A method and system for dynamic calibration of tire pressure monitoring by integrating vehicle speed data

Abstract

The present application belongs to the field of automobile electronics, and specifically relates to a tire pressure monitoring dynamic calibration method and system fusing vehicle speed data, which comprises the following steps: acquiring real-time tire pressure, tire temperature, vehicle speed and ambient temperature; estimating tire theoretical temperature by using a dynamic thermodynamic model based on vehicle speed, ambient temperature and calibration parameters; fusing real-time tire temperature and theoretical temperature by Kalman filtering to obtain corrected internal temperature; and calculating cold tire pressure equivalent value according to real-time tire pressure, corrected internal temperature and cold tire reference temperature to judge tire pressure state. By adopting the above technical scheme, the present application can overcome the ambiguity of traditional tire pressure monitoring, provide a cold tire pressure equivalent value with more guiding significance, and enhance system accuracy, robustness and self-adaptive learning ability.

Description

Technical Field

[0001] This invention belongs to the field of automotive electronics technology, specifically a method and system for dynamic calibration of tire pressure monitoring that integrates vehicle speed data. Background Technology

[0002] Tire pressure monitoring systems (TPMS), as a key technology for improving vehicle safety, optimizing fuel economy, and reducing carbon emissions, are widely used globally and are mandated by law. Their core function is to acquire real-time tire pressure data and promptly alert the driver to abnormal tire pressure (too high or too low) to avoid traffic accidents and slow performance degradation. TPMS is an important component of modern automotive active safety systems, playing a significant role in ensuring driving safety and extending tire lifespan.

[0003] The current mainstream direct tire pressure monitoring system (dTPMS) uses sensor modules installed inside each tire, integrating pressure and temperature sensors, a microcontroller, an RF transmitter, and a power module. These sensors periodically collect real-time tire pressure and temperature data, which is wirelessly transmitted to an onboard receiver for decoding. The data is then sent to the control unit via the vehicle bus for processing, and finally presented as a digital display or indicator light. An alarm is triggered when the tire pressure deviates from a preset threshold. This direct measurement method solves the problems of long intervals, inconvenience, and inaccurate data associated with traditional manual tire pressure checks, providing drivers with accurate real-time tire pressure and temperature data.

[0004] However, dTPMS has inherent limitations at the theoretical level, particularly in providing consistent, interpretable, and operationally guiding tire pressure data. The core issue lies in the significant temperature dependence of tire pressure: when a vehicle is in motion, the internal temperature of the tire rises due to road friction, tire deformation, and ambient temperature. According to the ideal gas law, tire pressure increases with temperature, making the instantaneous tire pressure measured by dTPMS a dynamic variable highly dependent on the current temperature. Although the sensor can transmit temperature data, simply presenting instantaneous data or using simple fixed-coefficient compensation cannot match the "cold tire pressure" standard that drivers habitually refer to (the tire pressure when the tires are at the same temperature as the ambient temperature after a long period of parking, a benchmark value set by the tire manufacturer). Different vehicle speeds, mileage, road conditions, and driving habits can all lead to variations in tire temperature. Static or simple temperature compensation models cannot accurately capture complex dynamic relationships, resulting in dynamic deviations between instantaneous measurements and cold tire pressure standards. This can easily lead to driver misjudgments (such as hastily releasing air due to a hot high tire pressure alarm, resulting in insufficient cold tire pressure, or ignoring seemingly normal under-pressure under hot conditions). This reduces the practicality and operability of the system information, affects the driver's trust in the system, and may even cause drivers to miss real abnormal warnings. Therefore, this invention provides a dynamic calibration method and system for tire pressure monitoring that integrates vehicle speed data. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a dynamic calibration method for tire pressure monitoring that integrates vehicle speed data, which is applied to the tire pressure monitoring control unit of a vehicle. The tire pressure monitoring control unit communicates with at least one tire pressure sensor module, a vehicle speed sensor, and an ambient temperature sensor. The method includes the following steps:

[0007] The system acquires real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data. The real-time tire pressure and temperature data are periodically measured by tire pressure sensor modules installed inside the vehicle tires and wirelessly transmitted to the tire pressure monitoring and control unit. The real-time vehicle speed data is generated by a vehicle speed sensor and transmitted to the tire pressure monitoring and control unit via the vehicle data bus. The real-time ambient temperature data is generated by an ambient temperature sensor and transmitted to the tire pressure monitoring and control unit via the vehicle data bus.

[0008] Based on the real-time vehicle speed data, the real-time ambient temperature data, and historically stored calibration parameters, a dynamic thermodynamic model is used to estimate the real-time theoretical temperature inside the tire. This dynamic thermodynamic model establishes the relationship between heat generation, heat dissipation, and temperature changes inside the tire. Its core function is to convert the vehicle's real-time speed into the real-time heat generation rate inside the tire, while also considering heat exchange between the tire and the environment. Specifically, the dynamic thermodynamic model includes a heat generation function that takes the real-time vehicle speed as input and outputs an instantaneous heat generation power value, representing the heat generated per unit time due to tire deformation, friction, and other effects. The dynamic thermodynamic model further includes a heat dissipation function that takes the real-time theoretical temperature inside the tire and the real-time ambient temperature as input and outputs an instantaneous heat dissipation power value, representing the heat lost from the tire to the environment per unit time due to convection, conduction, radiation, and other effects. The dynamic thermodynamic model calculates the real-time theoretical temperature inside the tire by integrating the difference between the instantaneous heat generation power and the instantaneous heat dissipation power, combined with the tire's heat capacity parameters.

[0009] The real-time tire temperature data measured by the tire pressure sensor module is fused with the real-time theoretical temperature estimated by the dynamic thermodynamic model to obtain a corrected real-time internal temperature. This fusion process employs a Kalman filter algorithm. Specifically, the real-time tire temperature data serves as the input measurement value for the Kalman filter algorithm, while the real-time theoretical temperature serves as the input state prediction value. The Kalman filter algorithm dynamically adjusts weights, comprehensively considering both the measurement noise characteristics of the real-time tire temperature data and the model prediction uncertainty of the real-time theoretical temperature, to output a more accurate estimate of the tire's internal temperature—the corrected real-time internal temperature.

[0010] Based on the real-time tire pressure data, the corrected real-time interior temperature, and the preset cold tire reference temperature, an equivalent cold tire pressure value is calculated. The calculation process is based on the ideal gas law and is specifically expressed as: Equivalent cold tire pressure value = Real-time tire pressure data × (Preset cold tire reference temperature / Corrected real-time interior temperature). The preset cold tire reference temperature is a standardized temperature value, such as 20 degrees Celsius.

[0011] The equivalent cold tire pressure value is compared with a preset cold tire pressure threshold to determine the tire pressure status. If the equivalent cold tire pressure value exceeds the cold tire pressure threshold range, an alarm signal is triggered, and a warning is issued to the driver through the vehicle display unit.

[0012] In a preferred embodiment of the present invention, the acquisition of the calibration parameters includes the following steps: before the vehicle is used for the first time or after tire replacement, the initial cold tire pressure and initial cold tire temperature are recorded under cold conditions. During normal vehicle operation, real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, real-time ambient temperature data, and vehicle mileage are continuously recorded. Based on the recorded data, a recursive least squares algorithm or gradient descent algorithm is used to adaptively learn and update the heat generation function parameters, heat dissipation function parameters, and tire heat capacity parameters in the dynamic thermodynamic model, so as to minimize the error between the corrected real-time internal temperature and the real-time tire temperature data, thereby optimizing the accuracy of the calibration parameters. The calibration parameters are stored in the non-volatile memory of the tire pressure monitoring and control unit and are loaded when the system starts.

[0013] In a preferred embodiment of the present invention, the heat generation function is expressed as:

[0014] ,in Indicates real-time vehicle speed. and These are calibration parameters. It is a constant term; it represents the basic heat generation that is independent of vehicle speed.

[0015] The heat dissipation function is expressed as:

[0016] ,in This represents the real-time theoretical temperature inside the tire. This indicates the real-time ambient temperature. It is a calibration parameter that characterizes the heat dissipation coefficient of the tire.

[0017] The heat capacity parameter of the tire is expressed as follows:

[0018] ,in For the equivalent mass of the tire, The product is the tire's equivalent specific heat capacity, and it is also a calibration parameter obtained through adaptive learning.

[0019] The dynamic thermodynamic model obtains the real-time theoretical temperature by solving the following differential equation:

[0020]

[0021] The real-time theoretical temperature is obtained, and the differential equation is calculated by numerical integration using the Euler method or the Runge-Kutta method.

[0022] In a preferred embodiment of the present invention, the tire pressure monitoring control unit performs system initialization upon vehicle startup. The initialization steps include: reading stored calibration parameters; if the vehicle is stationary for more than a preset threshold (e.g., 4 hours), the currently measured tire temperature data is considered the initial cold tire temperature, and the currently measured tire pressure data is considered the initial cold tire pressure, while the internal state variables of the dynamic thermodynamic model are reset to the initial cold tire temperature. If the cold-state condition is not met, the estimated internal temperature value from the last parking time is loaded as the initial value.

[0023] This invention also provides a tire pressure monitoring dynamic calibration system that integrates vehicle speed data, the system comprising:

[0024] Multiple tire pressure sensor modules are installed inside each tire of the vehicle to periodically measure real-time tire pressure and temperature data, and transmit this data via a wireless communication module. Each tire pressure sensor module consists of a pressure sensor, a temperature sensor, a microcontroller, an RF transmitter, and a power supply. The pressure sensor is a MEMS pressure sensor with a measurement range of 0 kPa to 600 kPa and an accuracy of ±5 kPa. The temperature sensor is an NTC thermistor with a measurement range of -40℃ to 125℃ and an accuracy of ±2℃. The microcontroller is responsible for data acquisition, data encapsulation, and wireless communication protocol processing. The RF transmitter operates at a frequency of 433.92 MHz and has a transmission power of 0 dBm. The power supply uses a CR2032 coin cell lithium battery with a rated voltage of 3.0V.

[0025] A TPMS receiver, installed inside the vehicle chassis or body, receives wireless data signals from the tire pressure sensor module, demodulates and decodes the signals, and transmits the processed real-time tire pressure and temperature data to the tire pressure monitoring and control unit via the vehicle's internal data bus. The TPMS receiver consists of an RF receiving front-end, a demodulator, a decoder, and a CAN bus interface module. The RF receiving front-end has a receiving sensitivity of -100dBm. The decoder processes Manchester-encoded or NRZ-encoded TPMS data frames. The CAN bus interface module supports the CAN 2.0B protocol with a communication rate of 500kbps.

[0026] A vehicle speed sensor, mounted on a non-drive wheel or driveshaft of the vehicle, measures the vehicle's real-time speed and transmits this data to the tire pressure monitoring and control unit via the vehicle's internal data bus. The vehicle speed sensor is a Hall effect sensor, outputting pulse signals and a preset number of pulses per wheel rotation cycle. Its data sampling frequency is 100Hz, and its accuracy is 0.5 km / h.

[0027] An ambient temperature sensor, installed on the exterior of the vehicle (e.g., behind the front bumper), measures real-time ambient temperature data and transmits this data to the tire pressure monitoring control unit via the vehicle's internal data bus. The ambient temperature sensor is a PT1000 platinum resistance temperature sensor with a measurement range of -40℃ to 85℃ and an accuracy of ±1℃.

[0028] A tire pressure monitoring and control unit (TPMS) is connected to the TPMS receiver, the vehicle speed sensor, and the ambient temperature sensor via a vehicle data bus. It receives and processes real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data. The TPMS integrates a data acquisition module, a data processing module, a dynamic calibration module, a tire pressure status judgment module, and an alarm output module.

[0029] The data acquisition module is responsible for receiving real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data from the CAN bus, and performing data synchronization and preliminary verification. The data processing module filters, converts units, and formats the received raw data to ensure data quality and consistency. For example, a moving average filter is used to smooth the tire pressure and tire temperature data, and outlier detection is performed.

[0030] The dynamic calibration module is the core of this system. This module incorporates a dynamic thermodynamic model to estimate the real-time theoretical temperature inside the tire, with real-time vehicle speed data serving as a key input to the heat generation rate in the thermodynamic model. The dynamic calibration module further includes a Kalman filter algorithm unit to fuse the real-time tire temperature data measured by the tire pressure sensor module with the real-time theoretical temperature estimated by the dynamic thermodynamic model, generating a corrected real-time internal temperature. The dynamic calibration module also includes a cold tire pressure equivalent value calculation unit, used to calculate the cold tire pressure equivalent value based on the real-time tire pressure data, the corrected real-time internal temperature, and a preset cold tire reference temperature, according to the ideal gas law.

[0031] The tire pressure status determination module compares the equivalent value of the cold tire pressure with a preset cold tire pressure threshold range to determine whether the tire pressure is normal, too high, or too low. The preset cold tire pressure threshold range is set by the vehicle manufacturer and stored in the non-volatile memory of the tire pressure monitoring and control unit. When the alarm output module determines that the tire pressure is abnormal, it generates an alarm signal and sends the alarm signal to the vehicle display unit via the vehicle data bus to illuminate the tire pressure abnormality indicator light or display detailed warning information on the in-vehicle display screen.

[0032] In a preferred embodiment of the present invention, the dynamic calibration module of the tire pressure monitoring control unit further includes an adaptive learning unit. During normal vehicle operation, the adaptive learning unit continuously collects and analyzes the real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data. The adaptive learning unit employs a recursive least squares algorithm to optimize and adjust the heat generation function parameters, heat dissipation function parameters, and tire heat capacity parameters in the dynamic thermodynamic model online based on the collected data. The goal of the adaptive learning is to minimize the mean square error between the corrected real-time internal temperature and the real-time tire temperature data. The optimized calibration parameters are periodically stored in the non-volatile memory of the tire pressure monitoring control unit to ensure the long-term accuracy of the system calibration and its adaptability to factors such as tire aging and wear.

[0033] In a preferred embodiment of the present invention, the processor of the tire pressure monitoring control unit is an automotive-grade microcontroller, such as a microcontroller based on the ARM Cortex-M series processor architecture, with a main frequency of 100MHz, equipped with 256KB of flash memory for storing programs and calibration parameters, and 64KB of RAM for runtime data storage. The operating system is a real-time operating system, such as FreeRTOS, to ensure the real-time performance of critical tasks.

[0034] In a preferred embodiment of the present invention, the tire pressure monitoring control unit is equipped with a vehicle identification module. During system initialization, the identification module obtains the vehicle's unique identification code (VIN) via the CAN bus. The tire pressure monitoring control unit uses the VIN to retrieve, from a pre-stored database, the initial cold tire pressure reference value and threshold range matching the vehicle, as well as the initial calibration parameter set of the dynamic thermodynamic model, thereby achieving precise matching between the system and the specific vehicle model.

[0035] The beneficial effects of this invention are as follows:

[0036] The present invention provides a dynamic calibration method and system for tire pressure monitoring that integrates vehicle speed data. By using the real-time vehicle speed as a key input for the rate of heat generation inside the tire, the present invention constructs a dynamic thermodynamic model, which can more accurately estimate the actual temperature change inside the tire, rather than relying solely on the instantaneous temperature sensor measurement value, thus solving the dynamic influence of temperature on tire pressure measurement results.

[0037] By converting the instantaneously measured hot tire pressure into an equivalent value of the cold tire pressure that drivers are accustomed to referencing, drivers can accurately judge whether the tire pressure is within the safe range, avoiding misjudgments caused by the thermal expansion and contraction effect.

[0038] By employing the Kalman filter algorithm to fuse model-estimated temperature and sensor-measured temperature, the complementary advantages of the two sources of information are effectively utilized, reducing the uncertainty caused by single sensor measurement noise or model prediction bias.

[0039] By adaptively optimizing the calibration parameters in the dynamic thermodynamic model, the system can continuously correct and improve its own model based on actual driving data, thereby improving its adaptability to different driving conditions, different tire characteristics, and even tire aging and wear, and ensuring the accuracy of long-term operation.

[0040] This system provides reliable and easy-to-understand tire pressure information, enabling drivers to promptly detect and correct abnormal tire pressure. This effectively prevents safety hazards such as tire blowouts and reduced handling performance caused by improper tire pressure, while also optimizing tire rolling resistance, extending tire life, and reducing fuel consumption. Attached Figure Description

[0041] The invention will now be further described with reference to the accompanying drawings.

[0042] Figure 1 This is a flowchart illustrating a dynamic calibration method for tire pressure monitoring that integrates vehicle speed data, according to the present invention.

[0043] Figure 2 The present invention provides a structural framework diagram of a tire pressure monitoring dynamic calibration system that integrates vehicle speed data.

[0044] In the diagram: 1. Tire pressure sensor module; 2. TPMS receiver; 3. Vehicle speed sensor; 4. Ambient temperature sensor; 5. Tire pressure monitoring and control unit; 51. Data acquisition module; 52. Data processing module; 53. Dynamic calibration module; 54. Tire pressure status judgment module; 55. Alarm output module; 56. Adaptive learning unit; 57. Vehicle identification module. Detailed Implementation

[0045] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0046] like Figure 1 As shown in the embodiment of the present invention, a dynamic calibration method for tire pressure monitoring that integrates vehicle speed data is applied to the tire pressure monitoring control unit of a vehicle. This control unit communicates with multiple key sensors in real time, including at least one tire pressure sensor module installed inside the tire, a vehicle speed sensor, and an ambient temperature sensor. This method, through a series of rigorous steps, transforms the raw sensor data into an equivalent cold tire pressure value with practical guiding significance.

[0047] First, the system periodically acquires various real-time data sets, which form the basis for all subsequent processing. Specifically, tire pressure sensor modules installed inside each tire of the vehicle wirelessly transmit real-time tire pressure and temperature data to the tire pressure monitoring and control unit. This data includes the instantaneous pressure and temperature information of the tires under current operating conditions. Simultaneously, the vehicle speed sensor generates real-time vehicle speed data and transmits this data to the control unit via the vehicle data bus. The ambient temperature sensor measures the real-time ambient temperature outside the vehicle and also provides it to the control unit via the vehicle data bus. Accurate acquisition and synchronization of all this data are prerequisites for subsequent dynamic calibration.

[0048] Furthermore, after acquiring the aforementioned real-time data, one of the core aspects of the method of this invention is to estimate the real-time theoretical temperature inside the tire using a sophisticated dynamic thermodynamic model based on real-time vehicle speed data, real-time ambient temperature data, and pre-stored calibration parameters. This dynamic thermodynamic model profoundly reveals the intrinsic relationship between heat generation, heat dissipation, and temperature changes inside the tire. Specifically, the core of this model lies in considering the vehicle's real-time speed as a key driving factor for the rate of heat generation inside the tire. When the vehicle is traveling at high speed, tire deformation, friction with the road surface, and the cyclical forces acting on the internal structure all significantly increase heat generation. This model includes a heat generation function. It takes the real-time vehicle speed V as input and outputs an instantaneous heat generation power value. This power value quantifies the heat generated per unit time due to the aforementioned mechanical effects. As a specific embodiment, this heat generation function can be expressed as:

[0049] ,in Indicates real-time vehicle speed. and These are calibration parameters. It is a constant term; it represents the basic heat generation that is independent of vehicle speed, such as the self-heating of electronic components inside the tires or the small amount of heat accumulation when the vehicle is parked.

[0050] At the same time, this dynamic thermodynamic model also includes a heat dissipation function. This function measures the real-time theoretical temperature inside the tire. As input, an instantaneous heat dissipation power value is output. This power value characterizes the heat lost from the tire interior to the external environment per unit time through various heat exchange mechanisms such as convection, conduction, and radiation. In a specific embodiment, this heat dissipation function can be expressed as:

[0051]

[0052] These calibration parameters are obtained through adaptive learning, and their physical meaning is the tire's overall heat dissipation coefficient. This model precisely balances heat generation and dissipation, incorporating the tire's heat capacity parameters. The rate of change of the tire's internal temperature over time is calculated. Specifically, this process is achieved by solving a differential equation:

[0053]

[0054] in, Represents the equivalent mass of the tire. The product of these represents the equivalent specific heat capacity of the tire. The calibration parameters are also obtained through adaptive learning. To ensure the real-time performance and accuracy of the calculations, the differential equation is solved using efficient numerical integration methods, such as the Euler method or the more precise Runge-Kutta method.

[0055] Thirdly, to overcome the limitations of single-sensor measurement noise or model prediction bias, this invention intelligently fuses the real-time tire temperature data measured by the tire pressure sensor module with the real-time theoretical temperature estimated by the aforementioned dynamic thermodynamic model, thereby obtaining a corrected real-time internal temperature. This fusion process employs a highly robust Kalman filter algorithm. Specifically, the real-time tire temperature data is considered as the measurement input to the Kalman filter algorithm, containing noise information from the actual physical system. The real-time theoretical temperature estimated by the dynamic thermodynamic model serves as the state prediction input to the Kalman filter algorithm, reflecting the dynamic evolution trend of the system. By dynamically adjusting weights, the Kalman filter algorithm comprehensively considers the measurement noise characteristics of the real-time tire temperature data and the model prediction uncertainty of the real-time theoretical temperature, outputting a more accurate and smoother estimate of the tire's internal temperature—the corrected real-time internal temperature. This fusion mechanism maximizes the complementary advantages of sensor data and model prediction, significantly improving the accuracy and stability of temperature estimation.

[0056] The fourth step involves using the acquired real-time data and corrected temperature information to calculate a more practically meaningful equivalent cold tire pressure value for the driver. This calculation process is based on the classic ideal gas law and is specifically expressed as: Equivalent cold tire pressure value = Real-time tire pressure data × (Preset cold tire reference temperature / Corrected real-time internal temperature). Here, the real-time tire pressure data is the instantaneous pressure value measured by the tire pressure sensor module under the current operating conditions. The corrected real-time internal temperature is an accurate temperature estimate after Kalman filtering and fusion processing. The preset cold tire reference temperature is an industry-standardized temperature value, such as the common 20 degrees Celsius. By proportionally converting the real-time hot tire pressure to the pressure at the standard cold tire temperature, the system can provide the driver with a stable and comparable tire pressure reference value, effectively avoiding misjudgments caused by tire pressure increases due to tire temperature rise.

[0057] Finally, the system compares the calculated equivalent cold tire pressure with the preset cold tire pressure threshold range in real time to determine the current tire pressure status. The preset cold tire pressure threshold range is typically set by the vehicle manufacturer based on factors such as tire model and vehicle load, and is stored in the non-volatile memory of the tire pressure monitoring and control unit. If the equivalent cold tire pressure exceeds the preset normal range (e.g., too high or too low), the system will immediately trigger an alarm signal and issue a clear warning to the driver through the vehicle display unit, such as illuminating the abnormal tire pressure indicator light or displaying detailed warning information on the in-vehicle infotainment system screen, thereby promptly reminding the driver to take necessary measures to ensure driving safety.

[0058] As a preferred embodiment of the present invention, to ensure the long-term accuracy of the system calibration parameters and their adaptability to actual working conditions, the present invention introduces an adaptive learning mechanism for the calibration parameters. The acquisition of the calibration parameters includes the following stages: Before the vehicle's first use or after critical maintenance operations such as tire replacement, the system records the initial cold tire pressure and initial cold tire temperature of each tire when the vehicle is stationary and cold (e.g., parked for more than 4 hours). During normal vehicle operation, the system continuously and intensively records real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, real-time ambient temperature data, and vehicle mileage. Based on this massive amount of actual operating data, the system employs advanced machine learning algorithms, such as recursive least squares or gradient descent, to adaptively learn and update the heat generation function parameters, heat dissipation function parameters, and tire heat capacity parameters in the dynamic thermodynamic model online. The goal of this learning process is to minimize the mean square error between the corrected real-time internal temperature and the real-time tire temperature data, thereby continuously optimizing the model's prediction accuracy and making it more closely fit the actual physical process. These optimized calibration parameters are periodically stored in the non-volatile memory of the tire pressure monitoring control unit and are automatically loaded when the system starts up, ensuring that the system is always dynamically calibrated based on the latest and most accurate parameters.

[0059] As a specific implementation, the tire pressure monitoring control unit executes a rigorous system initialization procedure each time the vehicle is started. This procedure first reads all previously stored calibration parameters from non-volatile memory. Next, the system determines whether the vehicle meets cold-state conditions, for example, by monitoring whether the vehicle's stationary state has continuously exceeded a preset threshold (e.g., 4 hours) and whether the ambient temperature change rate is within an extremely low range. If it is determined to be cold-state, the system uses the currently measured tire temperature data as the initial cold tire temperature and the currently measured tire pressure data as the initial cold tire pressure. Simultaneously, it resets all state variables within the dynamic thermodynamic model (e.g., the theoretical tire temperature estimate at the time of the last stop) to this initial cold tire temperature, ensuring the model starts from a clear baseline. If the cold-state conditions are not met, for example, if the vehicle has just been briefly parked or the last time the engine was turned off was insufficient, the system loads the internal temperature estimate from the time of the last stop as the initial state of the dynamic thermodynamic model, ensuring the model can continue operating from a reasonable starting point even when starting from a non-cold state, avoiding unnecessary model convergence time.

[0060] like Figure 2 As shown, the present invention also provides a tire pressure monitoring dynamic calibration system that integrates vehicle speed data. This system consists of multiple modules that work together to form a complete closed-loop control system.

[0061] The system comprises multiple tire pressure sensor modules, each precisely installed inside each tire of the vehicle. Each tire pressure sensor module is a highly integrated microelectronic unit, consisting of a high-precision pressure sensor, a temperature sensor, a dedicated microcontroller, a radio frequency transmitter, and a miniature power supply.

[0062] Specifically, the pressure sensor employs advanced MEMS (Micro-Electro-Mechanical Systems) technology, covering a typical tire pressure range, such as 0 kPa to 600 kPa, with a measurement accuracy of ±5 kPa, sufficient to meet the stringent requirements of tire pressure monitoring. The temperature sensor uses an NTC thermistor, with a measurement range of -40℃ to 125℃ and an accuracy of ±2℃, accurately capturing subtle changes in the tire's internal temperature. The microcontroller is the brain of this module, responsible for real-time acquisition of pressure and temperature data, signal processing, data encapsulation, and transmission according to a specific wireless communication protocol. The RF transmitter operates in the internationally recognized 433.92MHz band with a transmission power of 0 dBm, ensuring stable and reliable wireless data transmission. The entire module is powered by a single coin cell lithium battery, such as the CR2032 model, with a rated voltage of 3.0V and a design life of several years.

[0063] A TPMS receiver is installed in a suitable location within the vehicle's chassis or body. This receiver is responsible for receiving wireless data signals from the various tire pressure sensor modules and performing demodulation and decoding operations. The TPMS receiver consists of a high-performance RF receiver front-end, a demodulator, a decoder, and a CAN bus interface module. The RF receiver front-end has a receiving sensitivity of -100dBm, sufficient to stably receive weak wireless signals in complex in-vehicle environments. The demodulator restores the RF signal to a baseband data stream. The decoder parses the data according to a specific TPMS data frame encoding format (such as Manchester encoding or NRZ encoding) to extract the raw real-time tire pressure and temperature data. The processed data is then transmitted to the tire pressure monitoring control unit via the CAN bus interface module at a communication rate of 500kbps, according to the CAN 2.0B protocol.

[0064] A vehicle speed sensor is mounted on the non-drive wheels or driveshaft of the vehicle to accurately measure real-time vehicle speed. This sensor typically employs the Hall effect principle, outputting a pulse signal, with a preset number of pulses per wheel rotation cycle. By calculating the number of pulses per unit time, the control unit can deduce the precise vehicle speed. Its data sampling frequency reaches 100Hz, ensuring real-time speed data and a measurement accuracy of up to 0.5 km / h, providing reliable input for dynamic thermodynamic models. The measured speed data is also transmitted to the tire pressure monitoring control unit via the vehicle's internal data bus.

[0065] An ambient temperature sensor is installed in a protected location on the exterior of the vehicle, such as behind the front bumper, to measure real-time ambient temperature data around the vehicle. This sensor is a PT1000 platinum resistance temperature sensor with a measurement range of -40°C to 85°C and an accuracy of ±1°C, accurately reflecting the external ambient temperature. Ambient temperature data is crucial for heat dissipation calculations in dynamic thermodynamic models, and it is also transmitted to the tire pressure monitoring and control unit via the vehicle data bus.

[0066] The core of this system is a tire pressure monitoring and control unit (TPMS). This unit is tightly connected to the TPMS receiver, vehicle speed sensor, and ambient temperature sensor via the vehicle data bus. It is responsible for receiving, processing, and integrating all sensor data, executing a dynamic calibration algorithm, and ultimately determining the tire pressure status. The TPMS control unit integrates multiple functional modules:

[0067] The data acquisition module is responsible for receiving all real-time data streams from the CAN bus, including real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data. This module also performs data synchronization operations to ensure that data from different sensors are aligned in time and performs preliminary data verification to filter out obvious communication errors or outliers.

[0068] The data processing module performs fine-tuning on the received raw data. This includes applying digital filters (such as moving average filters) to smooth tire pressure and temperature data to eliminate measurement noise; performing unit conversion to convert the raw sensor outputs into standard engineering units; and formatting the data to suit the requirements of subsequent algorithm modules. This module also performs outlier detection, such as identifying and flagging sensor readings that are outside the physically reasonable range.

[0069] The dynamic calibration module is the core intelligent component of the entire system. This module incorporates the dynamic thermodynamic model detailed earlier, using real-time vehicle speed data as a key input to the heat generation rate to estimate the real-time theoretical temperature inside the tire. This module further includes a Kalman filter algorithm unit, which fuses the real-time tire temperature data measured by the tire pressure sensor module with the real-time theoretical temperature estimated by the dynamic thermodynamic model, thereby generating a more accurate corrected real-time internal temperature.

[0070] In addition, the dynamic calibration module includes a cold tire pressure equivalent value calculation unit. Based on real-time tire pressure data, the corrected real-time internal temperature, and a preset cold tire reference temperature, it calculates the cold tire pressure equivalent value strictly according to the ideal gas law. The tire pressure status judgment module compares the cold tire pressure equivalent value output by the dynamic calibration module with the cold tire pressure threshold range preset by the vehicle manufacturer to accurately determine whether the tire pressure is normal, too high, or too low. These preset thresholds are securely stored in the non-volatile memory of the tire pressure monitoring and control unit.

[0071] When the alarm output module detects an abnormal tire pressure, it generates a standard alarm signal and sends the signal to the vehicle display unit via the vehicle data bus. This signal then illuminates the tire pressure abnormality indicator light on the vehicle's dashboard or displays a detailed warning message on the in-vehicle display screen, intuitively reminding the driver that there is a problem with the tire pressure.

[0072] In a preferred embodiment of the present invention, the dynamic calibration module of the tire pressure monitoring control unit further includes an adaptive learning unit. This unit continuously collects and analyzes a large amount of real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data, and real-time ambient temperature data during normal vehicle operation. The adaptive learning unit employs online optimization techniques such as recursive least squares algorithms to continuously optimize and adjust key calibration parameters in the dynamic thermodynamic model, such as heat generation function parameters, heat dissipation function parameters, and tire heat capacity parameters, based on these accumulated real-world data. The goal of adaptive learning is to minimize the mean square error between the corrected real-time internal temperature and the real-time tire temperature data, thereby continuously improving the model's predictive accuracy. These optimized and verified calibration parameters are periodically stored in the non-volatile memory of the tire pressure monitoring control unit, ensuring that the system can adapt to tire aging and wear, as well as changes in different driving conditions and environmental conditions, thereby maintaining long-term operational accuracy.

[0073] Furthermore, the tire pressure monitoring control unit employs a high-performance automotive-grade microcontroller, such as one based on the ARM Cortex-M series architecture. This processor boasts a clock speed of up to 100MHz, providing ample computing power to execute complex dynamic thermodynamic models and Kalman filtering algorithms. To store program code and all calibration parameters, the microcontroller is equipped with 256KB of flash memory. Simultaneously, 64KB of RAM is used to store various real-time data and intermediate calculation results during system operation. Regarding the operating system, to ensure real-time responsiveness for all critical tasks, the system runs on a real-time operating system (RTOS), such as FreeRTOS, guaranteeing the timeliness and reliability of tire pressure monitoring.

[0074] As a specific embodiment, the tire pressure monitoring control unit also includes a vehicle identification module. During system initialization, this module securely obtains the vehicle's unique identification number (VIN) via the CAN bus. The tire pressure monitoring control unit then uses this VIN to retrieve, from its internally stored database, an initial cold tire pressure reference value and threshold range precisely matching the vehicle model, as well as a set of preset dynamic thermodynamic model initial calibration parameters for that specific vehicle type. This mechanism ensures that the system of this invention can achieve precise matching with different vehicle models and initialize according to the vehicle's specific performance parameters and tire characteristics, thereby improving the system's universality and applicability.

[0075] To illustrate in detail the advantages of the tire pressure monitoring dynamic calibration method and system that integrates vehicle speed data, the following is a specific embodiment and a comparative example.

[0076] Example 1: Application of the Method of the Invention

[0077] Suppose a passenger car has a standard tire pressure of 2.4 Bar (240 kPa) on its front tires when cold (left to stand overnight at an ambient temperature of 20°C), and the tire temperature is also 20°C. After the vehicle is started, it is driven continuously at a constant speed of 100 km / h while maintaining an ambient temperature of 20°C.

[0078] The system of this invention operates as follows in this scenario: Initial state: cold tire pressure 240 kPa, cold tire temperature 20°C. Vehicle starts, ambient temperature sensor continuously outputs 20°C. Vehicle speed sensor continuously outputs 100 km / h. Tire pressure sensor module begins sending real-time tire pressure and tire temperature data.

[0079] After 20 minutes of driving: Real-time tire pressure sensor reading: 2.8 Bar (280 kPa). Real-time tire temperature sensor reading: 45°C.

[0080] Dynamic thermodynamic models estimate real-time theoretical temperatures:

[0081] Assume the adaptive learning model parameters of this invention are: k1=5e-6, alpha=2.2, k_0=0.5W, k2=0.1W / °. .

[0082] Heat generation function Calculate at V = 100 km / h (i.e., 27.78 m / s):

[0083] (This is a simplified example; the actual power will be significantly higher to reflect the temperature rise.)

[0084] During the journey, the model will... Continuous integration.

[0085] Assuming 20 minutes of integral calculation, the real-time theoretical temperature predicted by the dynamic thermodynamic model... The temperature is 47°C.

[0086] Kalman filter fusion:

[0087] Measurement value: Real-time tire temperature sensor measurement value .

[0088] Predicted values: Estimated by dynamic thermodynamic model .

[0089] Kalman filtering dynamically weights the sensor noise covariance and the model prediction error covariance.

[0090] Assuming the corrected real-time internal temperature of the Kalman filter output (An optimized value that lies between prediction and measurement).

[0091] Calculation of equivalent cold tire pressure:

[0092] Real-time tire pressure data .

[0093] Corrected real-time internal temperature .

[0094] Preset cold tire reference temperature .

[0095] Equivalent cold tire pressure:

[0096] .

[0097] The results showed that although the instantaneous tire pressure reached 2.8 Bar, after calibration using the method of this invention, the equivalent cold tire pressure was approximately 2.57 Bar. If the preset cold tire pressure threshold range is 2.3 Bar to 2.5 Bar, the system will determine that it is slightly high and issue a warning, instructing the driver to release air appropriately when the tire is cold. This contrasts sharply with the erroneous operation of simply considering 2.8 Bar as too high and immediately releasing air.

[0098] Comparative Example 1: Traditional TPMS based on fixed temperature compensation

[0099] Many existing TPMS systems attempt to perform some form of temperature compensation, but often lack the dynamic thermodynamic model and adaptive learning capabilities proposed in this invention. They may compensate for temperature based on a fixed or simple empirical formula, or simply use instantaneous tire temperature measured by sensors for linear compensation.

[0100] Using the same scenario as the previous embodiment: Initial state: cold tire pressure 240 kPa, cold tire temperature 20°C. After driving for 20 minutes: Real-time tire pressure sensor measurement: 2.8 Bar (280 kPa). Real-time tire temperature sensor measurement: 45°C.

[0101] Traditional fixed temperature compensation (based on instantaneous tire temperature):

[0102] These types of systems typically use real-time tire temperature sensor measurements directly. Compensation will be provided.

[0103] The calculation method is similar to that of this invention: .

[0104] Although at this particular single point in time, the result appears to be close to the 2.57 Bar of the present invention, this conventional method has significant drawbacks:

[0105] Measurement noise sensitivity: If the tire temperature sensor itself has instantaneous measurement error or drift, the error will be directly reflected in the compensation result, leading to instability.

[0106] Hysteresis: The response speed and location of the tire temperature sensor may not be able to fully capture the true temperature of all areas inside the tire, especially under complex conditions (such as rapid acceleration, rapid deceleration, and different road surfaces). The local measurement value of the sensor may deviate from the overall temperature distribution.

[0107] Lack of predictability: Traditional methods cannot predict temperature change trends, nor can they utilize dynamic information such as vehicle speed for deeper physical modeling. When operating conditions such as vehicle speed and load change rapidly, the compensation accuracy drops rapidly, potentially causing drastic fluctuations in the equivalent value of cold tire pressure, which can confuse or mislead drivers.

[0108] To further quantify the superiority of the method of this invention, we demonstrate it by comparing multiple sets of data. The table below simulates the performance of the method of this invention and the traditional fixed temperature compensation method in estimating the equivalent value of cold tire pressure at different driving stages of the vehicle, and assumes that the actual cold tire pressure should always fluctuate within the range of 2.3-2.5 Bar.

[0109] Driving phase Real-time vehicle speed (km / h) Real-time ambient temperature (°C) Real-time tire pressure (Bar) Real-time tire temperature (°C) The cold tire pressure equivalent value (Bar) of this invention Traditional compensated cold tire pressure equivalent value (Bar) Actual cold tire pressure reference (Bar) cold initial state 0 20 2.40 20 2.40 2.40 2.40 Initial driving period 60 20 2.55 35 2.45 2.46 2.42 High-speed cruise 120 20 2.85 50 2.48 2.49 2.45 Slow down 80 20 2.70 45 2.45 2.47 2.43 Congested and slow 30 25 2.50 40 2.37 2.39 2.35 brief stop 0 25 2.48 38 2.38 2.39 2.36 Restart 70 25 2.65 42 2.45 2.46 2.43

[0110] As can be seen from the data in the table above, both methods accurately reflect cold tire pressure at the "initial cold state" point. However, in the subsequent dynamic driving phase, the method of this invention, by fusing vehicle speed data, a dynamic thermodynamic model, and Kalman filtering, calculates an equivalent cold tire pressure value that is closer to the "actual cold tire pressure benchmark," with less fluctuation and a more stable reflection of the true cold tire pressure level. For example, during the "high-speed cruising" phase, despite a significant increase in real-time tire pressure and temperature, the method of this invention can still stabilize the equivalent cold tire pressure value at 2.48 Bar, accurately indicating that the tire pressure is at a normal to slightly high level. While the "traditional compensated cold tire pressure equivalent value" is numerically close, it lacks in-depth modeling and adaptive capabilities for complex thermodynamic processes. Under rapidly changing conditions, it may exhibit greater lag or fluctuation, or lack robustness in the face of accidental sensor errors, thus introducing uncertainty into the driver's actual judgment. For example, if a sensor experiences a small instantaneous error at a certain moment, the Kalman filtering fusion mechanism of this invention can effectively suppress the influence of this noise, while the traditional method may directly amplify the error. This invention continuously optimizes model parameters through adaptive learning, enabling the "equivalent value of cold tire pressure of this invention" to maintain consistency with the actual characteristics of vehicles and tires over a long period of time, rather than relying solely on static or empirical parameters, thereby significantly improving the long-term reliability and accuracy of the system.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method of dynamic calibration of tire pressure monitoring with fusion of vehicle speed data, applied to a tire pressure monitoring control unit of a vehicle, said tire pressure monitoring control unit (5) being in data communication with at least one tire pressure sensor module (1), a vehicle speed sensor (3) and an ambient temperature sensor (4), characterized in that, The method comprises the following steps: S1: acquiring real-time tire pressure data, real-time tire temperature data, real-time vehicle speed data and real-time ambient temperature data; the real-time tire pressure data and the real-time tire temperature data are periodically measured by the tire pressure sensor module (1) installed in the tire of the vehicle and wirelessly transmitted to the tire pressure monitoring control unit (5); the real-time vehicle speed data is generated by the vehicle speed sensor (3) and transmitted to the tire pressure monitoring control unit (5) through the vehicle data bus; the real-time ambient temperature data is generated by the ambient temperature sensor (4) and transmitted to the tire pressure monitoring control unit (5) through the vehicle data bus; S2: based on the real-time vehicle speed data, the real-time ambient temperature data and the historically stored calibration parameters, the real-time theoretical temperature inside the tire is estimated by using a dynamic thermodynamic model, the dynamic thermodynamic model establishes the relationship between heat generation, heat dissipation and temperature change inside the tire, the dynamic thermodynamic model includes a heat generation function and a heat dissipation function; the heat generation function takes the real-time vehicle speed as input and outputs an instantaneous heat generation power value; the heat dissipation function takes the real-time theoretical temperature inside the tire and the real-time ambient temperature as input and outputs an instantaneous heat dissipation power value; S3: fusing the real-time tire temperature data measured by the tire pressure sensor module (1) and the real-time theoretical temperature estimated by the dynamic thermodynamic model to obtain a corrected real-time internal temperature; S4: according to the real-time tire pressure data, the corrected real-time internal temperature and a preset cold tire reference temperature, a cold tire pressure equivalent value is calculated according to the ideal gas law; S5: comparing the cold tire pressure equivalent value with a preset cold tire pressure threshold value to determine the tire pressure state; if the cold tire pressure equivalent value exceeds the cold tire pressure threshold value range, an alarm signal is triggered and a warning is sent to the driver through the vehicle display unit.

2. The dynamic calibration method of tire pressure monitoring fusing vehicle speed data according to claim 1, wherein, In S3, the fusion process adopts a Kalman filtering algorithm, the real-time tire temperature data is taken as the measurement value input of the Kalman filtering algorithm, and the real-time theoretical temperature is taken as the state prediction value input of the Kalman filtering algorithm; the Kalman filtering algorithm dynamically adjusts the weight, comprehensively considers the measurement noise characteristics of the real-time tire temperature data and the model prediction uncertainty of the real-time theoretical temperature, and outputs the corrected real-time internal temperature.

3. The dynamic calibration method of tire pressure monitoring fusing vehicle speed data according to claim 1, wherein, In S2, the dynamic thermodynamic model calculates the real-time theoretical temperature inside the tire by integrating the difference between the instantaneous heat generation power and the instantaneous heat dissipation power, and combining the heat capacity parameter of the tire; the heat capacity parameter of the tire is represented as: ； wherein is the equivalent mass of the tire, is the equivalent specific heat capacity of the tire.

4. The dynamic calibration method of tire pressure monitoring fusing vehicle speed data according to claim 3, wherein, The heat generation function is expressed as: ； wherein represents the real-time vehicle speed, and is a calibration parameter, is a constant term; The heat dissipation function is expressed as: wherein represents the real-time theoretical temperature inside the tyre, represents the real-time ambient temperature, is a calibration parameter; The dynamic thermodynamic model solves the differential equation: ； The real-time theoretical temperature is obtained, and the differential equation is numerically integrated by using Euler method or Runge-Kutta method.

5. The dynamic calibration method of tire pressure monitoring fusing vehicle speed data according to claim 4, wherein, The acquisition of the calibration parameters includes the following steps: recording the initial cold tire pressure value and the initial cold tire temperature value of the tire under cold conditions before the vehicle is first used or after tire replacement; continuously recording the real-time tire pressure data, the real-time tire temperature data, the real-time vehicle speed data, the real-time ambient temperature data and the vehicle driving mileage during normal driving of the vehicle; according to the recorded data, using a recursive least squares algorithm or a gradient descent algorithm to adaptively learn and update the heat generation function parameters, the heat dissipation function parameters and the tire heat capacity parameters in the dynamic thermodynamic model, so as to minimize the error between the corrected real-time internal temperature and the real-time tire temperature data, thereby optimizing the accuracy of the calibration parameters; the calibration parameters are stored in the non-volatile memory of the tire pressure monitoring control unit (5) and are loaded when the system starts.

6. The dynamic calibration method of tire pressure monitoring fusing vehicle speed data according to claim 5, wherein, The tire pressure monitoring control unit (5) performs system initialization when the vehicle starts, and the initialization steps include: reading the stored calibration parameters; if the vehicle is in a stationary state and the duration exceeds a preset threshold, the currently measured tire temperature data is regarded as the initial cold tire temperature, and the currently measured tire pressure data is regarded as the initial cold tire pressure, and the internal state variable of the dynamic thermodynamic model is reset to the initial cold tire temperature; if the cold condition is not met, the internal temperature estimate value at the last stop is loaded as the initial value of the dynamic thermodynamic model.

7. A tire pressure monitoring dynamic calibration system fusing vehicle speed data, adapted to the tire pressure monitoring dynamic calibration method fusing vehicle speed data according to any one of claims 1-6; characterized in that, Comprise: A plurality of tire pressure sensor modules (1) are respectively installed inside each tire of the vehicle for periodically measuring real-time tire pressure data and real-time tire temperature data inside the tire, and transmitting the data through a wireless communication module; A TPMS receiver (2) is installed inside the vehicle for receiving wireless data signals from the tire pressure sensor module (1), and demodulating and decoding the signals, and transmitting the processed real-time tire pressure data and real-time tire temperature data through the vehicle internal data bus; A vehicle speed sensor (3) is installed on the vehicle for measuring real-time vehicle speed data and transmitting the data through the vehicle internal data bus; An ambient temperature sensor (4) is installed outside the vehicle for measuring real-time ambient temperature data and transmitting the data through the vehicle internal data bus; A tire pressure monitoring control unit (5) is connected with the TPMS receiver (2), the vehicle speed sensor (3) and the ambient temperature sensor (4) through the vehicle data bus for receiving and processing the real-time tire pressure data, the real-time tire temperature data, the real-time vehicle speed data and the real-time ambient temperature data.

8. The dynamic calibration system for tire pressure monitoring fusing vehicle speed data according to claim 7, wherein, The tire pressure monitoring control unit (5) comprises a data acquisition module (51), a data processing module (52), a dynamic calibration module (53), a tire pressure state judgment module (54) and an alarm output module (55); The data acquisition module (51) is used to receive the real-time tire pressure data, the real-time tire temperature data, the real-time vehicle speed data and the real-time ambient temperature data from the vehicle data bus, and perform data synchronization and preliminary verification; The data processing module (52) is used for filtering, unit conversion and data formatting of the received raw data to ensure data quality and consistency; The dynamic calibration module (53) internally contains the dynamic thermodynamic model, which is used to estimate the real-time theoretical temperature inside the tire, wherein the real-time vehicle speed data is used as a key input of the heat generation rate in the thermodynamic model; the dynamic calibration module (53) further comprises a Kalman filtering algorithm unit for fusing the real-time tire temperature data measured by the tire pressure sensor module (1) and the real-time theoretical temperature estimated by the dynamic thermodynamic model to generate a corrected real-time internal temperature; the dynamic calibration module (53) further comprises a cold tire pressure equivalent value calculation unit for calculating the cold tire pressure equivalent value according to the real-time tire pressure data, the corrected real-time internal temperature and a preset cold tire reference temperature according to the ideal gas law; The tire pressure state judgment module (54) is used for comparing the cold tire pressure equivalent value with a preset cold tire pressure threshold range to determine whether the tire pressure is in a normal, too high or too low state; The alarm output module (55) is used for generating an alarm signal when the tire pressure state is abnormal, and sending the alarm signal to the vehicle display unit through the vehicle data bus to light up the tire pressure abnormality indicator light or display detailed warning information on the vehicle display screen.

9. The dynamic calibration system for tire pressure monitoring fusing vehicle speed data according to claim 7, wherein, The dynamic calibration module (53) of the tire pressure monitoring control unit (5) further comprises an adaptive learning unit (56); the adaptive learning unit (56) continuously collects and analyzes the real-time tire pressure data, the real-time tire temperature data, the real-time vehicle speed data and the real-time environmental temperature data during normal driving of the vehicle; the adaptive learning unit (56) uses the recursive least squares algorithm or the gradient descent algorithm to optimize and adjust the heat generation function parameters, heat dissipation function parameters and tire heat capacity parameters in the dynamic thermodynamic model based on the collected data to minimize the mean square error between the corrected real-time internal temperature and the real-time tire temperature data; the calibration parameters are periodically stored in the non-volatile memory of the tire pressure monitoring control unit (5) to ensure long-term accuracy and adaptability of the system calibration to factors such as tire aging and wear.

10. The tire pressure monitoring dynamic calibration system of claim 7, wherein: The tire pressure sensor module (1) is composed of a MEMS pressure sensor, an NTC thermistor temperature sensor, a microcontroller, a radio frequency transmitter and a power supply, the measurement range of the MEMS pressure sensor is 0 kPa to 600 kPa, the accuracy is ± 5 kPa, the measurement range of the NTC thermistor temperature sensor is -40℃ to 125℃, the accuracy is ± 2℃; The TPMS receiver (2) is composed of a radio frequency receiving front end, a demodulator, a decoder and a CAN bus interface module, the radio frequency receiving front end has a receiving sensitivity of -100 dBm, and the CAN bus interface module supports CAN2.0B protocol; The vehicle speed sensor (3) is a Hall effect sensor; The ambient temperature sensor (4) is a PT1000 type platinum resistance temperature sensor, the measurement range is -40℃ to 85℃, and the precision is ±1℃; The processor of the tire pressure monitoring control unit (5) is an automobile level microcontroller.

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