tire three-dimensional force, temperature field distribution and tire deformation through a multi-physics field sensor array embedded in the tire to obtain the real-time state parameters; Method 2: Based on the vehicle state signal, the tire-vehicle coupled dynamic state is perceived, modeled and evaluated in real time through software algorithms to obtain the real-time state parameters; The real-time state parameters include at least the tire longitudinal force, lateral force, vertical force, tire body deformation, tread temperature field distribution matrix, and tread pressure distribution matrix.

[0010] Optionally, the intelligent tire control module and the integrated computing control module employ a dual-channel communication protocol: A high-speed communication channel is used to transmit real-time control data at a rate not lower than a preset first frequency. The real-time control data includes at least tire longitudinal force, lateral force, vertical force, and peak road adhesion coefficient. A diagnostic and configuration channel is used to transmit diagnostic and configuration data at a rate not lower than a preset second frequency. The diagnostic and configuration data includes at least the tire temperature gradient matrix and the tire health index. The first frequency is greater than the second frequency, and the two channels are time-synchronized.

[0011] Optionally, the stability control strategy includes at least: Multi-physics field coupling compensation strategy, adhesion limit dynamic management strategy, distributed tire force optimization allocation strategy, and failure tolerance collaborative control strategy.

[0012] Optionally, the multiphysics coupling compensation strategy includes: Temperature-stiffness compensation: Based on the tire temperature gradient matrix, the real-time tread temperature is determined, and a preset temperature-stiffness compensation function is called to correct the tire's longitudinal stiffness, lateral stiffness, and vertical stiffness in real time. Temperature-friction compensation: Based on the real-time tread temperature, a preset temperature-friction compensation function is called to correct the tire-road friction coefficient in real time, so as to update the peak road adhesion coefficient; Temperature-load compensation: Based on the real-time tread temperature, a preset temperature-load compensation function is called to perform temperature correction on the vertical force to obtain the temperature-corrected vertical load; Three-dimensional force compensation output: The temperature-corrected tire stiffness, the updated friction coefficient, and the temperature-corrected vertical load are combined with the tire slip ratio and the tire three-dimensional force after full temperature compensation is calculated by a preset multi-physics coupling model. Real-time update: The compensated three-dimensional tire force is updated at a preset cycle for use in dynamic adhesion limit management strategies, distributed tire force optimization allocation strategies, and failure tolerance collaborative control strategies.

[0013] Optionally, the attachment limit dynamic management strategy includes: The wheel's used adhesion coefficient is calculated based on the compensated three-dimensional tire force, and the road surface peak adhesion coefficient is corrected based on the tire temperature gradient matrix and tire health index, thereby calculating the usable adhesion coefficient. The current stability control zone is determined based on a preset threshold range where the available adhesion coefficient of the wheel is located, wherein the stability control zone includes a comfort zone, a stable zone, and a limit zone; The corresponding control strategy is executed according to the current stability control zone: the normal control mode is executed in the comfort zone, the coordinated control mode is executed in the stable zone, and the emergency control mode is executed in the extreme zone. Specifically, in the extreme zone, a control strategy that prioritizes torque reduction over braking is automatically implemented, and the steering angle change rate is limited to a preset angle change rate threshold. In the region where the peak road surface adhesion coefficient gradient changes, a smooth transition algorithm is used to control the longitudinal force change rate, and when the difference between the peak road surface adhesion coefficients of the left and right wheels exceeds a preset threshold, lateral torque transfer is initiated.

[0014] Optionally, the distributed tire force optimization allocation strategy includes: The objective function is the sum of the absolute values ​​of the deviations between the compensated longitudinal forces of each tire and the desired longitudinal forces, plus the safety margin weighting coefficient multiplied by the sum of the available adhesion coefficients of each tire. The optimal braking torque allocation is solved by an optimization algorithm to minimize the objective function; The optimal braking torque allocation result obtained from the solution is converted into a braking torque command and sent to the braking system.

[0015] Optionally, the failure-tolerance collaborative control strategy includes: Brake failure compensation: When the deviation between the theoretical braking force of a wheel and the compensated longitudinal force of the tire is detected to be greater than the preset deviation threshold, the target braking torque of the wheel is automatically distributed to the healthy wheels on the same or opposite sides according to the adhesion coefficient ratio, and the compensation response time is less than the preset response time threshold. Steering failure coordination: When the electric power steering system partially fails, the equivalent steering angle is calculated based on the difference in lateral force between the left and right wheels after compensation, and a compensating yaw moment is generated through torque vector control; Drive failure transfer: When a single drive motor fails, the target torque of the failed motor is redistributed to the healthy wheels based on the updated peak road adhesion coefficient distribution of each wheel, ensuring that the remaining total driving force is not lower than the preset proportion threshold of the total driving force before failure.

[0016] Optionally, a tire-actuator dynamic matching mechanism is also included: Wear-Steering Coordination: The intelligent tire control module detects and reports the wear difference between the inner and outer sides of the tire; when the wear difference exceeds a preset wear threshold, the integrated calculation control module generates a four-wheel alignment adjustment command, which is sent to the steering system by the execution control module to dynamically adjust the kingpin inclination angle and toe angle. Stiffness-damping coordination: The intelligent tire control module acquires and reports the tire body deformation; the comprehensive calculation control module identifies tire stiffness changes based on the tire body deformation, and when the stiffness change exceeds a preset stiffness change threshold, it generates a damping adjustment command, which is sent to the suspension system by the execution control module to adjust its damping coefficient in real time. Pressure-load coordination: The intelligent tire control module acquires and reports the pressure distribution matrix; the integrated calculation control module uses the pressure distribution matrix to calculate the actual load transfer coefficient and corrects the load estimation error based on the suspension displacement sensor.

[0017] Secondly, the present invention proposes a chassis domain control stability control method based on intelligent tires, applied to the system described in any of the first aspects, the method comprising the following steps: Step S1: Collect driver demand signals, vehicle status signals, and real-time tire status parameters; Step S2: Construct a normalized tire state parameter package based on the real-time tire state parameters. The parameter package includes tire three-dimensional force, road surface peak adhesion coefficient and tire health status data after multi-physics field coupling compensation. Step S3: Based on the parameter package and vehicle status signal, perform dynamic management of adhesion limits, distributed tire force optimization allocation and failure-tolerant collaborative control to generate chassis control commands; Step S4: Priority arbitration and conflict coordination are performed on the chassis control commands, and the commands are sent to the execution unit for execution to achieve integrated control of vehicle stability.

[0018] The beneficial effects of this invention are as follows: First, this invention directly acquires real-time state parameters such as tire three-dimensional force, tire deformation, tread temperature field distribution, and pressure distribution through an intelligent tire control module, and generates a normalized tire state parameter package including the peak road adhesion coefficient, temperature gradient matrix, and health index, which is then transmitted to the integrated calculation and control module. This mechanism completely changes the traditional indirect estimation method, reducing the tire longitudinal force estimation error from 15%~20% to less than 5%, the lateral force estimation error to less than 8%, and shortening the road adhesion coefficient recognition delay from 200~300ms to milliseconds, truly achieving a highly efficient closed loop in the "perception-decision-execution" process.

[0019] Secondly, the integrated calculation and control module is based on a multi-physics field coupling compensation strategy. It uses the tire temperature gradient matrix to perform real-time correction of tire stiffness, friction coefficient and vertical load in all dimensions, so that the control accuracy of the chassis domain control is maintained above 95% in a wide temperature range of -30℃ to 80℃. This effectively solves the control failure problem caused by temperature drift and wear aging in traditional fixed parameter models.

[0020] Furthermore, the arbitration management module and the diagnostic management module work together to prioritize and coordinate conflicts of multi-system commands. Combined with a distributed tire force optimization distribution strategy (using the sum of the absolute values ​​of the longitudinal force deviations of each wheel and the weighted sum of the adhesion margins as the objective function), the repeated adjustments of drive torque distribution are reduced by 20% to 30%. In the extreme zone, a control strategy that prioritizes torque reduction over braking and lateral torque transfer is automatically implemented, which significantly improves the stability of the vehicle under extreme conditions such as ice and snow and split-road surfaces, reducing the lateral acceleration tracking error from 0.4 m / s² of the traditional PID to within the safe threshold of 0.2 m / s².

[0021] In addition, the failure-tolerant collaborative control mechanism utilizes the real mechanical state feedback from the intelligent tires to achieve rapid torque reconstruction (compensation response time less than 80ms, remaining driving force not less than 70%) when a single actuator fails in braking, steering, or driving, reducing the accident risk of single system failure by more than 60%.

[0022] Finally, this invention actively suppresses uneven tire wear, optimizes suspension damping matching, and corrects load estimation errors through actuator dynamic matching mechanisms such as wear-steering coordination, stiffness-damping coordination, and pressure-load coordination. This can extend tire life by about 30% and reduce rolling resistance by about 8%, thereby effectively improving the driving range of new energy vehicles.

[0023] In summary, this invention breaks through the technical bottleneck of traditional chassis "indirect estimation-hysteresis response" and achieves the comprehensive effect of improving vehicle stability control accuracy by more than 30% and significantly reducing the accident rate on wet and slippery roads.

[0024] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0025] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0026] Figure 1 A schematic diagram of a chassis domain control stability control system based on smart tires according to Embodiment 1 of the present invention is shown.

[0027] Figure 2 A schematic diagram of the working process of a chassis domain control stability control system based on smart tires according to Embodiment 1 of the present invention is shown. Detailed Implementation

[0028] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0029] Example 1

[0030] like Figure 1 As shown, this embodiment provides a chassis domain control stability control system based on intelligent tires, including: Demand input module, chassis domain control unit, and execution unit; The demand input module is used to collect driver demand signals and vehicle status signals; The chassis domain control unit is communicatively connected to the demand input module, and includes: The intelligent tire control module is used to acquire real-time tire status parameters and generate a normalized tire status parameter package based on the real-time status parameters. The normalized tire status parameter package includes at least: tire three-dimensional force, road surface peak adhesion coefficient, tire temperature gradient matrix and tire health index. The integrated calculation and control module is used to receive and execute stability control strategies based on normalized tire state parameter packages, driver demand signals, and vehicle state signals to generate control commands; The arbitration management module is used to prioritize and coordinate conflicts in the control commands generated by the integrated calculation and control module. The diagnostic management module is used for fault diagnosis and status monitoring of the system; The execution control module is used to send the arbitrated control commands to the execution unit; The execution unit includes at least a braking system, a drive system, a steering system, and a suspension system, and is used to receive and execute control commands issued by the execution control module.

[0031] Specifically, the system consists of three main parts: a demand input module, a chassis domain control unit, and an execution unit. The demand input module is responsible for collecting the driver's driving demand signals (such as accelerator pedal opening, brake pedal force, steering wheel angle, etc.) and the vehicle's status signals (such as vehicle speed, yaw rate, wheel speed, longitudinal / lateral acceleration, etc.) in real time.

[0032] The chassis domain control unit communicates with the demand input module and integrates an intelligent tire control module, a comprehensive calculation control module, an arbitration management module, a diagnostic management module, and an execution control module. The intelligent tire control module acquires real-time tire status parameters, including tire three-dimensional forces (longitudinal, lateral, and vertical forces), tire deformation, tread temperature field distribution, and pressure distribution. Based on these real-time status parameters, it calculates a normalized tire status parameter package using built-in algorithms (such as magic formula fitting and Gaussian process regression). This parameter package includes at least: tire three-dimensional forces, peak road adhesion coefficient (resolution 0.05), tire temperature gradient matrix (16×4 partitions, accuracy ±0.5℃), and tire health index (dimensionless score from 0 to 100).

[0033] The integrated calculation and control module receives the normalized tire state parameter package, driver demand signal and vehicle state signal, and executes in parallel the multi-physics coupling compensation strategy, the adhesion limit dynamic management strategy, the distributed tire force optimization allocation strategy and the failure-tolerant collaborative control strategy.

[0034] Specifically, the multi-physics coupling compensation strategy utilizes the tire temperature gradient matrix and tire health index to perform real-time correction of tire stiffness, friction coefficient, and vertical load across all dimensions of temperature, force, stiffness, and friction, outputting the three-dimensional tire force after full temperature compensation; the adhesion limit dynamic management strategy calculates the available adhesion coefficient of each wheel based on the compensated three-dimensional tire force and the corrected peak road adhesion coefficient, and dynamically adjusts the control mode according to preset threshold intervals (comfort zone, stable zone, and limit zone), automatically implementing torque reduction prioritizing braking and limiting the rate of change of steering angle in the limit zone; the distributed tire force optimization allocation strategy uses the sum of the absolute values ​​of the deviations between the actual longitudinal force and the desired longitudinal force of each tire plus the safety margin weight coefficient multiplied by the sum of the available adhesion coefficients of each wheel as the objective function, employs an optimization algorithm to solve for the optimal braking torque allocation, and then issues the converted command; the failure-tolerant collaborative control strategy, when detecting partial failure of the braking, steering, or drive system, utilizes the real-time mechanical state feedback from intelligent tires to maintain vehicle stability through proportional torque transfer, torque vector control, or motor torque reconstruction.

[0035] The control commands generated by the integrated calculation control module are then sent to the arbitration management module. This module coordinates conflicts and arbitrates priorities among the commands based on the priority of each strategy and the current operating conditions to ensure that there is no interference between the commands.

[0036] The diagnostic management module monitors the status of the intelligent tire control module, the integrated computing control module, and the execution unit in real time, performs fault diagnosis and health assessment, and feeds back abnormal information to the arbitration management module to trigger degradation or fault tolerance strategies.

[0037] After arbitration, the valid control commands are sent from the execution control module to the execution unit via a bus (such as CAN FD). The execution unit includes at least a braking system (such as Electronic Stability Program ESP), a drive system (such as a motor controller), a steering system (such as Electric Power Steering EPS), and a suspension system (such as a Continuously Damped Controlled Damper CDC shock absorber). These actuators respond to the corresponding commands and respectively complete the adjustment of braking torque, drive torque distribution, steering angle compensation, or damping coefficient adjustment, ultimately achieving high-precision stability control of the vehicle under complex operating conditions.

[0038] In this embodiment, the intelligent tire control module obtains real-time status parameters through any of the following methods: Method 1: Real-time data collection of tire three-dimensional force, temperature field distribution, and tire deformation is obtained through a multi-physics field sensor array embedded in the tire to acquire real-time status parameters; Method 2: Based on the vehicle state signals, the tire-vehicle coupled dynamic state is perceived, modeled and evaluated in real time through software algorithms to obtain real-time state parameters; The real-time state parameters include at least the tire longitudinal force, lateral force, vertical force, tire body deformation, tread temperature field distribution matrix, and tread pressure distribution matrix.

[0039] Specifically, in specific embodiments, the intelligent tire control module can acquire real-time state parameters through two independent physical or logical paths, corresponding to the hardware sensing scheme (method one) and the pure software algorithm scheme (method two), respectively. Both can output real-time state parameters required for stability control, including at least the tire longitudinal force, lateral force, vertical force, tire body deformation, tread temperature field distribution matrix, and tread pressure distribution matrix.

[0040] Method 1: Hardware acquisition scheme based on embedded multiphysics sensor array. During tire manufacturing, multiple types of micro-sensors are pre-embedded in key locations such as the inner surface of the tire crown, the sidewall, and the bottom of the tread, forming a multiphysics sensor array. Specifically, this includes: ① MEMS pressure sensor array: Arranged in a grid pattern along the tire's circumference (e.g., 16 zones per 22.5° interval) and laterally (divided into 4 equal-width zones from the inner to the outer side of the tread), with more than 64 sensing units. Each unit collects the pressure value at the corresponding location within the contact patch in real time. The vertical force of the tire is obtained by integration and summation. Meanwhile, the offset of the grounding imprint center is calculated based on the asymmetry of the pressure distribution.

[0041] ② Flexible thin-film temperature sensor array: Arranged in the same area as the pressure sensor, it directly reads the temperature values ​​of different zones of the tire tread, constructs a 16×4 tire tread temperature field distribution matrix, and calculates the circumferential temperature gradient. and transverse temperature gradient .

[0042] ③ Fiber Bragg grating (FBG) strain sensor: Embedded along the radial direction (from the bead to the crown) and circumferential direction (center of the tread and shoulder) of the tire, it measures the radial strain during tire rolling. and circumferential strain By analyzing the time-frequency signals of the strain signals and combining them with the tire stiffness model, the longitudinal force of the tire can be deduced. and lateral force .

[0043] ④ 6-axis accelerometer: Installed on the inner wall of the rim, it measures the triaxial acceleration and triaxial angular velocity of the tire during rotation, used to assist in calculating the slip ratio and tire dynamic load. All analog or digital signals output from the sensors are conditioned, filtered, and converted from analog to digital by an integrated analog front-end within the tire, and then aggregated to the intelligent tire control module via near-field communication (such as NFC) or Bluetooth Low Energy. The intelligent tire control module runs embedded signal processing algorithms, such as Kalman filtering to remove noise, integrating the pressure matrix into vertical force using a calibrated pressure-load relationship, calculating longitudinal / lateral forces using a strain-stiffness mapping model, and directly outputting the temperature field distribution matrix through a temperature sensor array. This solution provides direct data sources, high single-parameter accuracy (e.g., vertical force error <3%, longitudinal force error <5%), and an update frequency exceeding 100Hz.

[0044] Method 2: Software-based virtual perception solution based on existing vehicle signals. This solution does not add any embedded hardware to the tires, but fully utilizes the vehicle's existing sensor signals to estimate tire status in real time through a "software-defined tire" algorithm. Input signals include at least: wheel speed sensor signals from all four wheels (typically 100Hz), longitudinal acceleration, lateral acceleration, and yaw rate provided by the vehicle's inertial measurement unit (IMU), suspension displacement sensor signals, steering wheel angle signal, brake master cylinder pressure signal, and vehicle speed signal (calculated via GPS or wheel speed). The intelligent tire control module incorporates a joint estimator based on vehicle dynamics and a tire model, mainly comprising the following sub-modules: Tire force estimator: Utilizing the vehicle's six-degree-of-freedom dynamics equations, and taking lateral acceleration, yaw rate, steering wheel angle, and wheel speed difference as inputs, it estimates the longitudinal force of each wheel online using either an extended Kalman filter (EKF) or an unscented Kalman filter (UKF). Lateral force and vertical force Among them, the longitudinal force is related to the wheel speed change rate and the driving / braking torque, the lateral force is related to the slip angle through a linear tire model or magic formula, and the vertical force is estimated by the suspension displacement sensor and corrected by combining the lateral load transfer model.

[0045] Tire deformation estimation: Using the high-frequency vibration components in the wheel speed signal (characteristic frequencies of tire deformation during tire rolling) and the periodic fluctuations in wheel angular velocity, radial strain is inferred through spectral analysis or a neural network regression model. and circumferential strain The general trend of change.

[0046] Tread temperature field distribution matrix estimation: A tire thermal model is established, inputting parameters such as ambient temperature, vehicle speed, driving / braking force slip work, tire rolling resistance, and brake heat conduction. The transient temperature of different regions of the tread is estimated through a thermal balance differential equation. Due to the lack of direct measurement, the temperature accuracy is relatively low (approximately ±3~5℃), but it can still reflect the trend of temperature gradient changes.

[0047] Tire pressure distribution matrix estimation: Static and dynamic axle load transfers are calculated using a vehicle dynamics model, and then combined with a simplified tire contact patch model (assuming a parabolic or normal pressure distribution) to generate a virtual pressure distribution matrix, which is used to calculate an approximate value for the contact patch center offset. Although the spatial resolution is not as good as sensor-based solutions, it is sufficient to provide a threshold for triggering control intervention in scenarios requiring continuous monitoring.

[0048] This software solution requires no additional hardware costs, offers an update frequency of 50Hz~100Hz, and outputs all estimated parameters as real-time state parameters, supporting the basic requirements of dynamic management of attachment limits and multi-physics coupling compensation strategies. Although the accuracy of individual parameters is slightly lower than that of Method 1 (e.g., longitudinal force error of approximately 8%~10%), its economic efficiency is excellent, making it suitable as a solution for high-performance vehicles or the aftermarket.

[0049] Regardless of whether Method 1 or Method 2 is used, the intelligent tire control module packages the aforementioned real-time status parameters at a cycle of no less than 100Hz and transmits them to the integrated computing control module via an internal bus (SPI or CAN) to provide the chassis domain with tire status data in a unified format. The two methods can be cross-validated or redundantly backed up depending on the vehicle configuration or real-time operating conditions: for example, both methods can be used simultaneously in highly automated vehicles to improve robustness through a fusion algorithm; Method 2 can be used alone in ordinary vehicles to achieve widespread technological accessibility.

[0050] In this embodiment, a dual-channel communication protocol is used between the intelligent tire control module and the integrated computing control module: A high-speed communication channel is used to transmit real-time control data at a rate not lower than a preset first frequency. The real-time control data includes at least tire longitudinal force, lateral force, vertical force, and peak road adhesion coefficient. The diagnostic and configuration channel is used to transmit diagnostic and configuration data at a rate not lower than a preset second frequency. The diagnostic and configuration data includes at least the tire temperature gradient matrix and the tire health index. The first frequency is greater than the second frequency, and the two channels are synchronized in time.

[0051] Specifically, the intelligent tire control module and the integrated computing control module employ a dual-channel communication protocol to achieve the separate transmission of high-real-time control data and low-frequency diagnostic configuration data. This protocol includes two independent physical or logical channels: a high-speed communication channel and a diagnostic and configuration channel. Both share the same communication interface (such as a controller supporting a hybrid CAN FD and FlexRay protocol), but are isolated from each other in terms of data transmission priority, scheduling strategy, and bandwidth allocation.

[0052] A high-speed communication channel is used to carry dynamic data with the highest real-time requirements for vehicle stability control. This channel periodically pushes real-time control data to the integrated computing control module at a frequency no less than a preset first frequency (e.g., 100Hz or 200Hz). This data includes at least: tire longitudinal force, lateral force, and vertical force directly acquired or preliminarily calculated by the intelligent tire control module, as well as the updated peak road adhesion coefficient after temperature-friction compensation. The longitudinal and lateral forces are used for real-time friction ellipse monitoring and tire force distribution, the vertical force is used for load transfer estimation, and the peak road adhesion coefficient serves as the core boundary parameter for the dynamic adhesion limit management strategy. To ensure millisecond-level response to control commands, this channel employs priority-based message scheduling. Information such as longitudinal force, lateral force, and μ_peak is mapped to the highest-priority message identifier (ID), ensuring priority effectiveness during bus arbitration.

[0053] The diagnostics and configuration channel is used to transmit data that has relatively low real-time requirements but is crucial for system health management and parameter calibration. This channel transmits diagnostics and configuration data at a rate no lower than a preset second frequency (e.g., 10Hz or 20Hz), which is significantly lower than the first frequency to avoid excessive bus bandwidth consumption. The diagnostics and configuration data includes at least: a tire temperature gradient matrix (e.g., temperature values ​​of each cell in a 16×4 partition and calculated circumferential / lateral gradients) and a tire health index (THI, a comprehensive score from 0 to 100). The temperature gradient matrix is ​​used for temperature-stiffness / friction correction in multiphysics coupling compensation strategies, while the THI is used to assess tire aging and wear levels and trigger maintenance reminders or control parameter degradation. In addition, this channel can also transmit downlink configuration frames for the intelligent tire control module's self-test status (e.g., sensor drift, communication failure), firmware version information, and dynamic calibration parameters (e.g., temperature compensation coefficient, wear threshold value).

[0054] Strict time synchronization is maintained between the two channels to ensure that the integrated computing control module can align the time reference when fusing data from different channels. The synchronization mechanism is achieved as follows: the intelligent tire control module maintains a local timestamp counter (microsecond resolution), and each frame of high-speed channel data (e.g., 100 frames per second) is accompanied by a current timestamp; the diagnostic channel data frames are also timestamped, and when diagnostic data and high-speed data are temporally correlated (e.g., using a temperature gradient matrix to correct the peak adhesion coefficient of the road surface), the integrated computing control module uses nearest neighbor interpolation or linear interpolation methods to align the diagnostic data with the high-speed data in time. Furthermore, the communication protocol can also adopt a simplified version of IEEE 802.1AS or a similar Precision Time Protocol (PTP). The integrated computing control module acts as the master clock, periodically sending synchronization messages. The intelligent tire control module calibrates its local clock based on these synchronization messages, keeping the synchronization error between the two modules within one sampling period (e.g., 10ms). This ensures that under complex and harsh operating conditions, the matching error between tire mechanical state and temperature / health data will not substantially affect control accuracy. This dual-channel time synchronization mechanism satisfies the high-frequency refresh requirements of the chassis domain control for tire force, while also enabling low-frequency reliable transmission of slowly changing parameters such as temperature field and health status, thus achieving the best balance between bandwidth utilization and control performance.

[0055] In this embodiment, the stability control strategy includes at least: Multi-physics field coupling compensation strategy, adhesion limit dynamic management strategy, distributed tire force optimization allocation strategy, and failure tolerance collaborative control strategy.

[0056] Specifically, the stability control strategy executed by the integrated calculation control module includes at least four interrelated sub-strategies: The multiphysics coupling compensation strategy utilizes information such as the temperature gradient matrix, pressure distribution matrix, and slip ratio fed back by the intelligent tire to correct tire stiffness, friction coefficient, and vertical load in real time. It eliminates the influence of temperature and wear on tire force estimation and outputs high-precision three-dimensional tire force after full temperature compensation, providing an accurate input basis for subsequent control.

[0057] The dynamic adhesion limit management strategy calculates the available adhesion coefficient of each wheel based on the compensated three-dimensional tire force and the corrected peak road adhesion coefficient. Based on this, the vehicle stability state is divided into comfort zone, stable zone and limit zone, and normal, coordinated or emergency control modes are executed in each zone. In the limit zone, a strategy of reducing torque and prioritizing braking is automatically implemented. In the area of ​​sudden change in adhesion coefficient, smooth transition control is initiated. When there is a large difference in adhesion between the left and right wheels, lateral torque transfer is triggered.

[0058] The distributed tire force optimization allocation strategy takes the sum of the absolute values ​​of the deviations between the actual longitudinal force and the expected longitudinal force of each wheel plus the weighted sum of the adhesion margin as the objective function. The optimal braking torque allocation is solved by the optimization algorithm, and the result is converted into a braking torque command and sent to the braking system, so as to retain the maximum tire adhesion margin while ensuring braking accuracy.

[0059] The failure-tolerant collaborative control strategy utilizes the high-precision tire force measured in real time by intelligent tires to quickly identify and compensate for local failures in the braking, steering, and drive systems: when braking fails, it is remedied by transferring the torque of the wheels on the same or opposite side; when steering fails, it generates an equivalent steering angle using the difference in lateral force and compensates for the yaw moment through torque vector; when drive fails, it redistributes the torque of the failed motor to the healthy wheels based on the adhesion coefficient of each wheel, thereby maintaining the basic stability of the vehicle under single actuator failure and significantly reducing the risk of accidents.

[0060] The four strategies above are interconnected and clearly hierarchical, working together to achieve highly robust and stable chassis domain control under multiple operating conditions, wide temperature ranges, and fault conditions.

[0061] In this embodiment, the multiphysics coupling compensation strategy includes: Temperature-stiffness compensation: Based on the tire temperature gradient matrix, the real-time tread temperature is determined, and the preset temperature-stiffness compensation function is called to correct the tire's longitudinal stiffness, lateral stiffness, and vertical stiffness in real time. Temperature-friction compensation: Based on the real-time tread temperature, a preset temperature-friction compensation function is called to correct the tire-road friction coefficient in real time, so as to update the peak road adhesion coefficient; Temperature-load compensation: Based on the real-time tread temperature, the preset temperature-load compensation function is called to correct the vertical force by temperature, and the temperature-corrected vertical load is obtained. Three-dimensional force compensation output: The temperature-corrected tire stiffness, updated friction coefficient, and temperature-corrected vertical load are combined with the tire slip ratio and the tire three-dimensional force after full temperature compensation is calculated by a preset multi-physics coupling model. Real-time update: The compensated three-dimensional tire force is updated at a preset cycle for use in dynamic adhesion limit management strategies, distributed tire force optimization allocation strategies, and failure tolerance collaborative control strategies.

[0062] Specifically, the multiphysics coupling compensation strategy is a fundamental compensation step executed by the integrated calculation and control module. It aims to eliminate the influence of temperature on tire mechanical properties and output high-precision temperature-compensated three-dimensional force. This strategy specifically includes the following: Temperature-stiffness compensation: The integrated calculation and control module obtains the tire temperature gradient matrix from the normalized tire state parameter package and calculates the real-time tread temperature. (For example, taking the weighted average of all partitions). Then, a pre-calibrated temperature-stiffness compensation function is called, which takes the form of a quadratic polynomial with a reference temperature. Tire longitudinal stiffness at 25°C (typical value) Lateral stiffness and vertical stiffness Based on this, the primary temperature coefficient was obtained using bench tests. and secondary temperature coefficient Calculate the actual stiffness at the current temperature: ; This correction can effectively compensate for changes in tire stiffness caused by low-temperature hardening or high-temperature softening, and avoid errors in tire force estimation due to stiffness deviations.

[0063] Temperature-friction compensation: based on real-time temperature of the same tread. The integrated calculation and control module calls the temperature-friction compensation function. This function, based on Arrhenius's law, describes the exponential relationship between the coefficient of friction of rubber materials and temperature. A reference temperature is used. The calibrated coefficient of friction below For reference, given the activation energy of the rubber material (Unit: J / mol) and universal gas constant (8.314 J / (mol·K)), calculate the actual coefficient of friction at the current temperature: ; in and Kelvin temperature was used for all measurements. Corrected coefficient of friction. Immediately used to update the peak road adhesion coefficient in the normalized tire condition parameter package. This solves the problem of inaccuracy in the fixed friction coefficient model under low-temperature ice skating and high-temperature thermal decay conditions.

[0064] Temperature-load compensation: The integrated calculation control module obtains the measured vertical load from the intelligent tire control module, which is obtained by integrating the tread pressure distribution matrix. Considering that temperature changes can cause pressure sensor sensitivity drift and tire thermal expansion can lead to changes in the contact area, the system calls a temperature-load compensation function, which is typically in quadratic polynomial form: ; in and This is the load temperature compensation coefficient (calibrated through bench testing). The temperature-corrected vertical load is then obtained. This value is more accurate than the load estimated by simply using suspension displacement sensors, and can reduce the load transfer coefficient error to below 3%.

[0065] Three-dimensional force compensation output: Tire stiffness after temperature correction as described above Updated coefficient of friction and temperature-corrected vertical load Combined with the tire longitudinal slip ratio obtained from vehicle dynamics models or wheel speed sensors Lateral slip ratio and vertical compressibility The three-dimensional forces of the tire after temperature compensation are calculated using a pre-defined multiphysics coupling model. This model is based on an extended form of the magic formula, specifically: ; in This is the tire's reference vertical load (calibrated value). This represents the actual axle load of the vehicle (which can be obtained by fusing data from suspension sensors and a pressure matrix). This formula couples stiffness, friction, load, and slip ratio together, outputting a fully temperature-compensated longitudinal force. Lateral force and vertical force .

[0066] Real-time updates: The integrated calculation and control module repeatedly executes all the above compensation steps at a preset period (e.g., 10 milliseconds, corresponding to a 100Hz update rate), continuously updating the compensated tire three-dimensional forces. These high-precision three-dimensional forces serve as core inputs, directly used in the adhesion limit dynamic management strategy (calculating the available adhesion coefficient), the distributed tire force optimization allocation strategy (the actual force values ​​in the objective function), and the failure-tolerant collaborative control strategy (judging braking force deviation and lateral force difference). Through this real-time update mechanism, even when the vehicle is driven in extremely cold environments of -30℃ or high temperatures of 80℃, the overall control accuracy of the chassis domain control unit can still be maintained above 95%, effectively ensuring the robustness of the vehicle's stability control.

[0067] In this embodiment, the attachment limit dynamic management strategy includes: The wheel's used adhesion coefficient is calculated based on the compensated three-dimensional tire force, and the road surface peak adhesion coefficient is corrected based on the tire temperature gradient matrix and tire health index, thereby calculating the usable adhesion coefficient. The current stability control zone is determined based on the preset threshold range of the available adhesion coefficient of the wheel, where the stability control zone includes the comfort zone, the stable zone, and the extreme zone; The corresponding control strategy is executed according to the current stability control zone: the normal control mode is executed in the comfort zone, the coordinated control mode is executed in the stable zone, and the emergency control mode is executed in the extreme zone. Specifically, in the extreme zone, a control strategy that prioritizes torque reduction over braking is automatically implemented, and the rate of change of steering angle is limited to a preset threshold. In the region where the peak road adhesion coefficient changes gradient, a smooth transition algorithm is used to control the rate of change of longitudinal force, and when the difference between the peak road adhesion coefficients of the left and right wheels exceeds a preset threshold, lateral torque transfer is initiated.

[0068] Specifically, the adhesion limit dynamic management strategy is the core decision-making layer that uses the high-precision three-dimensional tire force output by the multi-physics coupling compensation strategy to perform zoned judgment and active intervention on the real-time stability of the vehicle. The specific implementation steps of this strategy are as follows.

[0069] (a) Calculating the available adhesion coefficient of the wheel: The integrated calculation control module receives the three-dimensional force of the tire after compensation. Calculate the longitudinal adhesion coefficient already used and lateral adhesion coefficient Thus, the total used adhesion coefficient is obtained. The original peak road adhesion coefficient is obtained from the normalized tire condition parameter package. The peak adhesion coefficient was corrected using the tire temperature gradient matrix and the tire health index (THI). Finally, the usable coefficient of adhesion for the wheel is calculated. .

[0070] Tire Health Index (THI) Calculation: THI consists of the wear health sub-index and temperature aging sub-index The weighted fusion yields: ; ; ; in The remaining pattern depth , These are the initial and scrap depths, respectively. This is a correction factor for the wear rate; This is the equivalent aging time. For tire design life, For activation energy, The gas constant is... As the reference temperature, This is the real-time temperature history of the tire tread.

[0071] Corrected peak road surface adhesion coefficient: ; To correct the strength coefficient (usually taken as 0.1~0.3), the lower the THI (severe wear / aging), the smaller the corrected peak adhesion coefficient, and the more conservative the control strategy.

[0072] (II) Stability control region division: based on the calculated... The system compares it with two preset thresholds, thereby dividing the vehicle stability state into three control zones: when At that time, it was determined to be in the comfort zone, with sufficient tire grip and the vehicle in a good and stable state.

[0073] when When the tire is nearing its adhesion limit, it is considered to be in a stable zone and requires appropriate intervention.

[0074] when When the vehicle is in a critical condition, it is considered to be in a critical zone, meaning the tires are close to or have reached their adhesion limit, and the vehicle is at risk of instability, requiring emergency intervention.

[0075] The above thresholds (0.3 and 0.15) can be calibrated and adjusted according to vehicle type (such as passenger car, commercial vehicle) and tire characteristics.

[0076] (III) Regional Control Mode: Different control zones implement stability control strategies of varying intensities. Comfort Zone: Adopts conventional control mode with minimal active intervention, retaining only basic braking force distribution and steering assist to maintain driving smoothness and energy efficiency.

[0077] Stable Zone: The coordinated control mode is activated, and the comprehensive calculation control module begins to actively fine-tune the power torque, drive torque and steering angle, but the intervention intensity is low, aiming to prevent the vehicle slip ratio from increasing further.

[0078] Extreme Zone: Immediately switch to emergency control mode, forcibly take over partial actuator permissions, and implement high-intensity stability intervention.

[0079] (iv) Specific control actions within the limit zone: When the system determines that it has entered the limit zone, the following sub-strategies will be executed automatically: Torque reduction takes precedence over braking: First, the drive system is instructed to quickly reduce the drive torque at a preset torque decay rate (e.g., 500 Nm per second) to avoid slippage due to excessive drive force; only when torque reduction is insufficient or emergency deceleration is required will braking pressure be increased.

[0080] Limit the rate of change of steering angle: Limit the rate of change of steering angle of the electric power steering system to ensure that it does not exceed a preset threshold (e.g., 200° / s) to prevent the driver from suddenly exceeding the adhesion limit when making a sudden turn of the steering wheel.

[0081] Smooth transition in the gradient region of road surface adhesion coefficient: In the region of gradient change of peak road surface adhesion coefficient (e.g., a vehicle suddenly enters an icy or snowy road from an asphalt road), the intelligent tire control module detects the peak road surface adhesion coefficient of the left and right wheels or the front and rear axles in real time. The gradient of change. When detected. When drastic changes occur in space or time, the integrated calculation and control module initiates an adaptive exponential smooth transition algorithm to filter the desired longitudinal force, ensuring that the rate of change of the longitudinal force does not exceed a preset safety threshold (e.g., This helps prevent wheel slippage or vehicle jerking caused by sudden changes in road surface.

[0082] Let the current control period be Sampling interval (typical value) ,correspond The desired longitudinal force is The smoothed actual output longitudinal force is The formula for adaptive exponential smoothing filter is as follows: ; Among them, the filter coefficients Based on the current peak road adhesion coefficient gradient Or adjust the longitudinal force change rate in real time: when When the magnitude is large (due to drastic changes in road surface adhesion), reduce This enhances the smoothing effect, making longitudinal force changes more gradual; when When the surface adhesion is relatively small, increase This reduces the smoothing effect and ensures control response speed.

[0083] To ensure that the rate of change of longitudinal force is strictly limited, the system also employs amplitude limiting constraints: setting a maximum permissible single-step change. ,in , For vehicle mass. The actual output smooth longitudinal force should satisfy: ; By combining the aforementioned adaptive exponential smoothing and amplitude limiting methods, the system can automatically control the longitudinal force change rate under various road adhesion abrupt change conditions. This eliminates the impact and instability risks caused by road surface jumps, thereby improving the smoothness and stability of vehicle driving.

[0084] Lateral torque transfer: Utilizing the peak road surface adhesion coefficients of the left and right wheels provided by the intelligent tire control module (denoted as...). and ), calculate the difference .when When the difference exceeds a preset threshold (e.g., 0.2), the lateral torque transfer function is activated. Specifically, the torque vector control system transfers a portion of the drive torque from the low-adhesion wheel to the high-adhesion wheel; the maximum transfer amount is a preset percentage threshold of the total drive torque (e.g., 30%), determined by calibration. The transferred torque command is sent to the drive motor controller, generating an additional yaw moment to compensate for the yaw tendency caused by the asymmetry of left and right adhesion.

[0085] Through the aforementioned regional and multi-level decision-making and control, the attached limit dynamic management strategy achieves a seamless transition from normal driving to extreme conditions, significantly improving the stability and safety of vehicles on complex road surfaces such as ice and snow, and split-road surfaces.

[0086] In this embodiment, the distributed tire force optimization allocation strategy includes: The objective function is the sum of the absolute values ​​of the deviations between the compensated longitudinal forces of each tire and the desired longitudinal forces, plus the safety margin weighting coefficient multiplied by the sum of the available adhesion coefficients of each tire. The optimal braking torque allocation is solved by an optimization algorithm to minimize the objective function; The optimal braking torque allocation result obtained from the solution is converted into a braking torque command and sent to the braking system.

[0087] Specifically, the distributed tire force optimization allocation strategy is the core execution step that further solves for the optimal braking torque allocation of each wheel, based on the multi-physics coupling compensation strategy and the dynamic management strategy of adhesion limits. The core idea of ​​this strategy is to maximize the adhesion margin of each tire while ensuring the accuracy of braking torque tracking, so as to avoid wheel lock-up or excessive slippage.

[0088] Objective function: Let the first... One wheel ( The actual longitudinal force after multiphysics coupling compensation is The expected longitudinal force given by the upper-level vehicle control algorithm (such as driver intent and stability control requirements) is: Meanwhile, the available adhesion coefficient for each wheel has been calculated using the adhesion limit dynamic management strategy. The objective function for optimizing allocation is defined as follows: ; in The safety margin weighting coefficient (calibrated value, typically ranging from 0.1 to 0.5) is used to balance braking torque tracking error and adhesion margin retention. The first part of the objective function makes the actual longitudinal force as close as possible to the desired value to ensure braking accuracy; the second part encourages the system to distribute less braking force on wheels using smaller adhesion margins, thereby retaining more safety reserves.

[0089] Optimization solution: The integrated calculation control module uses the braking torque of each wheel. To optimize the variables, the relationship between braking torque and longitudinal force was used. ( The objective function is transformed into a function of braking torque (where the wheel rolling radius is used). Optimization algorithms such as weighted least squares or quadratic programming are used to solve the problem under the following constraints. Minimize the optimal braking torque distribution: Total braking torque requirement: (Determined by the master cylinder pressure or regenerative braking requirements); The braking torque of each wheel is limited by the maximum capacity of the brakes and the tire adhesion limit (i.e., ).

[0090] Command issued: The optimal braking torque distribution result obtained from the solution. The braking torque is converted into specific braking torque commands and sent to the brake actuators of each wheel (such as the hydraulic adjustment unit or electromechanical brake caliper of the Electronic Stability Program (ESP)) via the execution control module. Additionally, if the vehicle is equipped with a regenerative braking system, the strategy can also coordinate the distribution of electric braking torque with hydraulic braking torque.

[0091] This distributed tire force optimization strategy not only accurately tracks the total braking force demand during braking but also dynamically adjusts the torque distribution to each wheel, ensuring that the adhesion margin of each tire is as uniform as possible while maintaining sufficient safety margin. This significantly improves braking stability and anti-skid capability. Theoretical and experimental results show that, compared to traditional even distribution or axle load-based braking distribution methods, this strategy can shorten braking distance on high-adhesion roads by approximately 5% and improve stability on low-adhesion roads by more than 30%.

[0092] In this embodiment, the failure-tolerance collaborative control strategy includes: Brake failure compensation: When the deviation between the theoretical braking force of a wheel and the compensated longitudinal force of the tire is detected to be greater than the preset deviation threshold, the target braking torque of the wheel is automatically distributed to the healthy wheels on the same or opposite sides according to the adhesion coefficient ratio, and the compensation response time is less than the preset response time threshold. Steering failure coordination: When the electric power steering system partially fails, the equivalent steering angle is calculated based on the difference in lateral force between the left and right wheels after compensation, and a compensating yaw moment is generated through torque vector control; Drive failure transfer: When a single drive motor fails, the target torque of the failed motor is redistributed to the healthy wheels based on the updated peak road adhesion coefficient distribution of each wheel, ensuring that the remaining total driving force is not lower than the preset proportion threshold of the total driving force before failure.

[0093] Specifically, the failure-tolerant collaborative control strategy targets local actuator failures in the braking, steering, and drive systems. It utilizes high-precision tire force feedback from intelligent tires in real time to achieve rapid fault identification and fault-tolerant reconstruction, ensuring the stability and safety of the vehicle under single-point failures.

[0094] (1) Braking failure compensation: The integrated calculation and control module compares the theoretical braking force of a wheel with the actual longitudinal force directly measured by the smart tire in real time. The theoretical braking force is estimated by the braking system based on wheel cylinder pressure. ; in, This is the coefficient of friction (calibrated value) between the brake friction pads and the brake disc. The real-time pressure of the wheel cylinder (collected by the brake pressure sensor). The working area of ​​the cylinder piston. The effective operating radius of the brake. Let be the wheel rolling radius. Calculate the relative deviation between the theoretical braking force and the actual measured value: ; when When the (preset deviation threshold) is reached, the braking of that wheel is determined to be faulty. At this point, the chassis domain controller, based on the vehicle's total braking demand and the remaining wheel adhesion limits, distributes the target braking torque of the faulty wheel to the healthy wheels on the same or opposite side according to the adhesion coefficient ratio. Let the set of healthy wheels be... , No. The available coefficient of adhesion for a healthy wheel is The additional braking torque allocated to this wheel is: ; The compensation response time is less than the preset response time threshold (e.g., 80ms) to ensure that the braking torque is not excessively lost due to the failure of a single wheel, while avoiding the locking of healthy wheels.

[0095] (2) Steering failure coordination: When the electric power steering (EPS) system partially fails (e.g., loss of motor assistance or sensor failure), the chassis domain controller compensates for the lateral forces of the left and right wheels based on intelligent tire measurements. and Calculate the equivalent steering angle: ; in For the vertical load on the wheel, This is the vehicle chassis calibration coefficient (related to tire side stiffness and steering geometry). This equivalent steering angle reflects the deviation between the actual yaw tendency and the driver's intention. The controller then generates a compensating yaw moment through torque vectoring control (applying differentiated drive / braking torques to the left and right wheels). The maximum compensation torque can reach 2500 N·m, which is used to simulate the effect of active steering and maintain the vehicle trajectory.

[0096] (3) Drive failure transfer: When a single drive motor fails, the chassis domain controller uses the peak adhesion coefficient of each wheel collected in real time by the intelligent tires. (Corrected using temperature gradient matrix and tire health index) As the core constraint, the target torque of the failed motor is redistributed according to the adhesion capability of the healthy wheels. Let the original target torque of the failed motor be... Healthy motors are assembled as , No. The maximum additional torque of each healthy motor is limited by the adhesion margin of that wheel. After distribution, the remaining total driving force is ensured to be no less than a preset proportion threshold (e.g., 70%) of the total driving force before failure, while ensuring that the output of each healthy motor does not exceed its adhesion limit, thereby maintaining the vehicle's power performance and driving stability.

[0097] Through the aforementioned braking failure compensation, steering failure coordination, and drive failure transfer mechanisms, the failure-tolerant collaborative control strategy fully utilizes the precise tire force and adhesion coefficient information provided by the smart tires to quickly reconstruct control commands after a single actuator failure, reducing accident risk by more than 60%.

[0098] In this embodiment, a tire-actuator dynamic matching mechanism is also included: Wear-Steering Coordination: The intelligent tire control module detects and reports the wear difference between the inner and outer sides of the tire; when the wear difference exceeds the preset wear threshold, the comprehensive calculation control module generates a four-wheel alignment adjustment command, which is sent to the steering system by the execution control module to dynamically adjust the kingpin inclination angle and toe angle. Stiffness-damping coordination: The intelligent tire control module acquires and reports the tire body deformation; the comprehensive calculation control module identifies tire stiffness changes based on the tire body deformation. When the stiffness change exceeds the preset stiffness change threshold, a damping adjustment command is generated and sent to the suspension system by the execution control module to adjust its damping coefficient in real time. Pressure-load coordination: The intelligent tire control module acquires and reports the pressure distribution matrix; the comprehensive calculation control module uses the pressure distribution matrix to calculate the actual load transfer coefficient and corrects the load estimation error based on the suspension displacement sensor.

[0099] Specifically, the system also integrates a tire-actuator dynamic matching mechanism. This mechanism utilizes the sensing capabilities of intelligent tires to actively adjust four-wheel alignment, suspension damping, and load estimation to achieve tire wear suppression, stiffness-damping matching, and improved load measurement accuracy. This mechanism serves as a long-term optimization and auxiliary function for chassis domain control, periodically or event-drivenly adjusting actuator parameters.

[0100] Wear-Steering Coordination: The intelligent tire control module monitors the strain distribution in different areas of the tire tread in real time through an embedded strain sensor array, and calculates the wear difference between the inner and outer sides using a wear estimation model (such as Gaussian process regression). .when When the wear exceeds a preset threshold (e.g., 1.5 mm), the intelligent tire control module reports the wear difference data to the integrated calculation control module. The integrated calculation control module then calculates the required adjustment to the kingpin inclination angle based on a mapping model between tire wear distribution and four-wheel alignment parameters. and toe angle correction The adjustment logic is as follows: the greater the wear difference, the greater the correction amount, and the correction direction should make the tire force more even and suppress the tendency of uneven wear (typical adjustment range: kingpin inclination ±0.5°, toe angle ±0.3°). The integrated calculation control module generates four-wheel alignment adjustment commands, which are sent by the execution control module to the steering actuators (such as the alignment adjustment mechanism in active front wheel steering systems or steer-by-wire systems) to dynamically adjust the kingpin inclination and toe angle, thereby delaying uneven wear and restoring the consistency of steering response.

[0101] Stiffness-damping coordination: Intelligent tire control module acquires radial strain of the tire carcass and circumferential strain These tire deformations are then reported to the integrated calculation and control module. The integrated calculation and control module uses a pre-calibrated strain-stiffness mapping model (e.g., an approximate linear relationship obtained through finite element analysis or bench testing) to identify the current longitudinal stiffness of the tire. and lateral stiffness Let the calibration stiffness at the reference temperature be... , Then the change in stiffness is , When the absolute value of the stiffness change exceeds a preset stiffness change threshold (e.g., 5%), the integrated calculation and control module generates a damping adjustment command based on the stiffness-damping matching strategy: when stiffness increases (tires become harder), the damping coefficient of the continuous damping control (CDC) shock absorber is appropriately increased to suppress high-frequency vibrations of the vehicle body; when stiffness decreases (tires become softer), the damping coefficient is decreased to improve comfort. Damping adjustment amount. It has a positive correlation with the rate of change of stiffness, with a typical adjustment step size of 50 N·s / m. The adjustment command is sent from the execution control module to the suspension system (CDC shock absorber) to adjust its damping coefficient in real time, thereby achieving dynamic matching between the suspension and the tire.

[0102] Pressure-load coordination: The intelligent tire control module utilizes an embedded MEMS pressure sensor array to acquire the tread pressure distribution matrix. (in The number of circumferential partitions, (This refers to the number of horizontal partitions). The integrated calculation and control module calculates the number of partitions using the following formula. Actual load transfer coefficient of each wheel : ; in, For the first The first tire surface of the car Real-time pressure value of each pressure sensing unit. This is the grounding area corresponding to the sensing unit. This is the reference load (used for normalization) corresponding to the vehicle's curb weight. The calculated value is... This characterizes the actual load change ratio of the current wheel relative to the reference load. Simultaneously, the chassis domain control unit obtains displacement-based load estimates from traditional suspension displacement sensors. The integrated calculation and control module will use the actual load transfer coefficient calculated by the smart tire. The load estimation is fused with the suspension displacement estimate (e.g., using complementary filtering or Kalman filtering) to correct the latter's systematic errors. The corrected load estimation error can be reduced to below a preset error threshold (e.g., 3%), thereby providing a more accurate vertical force input for brake force distribution, ESP yaw moment calculation, and adhesion coefficient estimation.

[0103] Through the three coordination mechanisms mentioned above, the tire-actuator dynamic matching mechanism makes full use of the multi-dimensional perception information of the smart tire to achieve active optimization of four-wheel alignment parameters, suspension damping and load estimation, effectively alleviates tire wear imbalance, improves ride comfort and handling stability, and provides accurate load input for high-precision chassis control.

[0104] like Figure 2 As shown, the workflow of the chassis domain control stability control system in this embodiment is as follows: First, after the system starts up, it enters the "multi-source sensor data acquisition" stage, which includes the demand input module collecting driver demand signals (accelerator, brake, steering, etc.) and vehicle status signals (vehicle speed, yaw rate, wheel speed, etc.). At the same time, the intelligent tire control module obtains real-time tire status parameters (three-dimensional force, tire deformation, temperature field, pressure distribution, etc.) through embedded sensor arrays or software algorithms.

[0105] Subsequently, the data is sent to the "intelligent tire control module," which generates a normalized tire state parameter package (including tire three-dimensional force, road surface peak adhesion coefficient, temperature gradient matrix, and health index, etc.) based on real-time state parameters and then transmits it to the "integrated calculation and control module."

[0106] The integrated calculation and control module executes a multi-physics coupling compensation strategy, outputting the tire's three-dimensional force after full temperature compensation. Then, combined with the adhesion limit dynamic management strategy, it determines the current vehicle stability state as either "comfort zone," "stable zone," or "limit zone." Based on the determination result, it enters either "normal control mode," "coordinated control mode," or "emergency control mode."

[0107] Under different control modes, the system outputs corresponding execution actions: In the normal control mode, the main functions performed include drive torque distribution, basic brake holding, and steering angle compensation. In coordinated control mode, features such as torque vector control, four-wheel independent braking, active steering intervention, and suspension stiffness adjustment are added. In emergency control mode, steering angle limitation, differential braking adjustment, drive torque cut-off, maximum suspension damping, and compound control output or safety mode output are further activated.

[0108] The aforementioned action commands are aggregated into the "Arbitration Management Module," which prioritizes and coordinates conflicts among the commands to ensure they do not interfere with each other. Finally, the arbitrated commands are sent to the braking, drive, steering, and suspension actuators via the execution control module to achieve vehicle stability control.

[0109] The entire process forms a closed loop of "perception → compensation → judgment → decision → arbitration → execution", realizing a complete working link from multi-source data acquisition to final control command output.

[0110] Example 2

[0111] This embodiment provides a chassis domain control stability control method based on intelligent tires, applied to the system described in Embodiment 1. The method includes the following steps: Step S1: Collect driver demand signals, vehicle status signals, and real-time tire status parameters; Step S2: Construct a normalized tire state parameter package based on the real-time tire state parameters. The parameter package includes tire three-dimensional force, road peak adhesion coefficient and tire health status data after multi-physics field coupling compensation. Step S3: Based on the parameter package and vehicle status signals, perform dynamic management of adhesion limits, distributed tire force optimization and allocation, and failure-tolerant collaborative control to generate chassis control commands; Step S4: Priority arbitration and conflict coordination are performed on the chassis control commands, and the commands are sent to the execution unit for execution to achieve integrated control of vehicle stability.

[0112] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A chassis domain control stability control system based on intelligent tires, characterized in that, include: Demand input module, chassis domain control unit, and execution unit; The demand input module is used to collect driver demand signals and vehicle status signals; The chassis domain control unit is communicatively connected to the demand input module, and includes: The intelligent tire control module is used to acquire real-time tire status parameters and generate a normalized tire status parameter package based on the real-time status parameters; the normalized tire status parameter package includes at least: tire three-dimensional force, road surface peak adhesion coefficient, tire temperature gradient matrix and tire health index; The integrated calculation and control module is used to receive and execute stability control strategies based on the normalized tire state parameter package, driver demand signal and vehicle state signal to generate control commands; The arbitration management module is used to perform priority arbitration and conflict coordination on the control commands generated by the integrated calculation and control module; The diagnostic management module is used for fault diagnosis and status monitoring of the system; The execution control module is used to send the arbitrated control commands to the execution unit; The execution unit includes at least a braking system, a drive system, a steering system, and a suspension system, and is used to receive and execute control commands issued by the execution control module.

2. The chassis domain control stability control system based on intelligent tires according to claim 1, characterized in that, The intelligent tire control module obtains the real-time status parameters through any of the following methods: Method 1: Real-time acquisition of tire three-dimensional force, temperature field distribution and tire deformation through a multi-physics field sensor array embedded in the tire to obtain the real-time state parameters; Method 2: Based on the vehicle state signal, the tire-vehicle coupled dynamic state is perceived, modeled and evaluated in real time through software algorithms to obtain the real-time state parameters; The real-time state parameters include at least the tire longitudinal force, lateral force, vertical force, tire body deformation, tread temperature field distribution matrix, and tread pressure distribution matrix.

3. The chassis domain control stability control system based on intelligent tires according to claim 1, characterized in that, The intelligent tire control module and the integrated computing control module use a dual-channel communication protocol: A high-speed communication channel is used to transmit real-time control data at a rate not lower than a preset first frequency. The real-time control data includes at least tire longitudinal force, lateral force, vertical force, and peak road adhesion coefficient. A diagnostic and configuration channel is used to transmit diagnostic and configuration data at a rate not lower than a preset second frequency. The diagnostic and configuration data includes at least the tire temperature gradient matrix and the tire health index. The first frequency is greater than the second frequency, and the two channels are time-synchronized.

4. The chassis domain control stability control system based on intelligent tires according to claim 1, characterized in that, The stability control strategy includes at least the following: Multi-physics field coupling compensation strategy, adhesion limit dynamic management strategy, distributed tire force optimization allocation strategy, and failure tolerance collaborative control strategy.

5. The chassis domain control stability control system based on intelligent tires according to claim 4, characterized in that, The multiphysics coupling compensation strategy includes: Temperature-stiffness compensation: Based on the tire temperature gradient matrix, the real-time tread temperature is determined, and a preset temperature-stiffness compensation function is called to correct the tire's longitudinal stiffness, lateral stiffness, and vertical stiffness in real time. Temperature-friction compensation: Based on the real-time tread temperature, a preset temperature-friction compensation function is called to correct the tire-road friction coefficient in real time, so as to update the peak road adhesion coefficient; Temperature-load compensation: Based on the real-time tread temperature, a preset temperature-load compensation function is called to perform temperature correction on the vertical force to obtain the temperature-corrected vertical load; Three-dimensional force compensation output: The temperature-corrected tire stiffness, the updated friction coefficient, and the temperature-corrected vertical load are combined with the tire slip ratio and the tire three-dimensional force after full temperature compensation is calculated by a preset multi-physics coupling model. Real-time updates: The compensated three-dimensional tire force is updated at a preset cycle for use in dynamic adhesion limit management strategies, distributed tire force optimization allocation strategies, and failure tolerance collaborative control strategies.

6. The chassis domain control stability control system based on intelligent tires according to claim 5, characterized in that, The adhesion limit dynamic management strategy includes: The wheel's used adhesion coefficient is calculated based on the compensated three-dimensional tire force, and the road surface peak adhesion coefficient is corrected based on the tire temperature gradient matrix and tire health index, thereby calculating the usable adhesion coefficient. The current stability control zone is determined based on a preset threshold range where the available adhesion coefficient of the wheel is located, wherein the stability control zone includes a comfort zone, a stable zone, and a limit zone; The corresponding control strategy is executed according to the current stability control zone: the normal control mode is executed in the comfort zone, the coordinated control mode is executed in the stable zone, and the emergency control mode is executed in the extreme zone. Specifically, in the extreme zone, a control strategy that prioritizes torque reduction over braking is automatically implemented, and the steering angle change rate is limited to a preset angle change rate threshold. In the region where the peak road surface adhesion coefficient gradient changes, a smooth transition algorithm is used to control the longitudinal force change rate, and when the difference between the peak road surface adhesion coefficients of the left and right wheels exceeds a preset threshold, lateral torque transfer is initiated.

7. The chassis domain control stability control system based on intelligent tires according to claim 5, characterized in that, The distributed tire force optimization allocation strategy includes: The objective function is the sum of the absolute values ​​of the deviations between the compensated longitudinal forces of each tire and the desired longitudinal forces, plus the safety margin weighting coefficient multiplied by the sum of the available adhesion coefficients of each tire. The optimal braking torque allocation is solved by an optimization algorithm to minimize the objective function; The optimal braking torque allocation result obtained from the solution is converted into a braking torque command and sent to the braking system.

8. The chassis domain control stability control system based on intelligent tires according to claim 5, characterized in that, The failure-tolerant collaborative control strategy includes: Brake failure compensation: When the deviation between the theoretical braking force of a wheel and the compensated longitudinal force of the tire is detected to be greater than the preset deviation threshold, the target braking torque of the wheel is automatically distributed to the healthy wheels on the same or opposite sides according to the adhesion coefficient ratio, and the compensation response time is less than the preset response time threshold. Steering failure coordination: When the electric power steering system partially fails, the equivalent steering angle is calculated based on the difference in lateral force between the left and right wheels after compensation, and a compensating yaw moment is generated through torque vector control; Drive failure transfer: When a single drive motor fails, the target torque of the failed motor is redistributed to the healthy wheels based on the updated peak road adhesion coefficient distribution of each wheel, ensuring that the remaining total driving force is not lower than the preset proportion threshold of the total driving force before failure.

9. The chassis domain control stability control system based on intelligent tires according to claim 2, characterized in that, It also includes a tire-actuator dynamic matching mechanism: Wear-Steering Coordination: The intelligent tire control module detects and reports the wear difference between the inner and outer sides of the tire; when the wear difference exceeds a preset wear threshold, the integrated calculation control module generates a four-wheel alignment adjustment command, which is sent to the steering system by the execution control module to dynamically adjust the kingpin inclination angle and toe angle. Stiffness-damping coordination: The intelligent tire control module acquires and reports the tire body deformation; the comprehensive calculation control module identifies tire stiffness changes based on the tire body deformation, and when the stiffness change exceeds a preset stiffness change threshold, it generates a damping adjustment command, which is sent to the suspension system by the execution control module to adjust its damping coefficient in real time. Pressure-load coordination: The intelligent tire control module acquires and reports the pressure distribution matrix; The integrated calculation and control module uses the pressure distribution matrix to calculate the actual load transfer coefficient and corrects the load estimation error based on the suspension displacement sensor.

10. A chassis domain control stability control method based on intelligent tires, characterized in that, Applied to the system as described in any one of claims 1 to 9, the method comprises the following steps: Step S1: Collect driver demand signals, vehicle status signals, and real-time tire status parameters; Step S2: Construct a normalized tire state parameter package based on the real-time tire state parameters. The parameter package includes tire three-dimensional force, road surface peak adhesion coefficient and tire health status data after multi-physics field coupling compensation. Step S3: Based on the parameter package and vehicle status signal, perform dynamic management of adhesion limits, distributed tire force optimization allocation and failure-tolerant collaborative control to generate chassis control commands; Step S4: Priority arbitration and conflict coordination are performed on the chassis control commands, and the commands are sent to the execution unit for execution to achieve integrated control of vehicle stability.

Images