Indirect tire pressure monitoring method and device, vehicle and storage medium

Abstract

The invention relates to the technical field of tire pressure monitoring, in particular to an indirect tire pressure monitoring method and device, a vehicle and a storage medium. The method includes: judging whether a vehicle is in a no-load state based on current mass of the vehicle; if the vehicle is in a no-load state, entering a no-load supervision mode based on a preset no-load correction coefficient and a preset standard frequency, and calculating a standard pulse number of each tire in the mode according to the preset no-load correction coefficient, a gear ratio of the wheel speed sensors of the first front and rear wheel sets and an actual pulse number of each tire; calculating the relative rolling radius characteristic of each tire according to the standard pulse number of each tire; and the relative rolling radius characteristic of each tire is compared with a first preset threshold value, and under the condition that the relative rolling radius characteristic of any tire is larger than the first preset threshold value, it is judged that the corresponding tire of the vehicle is in an underpressure state. Therefore, the problems of missing report and false report in the existing monitoring technology are solved, and the tire pressure monitoring accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of tire pressure monitoring, and particularly relates to an indirect tire pressure monitoring method, device, vehicle, and storage medium. Background Art

[0002] Statistics show that the proportion of flat tire causes in traffic accidents is as high as 60%. If the vehicle speed exceeds 160 km / h, the survival probability of a front tire blowout is almost zero; approximately 75% of flat tire accidents are caused by insufficient tire pressure. When the tire pressure is insufficient, the deformation amplitude of the tire increases, and the side of the tire bends due to excessive extrusion, resulting in an increase in tire temperature and causing a flat tire. In recent years, with the release of the latest mandatory regulations on tire pressure monitoring in China, tire pressure monitoring systems have received unprecedented attention.

[0003] In related technologies, tire pressure monitoring is mainly divided into two types: direct tire pressure monitoring and indirect tire pressure monitoring. Direct tire pressure monitoring installs a pressure sensor inside or outside the tire to monitor the actual pressure of the tire in real time and transmits the data to the vehicle control unit. Indirect tire pressure monitoring monitors the change in the rotational speed of the tire through the wheel speed sensor of the vehicle and uses the relationship between tire pressure and rotational speed to infer whether the tire pressure is abnormal.

[0004] However, direct tire pressure monitoring may cause data loss or inaccuracy due to sensor failure, battery life problems, or signal interference, resulting in missed reports. In addition, indirect tire pressure monitoring may misjudge insufficient tire pressure due to abnormal changes in tire rotational speed under certain complex road conditions (such as bumpy roads or slippery roads), leading to false reports, which urgently need to be solved. Summary of the Invention

[0005] The present application provides an indirect tire pressure monitoring method, device, vehicle, and storage medium to solve the problems of missed reports and false reports in existing monitoring technologies and improve the accuracy of tire pressure monitoring.

[0006] To achieve the above object, the first aspect embodiment of the present application proposes an indirect tire pressure monitoring method, including the following steps:

[0007] Obtain the current mass of the vehicle and determine whether the vehicle is in an unloaded state based on the current mass;

[0008] If the vehicle is in the unloaded state, enter the unloaded supervision mode based on a preset unloaded correction coefficient and a preset standard frequency, and obtain the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets of the vehicle and the actual pulse number of each tire in the unloaded supervision mode;

[0009] Calculate the standard pulse number of each tire in the no-load supervision mode according to the preset no-load correction coefficient, the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets, and the actual pulse number of each tire in the no-load supervision mode, and calculate the relative rolling radius characteristic of each tire in the no-load supervision mode according to the standard pulse number of each tire in the no-load supervision mode;

[0010] Compare the relative rolling radius characteristics of each tire in the no-load supervision mode with a first preset threshold respectively. When the relative rolling radius characteristic of any tire in the no-load supervision mode is greater than the first preset threshold, it is determined that the corresponding tire of the vehicle is in an underpressure state.

[0011] According to an embodiment of the present application, after entering the no-load supervision mode based on the preset no-load correction coefficient and the preset standard frequency, it further includes:

[0012] Obtain the wheel speed signal of the target tire in the no-load supervision mode, and process the wheel speed signal of the target tire in the no-load supervision mode to obtain a processed no-load wheel speed signal;

[0013] Based on the processed no-load wheel speed signal, use a second-order autoregressive model to identify the natural frequency characteristic of the target tire in the no-load supervision mode;

[0014] Perform a difference calculation between the preset standard frequency and the natural frequency characteristic of the target tire in the no-load supervision mode, and compare the first difference result with a second preset threshold. When the first difference result is greater than the second preset threshold, it is determined that each tire of the vehicle is in the underpressure state.

[0015] According to an embodiment of the present application, after determining whether the vehicle is in the no-load state based on the current mass, it further includes:

[0016] If the vehicle is not in the no-load state, enter the load self-learning mode;

[0017] Based on the load self-learning mode, determine the load correction coefficient of each tire and the load standard frequency of each tire.

[0018] According to an embodiment of the present application, the determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode includes:

[0019] Obtain the actual pulse number of each tire in the load self-learning mode and the tooth number ratio of the wheel speed sensors of the second front and rear wheel sets;

[0020] Calculate the average pulse number based on the actual pulse number of each tire in the load self-learning mode and the tooth number ratio of the wheel speed sensors of the second front and rear wheel sets;

[0021] Determine the load correction coefficient of each tire based on the actual pulse number of each tire in the load self-learning mode and the average pulse number.

[0022] According to an embodiment of the present application, the determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode further includes:

[0023] Resample and filter the wheel speed signal of each tire in the load self-learning mode to obtain a processed load wheel speed signal;

[0024] Based on the processed load wheel speed signal, estimate the current natural frequency characteristics of each tire in the load self-learning mode by using a second-order autoregressive model, and use the current natural frequency characteristics of each tire in the load self-learning mode as the load standard frequency of each tire.

[0025] According to an embodiment of the present application, after determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode, it further includes:

[0026] Enter the load supervision mode based on the load correction coefficient of each tire and the load standard frequency of each tire;

[0027] Determine the relative rolling radius characteristics of each tire in the load supervision mode and the natural frequency characteristics of the target tire in the load supervision mode;

[0028] Compare the relative rolling radius characteristics of each tire in the load supervision mode with a third preset threshold respectively. In the case where the relative rolling radius characteristic of any tire in the load supervision mode is greater than the third preset threshold, it is determined that the corresponding tire of the vehicle is in the underpressure state;

[0029] Or, calculate the difference between the load standard frequency and the natural frequency characteristics of the target tire in the load supervision mode, and compare the second difference result with a fourth preset threshold. In the case where the second difference result is greater than the fourth preset threshold, it is determined that all the target tires of the vehicle are in the underpressure state.

[0030] According to an embodiment of the present application, the obtaining the current mass of the vehicle includes:

[0031] Obtain the real-time speed, real-time motor torque, real-time transmission ratio, and tire radius of the vehicle when it is in the acceleration stage;

[0032] Obtain the real-time acceleration of the vehicle based on the real-time speed, and estimate the longitudinal driving force of the vehicle based on the real-time motor torque, the real-time transmission ratio, and the tire radius;

[0033] Use the Kalman filter algorithm to calculate the current mass of the vehicle based on the real-time acceleration and the longitudinal driving force.

[0034] According to the indirect tire pressure monitoring method proposed in the embodiments of the present application, by introducing a real-time estimation and self-learning mechanism of the vehicle load state, it is possible to dynamically distinguish between the unloaded and loaded states of the vehicle, and select a suitable tire pressure monitoring mode according to the current load condition, solve the problems of missed reports and false reports existing in the existing monitoring technology, and improve the accuracy of tire pressure monitoring.

[0035] To achieve the above object, an embodiment of the second aspect of the present application proposes an indirect tire pressure monitoring device, including:

[0036] A first acquisition module, configured to acquire the current mass of the vehicle, and determine whether the vehicle is in an unloaded state based on the current mass;

[0037] A second acquisition module, configured to enter an unloaded supervision mode based on a preset unloaded correction coefficient and a preset standard frequency when the vehicle is in the unloaded state, and acquire the wheel speed sensor tooth number ratio of the first front and rear wheel sets of the vehicle and the actual pulse number of each tire in the unloaded supervision mode;

[0038] A calculation module, configured to calculate the standard pulse number of each tire in the unloaded supervision mode according to the preset unloaded correction coefficient, the wheel speed sensor tooth number ratio of the first front and rear wheel sets, and the actual pulse number of each tire in the unloaded supervision mode, and calculate the relative rolling radius feature of each tire in the unloaded supervision mode according to the standard pulse number of each tire in the unloaded supervision mode;

[0039] A determination module, configured to compare the relative rolling radius feature of each tire in the unloaded supervision mode with a first preset threshold respectively, and determine that the corresponding tire of the vehicle is in an underpressure state when the relative rolling radius feature of any tire in the unloaded supervision mode is greater than the first preset threshold.

[0040] According to an embodiment of the present application, after entering the unloaded supervision mode based on the preset unloaded correction coefficient and the preset standard frequency, the second acquisition module further includes:

[0041] Obtain the wheel speed signal of the target tire in the no-load supervision mode, and process the wheel speed signal of the target tire in the no-load supervision mode to obtain the processed no-load wheel speed signal;

[0042] Based on the processed no-load wheel speed signal, use a second-order autoregressive model to identify the inherent frequency characteristics of the target tire in the no-load supervision mode;

[0043] Perform a difference calculation between the preset standard frequency and the inherent frequency characteristics of the target tire in the no-load supervision mode, and compare the first difference result with a second preset threshold. When the first difference result is greater than the second preset threshold, determine that each tire of the vehicle is in the under-pressure state.

[0044] According to an embodiment of the present application, after determining whether the vehicle is in the no-load state based on the current mass, the first acquisition module further includes:

[0045] A control unit, configured to enter a load self-learning mode when the vehicle is not in the no-load state;

[0046] A determination unit, configured to determine the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode.

[0047] According to an embodiment of the present application, the determination unit is specifically configured to:

[0048] Obtain the actual pulse number of each tire in the load self-learning mode and the wheel speed sensor tooth number ratio of the second front and rear wheel groups;

[0049] Calculate the average pulse number based on the actual pulse number of each tire in the load self-learning mode and the wheel speed sensor tooth number ratio of the second front and rear wheel groups;

[0050] Determine the load correction coefficient of each tire based on the actual pulse number of each tire in the load self-learning mode and the average pulse number.

[0051] According to an embodiment of the present application, the determination unit is further configured to:

[0052] Perform resampling and filtering processing on the wheel speed signal of each tire in the load self-learning mode to obtain the processed load wheel speed signal;

[0053] Based on the processed load wheel speed signal, use a second-order autoregressive model to estimate the current inherent frequency characteristics of each tire in the load self-learning mode, and use the current inherent frequency characteristics of each tire in the load self-learning mode as the load standard frequency of each tire.

[0054] According to an embodiment of the present application, after determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode, the determining unit is further configured to:

[0055] Enter the load supervision mode based on the load correction coefficient of each tire and the load standard frequency of each tire;

[0056] Determine the relative rolling radius characteristics of each tire in the load supervision mode and the natural frequency characteristics of the target tire in the load supervision mode;

[0057] Compare the relative rolling radius characteristics of each tire in the load supervision mode with a third preset threshold respectively. When the relative rolling radius characteristic of any tire in the load supervision mode is greater than the third preset threshold, it is determined that the corresponding tire of the vehicle is in the underpressure state;

[0058] Or, calculate the difference between the load standard frequency and the natural frequency characteristics of the target tire in the load supervision mode, and compare the second difference result with a fourth preset threshold. When the second difference result is greater than the fourth preset threshold, it is determined that all the target tires of the vehicle are in the underpressure state.

[0059] According to an embodiment of the present application, the first acquisition module is specifically configured to:

[0060] Acquire the real-time speed, real-time motor torque, real-time transmission ratio, and tire radius when the vehicle is in the acceleration stage;

[0061] Obtain the real-time acceleration of the vehicle based on the real-time speed, and estimate the longitudinal driving force of the vehicle based on the real-time motor torque, the real-time transmission ratio, and the tire radius;

[0062] Use the Kalman filter algorithm to calculate the current mass of the vehicle based on the real-time acceleration and the longitudinal driving force.

[0063] The indirect tire pressure monitoring device proposed according to the embodiment of the present application can dynamically distinguish the unloaded and loaded states of the vehicle by introducing the real-time estimation and self-learning mechanism of the vehicle load state, and select a suitable tire pressure monitoring mode according to the current load condition, solving the problems of missed reports and false reports existing in the existing monitoring technology and improving the accuracy of tire pressure monitoring.

[0064] To achieve the above object, an embodiment of the third aspect of the present application proposes a vehicle, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the indirect tire pressure monitoring method as described in the above embodiment.

[0065] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the indirect tire pressure monitoring method as described in the above embodiments.

[0066] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it is used to implement the indirect tire pressure monitoring method as described in the above embodiments.

[0067] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0068] The above and / or additional aspects and advantages of the present application will become apparent and be easily understood from the following description of the embodiments in conjunction with the drawings, where:

[0069] Figure 1 is a flowchart of an indirect tire pressure monitoring method provided according to an embodiment of the present application;

[0070] Figure 2 is a flowchart of frequency identification based on the AR (Autoregressive Model of Order 2) model method according to an embodiment of the present application;

[0071] Figure 3 is a schematic diagram of the frequency identification result based on the AR model according to an embodiment of the present application (the full pressure of a certain tire is 42 Hz);

[0072] Figure 4 is a schematic diagram of the frequency identification result based on the AR model according to another embodiment of the present application (the underpressure of a certain same tire is 40 Hz);

[0073] Figure 5 is a flowchart of another indirect tire pressure monitoring method provided according to an embodiment of the present application;

[0074] Figure 6 is a block schematic diagram of an indirect tire pressure monitoring device provided according to an embodiment of the present application;

[0075] Figure 7 is a schematic structural diagram of a vehicle provided according to an embodiment of the present application. Detailed Embodiments

[0076] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0077] An indirect tire pressure monitoring method, device, vehicle, and storage medium according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0078] Figure 1 It is a flowchart of an indirect tire pressure monitoring method according to an embodiment of the present application.

[0079] As Figure 1 shown, the indirect tire pressure monitoring method includes the following steps:

[0080] In step S101, obtain the current mass of the vehicle and determine whether the vehicle is in an unloaded state based on the current mass.

[0081] It can be understood that in order to obtain a more accurate monitoring result of the underinflation of the vehicle tires, in the embodiments of the present application, the current mass of the vehicle can be estimated first, and then the current load state of the vehicle, that is, whether the vehicle is in an unloaded state or a loaded state, can be determined according to the current mass, so as to provide a basis for selecting a suitable indirect tire pressure monitoring working mode subsequently and ensure the accuracy and reliability of the monitoring result.

[0082] It should be noted that regardless of the selected working mode, in the embodiments of the present application, the relative rolling radius method and the natural frequency method can be used to determine the underinflation condition of the vehicle tires.

[0083] The relative rolling radius method essentially belongs to a wheel speed pulse comparison method. When the tire is in an underinflated state, its rolling radius decreases, and the number of wheel speed pulses generated by the underinflated tire is more than that of other normal tires. Therefore, by comparing the difference in the number of wheel pulses of each tire, it can be determined whether the tire is in an underinflated condition. The natural frequency method is based on the difference in the dynamic characteristics of the tire under different tire pressure states. When the tire is in an underinflated state, its stiffness and damping change, and this change causes the natural frequency of the tire to decrease. Therefore, by identifying the natural frequency of the tire and comparing it with the natural frequency under normal tire pressure, it can be determined whether the tire is in an underinflated condition.

[0084] How to obtain the current mass of the vehicle will be described in detail below.

[0085] As a possible implementation manner, in some embodiments, obtaining the current mass of the vehicle includes: obtaining the real-time speed, real-time motor torque, real-time transmission ratio, and tire radius of the vehicle when it is in the acceleration stage; obtaining the real-time acceleration of the vehicle based on the real-time speed, and estimating the longitudinal driving force of the vehicle based on the real-time motor torque, real-time transmission ratio, and tire radius; using the Kalman filtering algorithm, calculating the current mass of the vehicle based on the real-time acceleration and the longitudinal driving force.

[0086] Specifically, during the vehicle acceleration stage, the system estimates the longitudinal driving force received by the vehicle during driving by obtaining parameters such as the torque signal of the engine or motor (i.e., real-time motor torque), real-time transmission ratio, and tire radius. At the same time, the system obtains the vehicle speed signal (i.e., real-time speed) and performs a difference processing on the vehicle speed signal to obtain the real-time acceleration of the vehicle; alternatively, the real-time acceleration of the vehicle can also be directly obtained using an acceleration sensor. After obtaining the longitudinal driving force and real-time acceleration, the embodiments of the present application can use Newton's second law and the vehicle longitudinal dynamics balance equation as the theoretical basis, and use the Kalman filtering algorithm to estimate the current mass of the vehicle through steps such as parameter initialization, mass prediction, offset force prediction, covariance prediction, Kalman gain calculation, driving force prediction, state update, and covariance update.

[0087] The Kalman filtering algorithm is an efficient recursive filter, which continuously corrects and optimizes the estimated value of the vehicle mass through a series of iterative calculation processes. In each iteration, the algorithm calculates a more accurate estimated value based on the new measurement data and the previous estimated value. This process continues until the estimated value converges to a stable state. Finally, the vehicle mass output by the system is an accurate value after multiple iterative corrections, and this value can be very close to the actual total mass of the vehicle.

[0088] In step S102, if the vehicle is in the no-load state, enter the no-load supervision mode based on a preset no-load correction coefficient and a preset standard frequency, and obtain the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets of the vehicle and the actual pulse number of each tire in the no-load supervision mode.

[0089] Specifically, in the embodiments of the present application, a four-wheel vehicle is taken as an example. If it is determined that the vehicle is in an unloaded state according to the current mass of the vehicle, then the historical data stored in the EEPROM (Electrically Erasable Programmable Read-Only Memory), including the preset unloaded correction coefficient and the preset standard frequency (the preset unloaded correction coefficients corresponding to each tire may be the same or different, and the same applies to the preset standard frequencies corresponding to each tire), can be directly read, and the preset unloaded correction coefficient and the preset standard frequency are used to enter the unloaded supervision mode of tire pressure monitoring. In the unloaded supervision mode, the system can collect the actual pulse number of each tire within a fixed time period and the tooth number ratio of the wheel speed sensors of the front and rear wheel groups (i.e., the tooth number ratio of the wheel speed sensors of the first front and rear wheel groups).

[0090] It should be noted that the preset unloaded correction coefficient and the preset standard frequency can be obtained through the self-learning process in the unloaded state and when the tires are fully inflated. The unloaded self-learning mode is the same as the loaded self-learning mode, and the loaded self-learning mode will be elaborated in detail below. To avoid repeated learning, the preset unloaded correction coefficient and the preset standard frequency obtained in the unloaded self-learning mode are directly stored in the EEPROM for standby.

[0091] In step S103, the standard pulse number of each tire in the unloaded supervision mode is calculated according to the preset unloaded correction coefficient, the tooth number ratio of the wheel speed sensors of the first front and rear wheel groups, and the actual pulse number of each tire in the unloaded supervision mode, and the relative rolling radius characteristic of each tire in the unloaded supervision mode is calculated according to the standard pulse number of each tire in the unloaded supervision mode.

[0092] It can be understood that for the relative rolling radius method, the load of a conventional freight vehicle is often concentrated at the rear of the vehicle, resulting in greater pressure on the rear axle tires, relatively higher air pressure, and larger rolling radii. Therefore, the rotational speeds of the two tires of the rear axle (i.e., the rear wheel group) will be lower than those of the two tires of the front axle (i.e., the front wheel group). Correspondingly, the number of pulses generated by the rear wheel group will be more than that of the front wheel group. Each time the tooth ring of the wheel speed sensor passes a tooth, a pulse signal is generated, and the pulse number is incremented by one. Therefore, it is necessary to consider the tooth number difference of the wheel speed sensors of the front and rear wheel groups when performing tire pressure monitoring. When the tire pressures of the front and rear wheel groups are inconsistent and there is a difference in the tooth numbers of the wheel speed sensors of the front and rear wheel groups, these two situations themselves will cause a difference in the pulse number. In this case, if a single pulse number difference is still used for comparison, misjudgment is likely to occur. Therefore, in the embodiments of the present application, the actual pulse number of each tire is corrected by the correction coefficient, and the correction coefficient can also correct the negative impact on tire pressure monitoring caused by the deviation of the tire pressure in the initial state.

[0093] It should be noted that when there are differences in the number of teeth of the wheel speed sensors of the front and rear wheel groups, first, the actually measured pulse numbers of each tire of the rear wheel group can be normalized. That is, the actually measured pulse numbers of each tire of the rear wheel group are respectively multiplied by the tooth number ratio of the wheel speed sensors of the first front and rear wheel groups to obtain the normalized pulse numbers. In all subsequent calculation processes, the actually measured pulse numbers of each tire of the rear wheel group are calculated using the normalized pulse numbers.

[0094] That is to say, based on the preset no-load correction coefficient, combined with the tooth number ratio of the wheel speed sensors of the first front and rear wheel groups and the actually measured pulse numbers of each tire in the no-load supervision mode, the standard pulse numbers of each tire in the no-load supervision mode can be calculated. Specifically, multiplying the actually measured pulse numbers of each tire of the front wheel group by the corresponding preset no-load correction coefficient can obtain the standard pulse numbers of each tire of the front wheel group, as shown in Equations (1)-(2); after multiplying the actually measured pulse numbers of each tire of the rear wheel group by the corresponding preset no-load correction coefficient and then multiplying by the tooth number ratio of the wheel speed sensors of the first front and rear wheel groups, the standard pulse numbers of each tire of the rear wheel group can be obtained, as shown in Equations (3)-(4) (equivalent to calculating the standard pulse numbers of each tire of the rear wheel group using the normalized pulse numbers).

[0095] Through these standard pulse numbers, the relative rolling radius characteristics of each tire in the no-load supervision mode can be further deduced. Specifically, calculate the average value of the standard pulse numbers of the other 3 tires except the i-th tire, as shown in Equation (5); and calculate the relative error between the standard pulse number of the i-th tire and the average value of the standard pulse numbers of the other 3 tires, as shown in Equation (6). Select the smallest relative error among the four tires as the standard value, and subtract the standard value from the relative error of each tire respectively. The result obtained is the relative rolling radius characteristic of each tire.

[0096] N sfl =C fl ×N fl ; (1)

[0097] N sfr =C fr ×N fr ; (2)

[0098] N srl =C rl ×N rl ×n; (3)

[0099] N srr =C rr ×N rr ×n; (4)

[0100]

[0101] Among them, N sfl , N sfr , N srl , N srr are the standard pulse numbers of the left front tire, right front tire, left rear tire, and right rear tire in sequence, and C fl , C fr , C rl , C rr are the preset no-load correction coefficients of the left front tire, right front tire, left rear tire, and right rear tire in sequence, and N fl , N fr , N rl , N rr are the actual pulse numbers of the left front tire, right front tire, left rear tire, and right rear tire in the no-load supervision mode in sequence, n is the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets, is the average value of the standard pulse numbers of the other 3 tires excluding the i-th tire, and N sj is the relative error between the standard pulse number of the i-th tire and the average value of the standard pulse numbers of the other 3 tires, and N si is the standard pulse number of the i-th tire.

[0102] In step S104, the relative rolling radius characteristics of each tire in the no-load supervision mode are respectively compared with a first preset threshold. When the relative rolling radius characteristic of any tire in the no-load supervision mode is greater than the first preset threshold, it is determined that the corresponding tire of the vehicle is in an underpressure state.

[0103] Among them, the first preset threshold can be preset by the user, can be obtained through a limited number of experiments, or can be obtained through a limited number of computer simulations, and is not specifically limited here.

[0104] Specifically, after obtaining the relative rolling radius characteristics of each tire in the no-load supervision mode, by comparing the relative rolling radius characteristics of each tire with the first preset threshold one by one, when it is found that the relative rolling radius characteristic of any tire exceeds the first preset threshold, it can be determined that the corresponding tire of the vehicle is currently in an underpressure state, and an underpressure warning signal is sent simultaneously.

[0105] For example, when there are differences in the number of teeth of the wheel speed sensors of the front and rear wheel sets, assume that the number of teeth of the toothed rings of the two wheel speed sensors of the front wheel set is 43, and the number of teeth of the toothed rings of the two wheel speed sensors of the rear wheel set is 48. At this time, the tooth number ratio n of the wheel speed sensors of the first front and rear wheel sets is approximately 0.896. Assume that the actual pulse numbers of the four tires within 10 s in the no-load supervision mode are 5500, 5000, 5580, and 5580 respectively. In the previous no-load self-learning process (the four tires are all fully pressurized, and the vehicle is not steering. The normalized pulse numbers (actual pulse numbers) of each tire are 4000, 4000, (4464×0.896), and (4464×0.896) respectively), the preset no-load correction coefficient corresponding to each tire is 1. Calculate the standard pulse numbers of each tire according to formulas (1)-(4), that is, the standard pulse numbers of the left front tire, right front tire, left rear tire, and right rear tire are successively: 5500, 5000, 5000, 5000. Further calculate the relative error of each tire according to (5)-(6): Except for the left front tire, the average value of the standard pulse numbers of the other 3 tires is (5000 + 5000 + 5000) / 3 = 5000, and the relative error of the left front tire is Except for the right front tire, the average value of the standard pulse numbers of the other 3 tires is (5500 + 5000 + 5000) / 3 = 5167, and the relative error of the right front tire is Similarly, the relative errors of the left rear tire and the right rear tire are -0.323 and -0.323 respectively.

[0106] It can be seen from this that the standard value is -0.323. Then the relative rolling radius characteristics of each tire: The relative rolling radius characteristic of the left front tire is 0 - (-0.0323) = 0.0323, the relative rolling radius characteristic of the right front tire is -0.0323 - (-0.0323) = 0, the relative rolling radius characteristic of the left rear tire is -0.0323 - (-0.0323) = 0, and the relative rolling radius characteristic of the right rear tire is -0.0323 - (-0.0323) = 0. Assume that the first preset threshold is 0.3. Then the relative error of the left front tire is greater than this first preset threshold. From this, it can be determined that the left front tire is in an under-pressure state, and an alarm signal is issued.

[0107] It should be noted that for the case where the tire pressures of the front and rear wheel sets are the same, the theoretical value of the correction coefficient is approximately 1 (such as in the above example or like 0.998, 0.999, 1.0001, etc.). For the case where the tire pressure of the rear wheel set is different from that of the front wheel set (for example, the tire pressure of the rear wheel set is 650 Kpa and the tire pressure of the front wheel set is 450 Kpa), the theoretical value of the correction coefficient is not 1, and the specific value can be calculated through the corresponding formula (which will be elaborated below). In addition, when the tire pressures of the front and rear wheel sets are different, the tire with a higher pressure has a larger rolling radius, a slower rotation speed, and fewer actual pulses. The correction coefficient is inversely proportional to the number of pulses. When calculating the relative error of the four tires, by using the standard number of pulses corrected by the correction coefficient, the problem of different numbers of tire pulses between the front and rear wheel sets caused by different tire pressures of the front and rear wheel sets can be solved.

[0108] It is worth noting that when facing the underpressure situation of 1 to 3 tires, precise monitoring can be achieved through the relative rolling radius method, which relies on the change of the tire radius to judge the tire pressure state. However, when all four tires are underpressure at the same time, since the pressures of all tires decrease synchronously, the difference in the number of pulses between them becomes not obvious. Therefore, the relative rolling radius method cannot identify the existence of the underpressure situation in this case. However, precisely because when all four tires are underpressure at the same time, the pressures of all tires decrease synchronously, the real-time natural frequency of each tire is lower than the normal standard frequency. By monitoring this frequency change, the problem of tire underpressure can be indirectly identified. That is, once the natural frequency method issues an alarm, it can be determined that all four tires are underpressure at the same time.

[0109] Next, a detailed description will be given on how to use the natural frequency method to monitor the underpressure state of tires in the no-load supervision mode.

[0110] Optionally, in some other embodiments, after entering the no-load supervision mode based on a preset no-load correction coefficient and a preset standard frequency, it further includes: obtaining the wheel speed signal of the target tire in the no-load supervision mode, and processing the wheel speed signal of the target tire in the no-load supervision mode to obtain the processed no-load wheel speed signal; based on the processed no-load wheel speed signal, using a second-order autoregressive model to identify the natural frequency characteristics of the target tire in the no-load supervision mode; calculating the difference between the preset standard frequency and the natural frequency characteristics of the target tire in the no-load supervision mode, and comparing the first difference result with a second preset threshold. When the first difference result is greater than the second preset threshold, it is determined that each tire of the vehicle is in an underpressure state.

[0111] It should be noted that, in view of the relatively large consumption of computing resources by the natural frequency method during operation, in the embodiments of the present application, the natural frequency method is only implemented for one tire (referred to as the target tire in this article) to achieve supervision.

[0112] Specifically, the wheel speed signal of the target tire in the no-load supervision mode is further resampled and filtered to obtain the processed no-load wheel speed signal. Then, the AR second-order model (i.e., the second-order autoregressive model) can be used to identify the processed no-load wheel speed signal. By analyzing the autoregressive characteristics of the signal, the natural frequency characteristics of the target tire (i.e., the frequency corresponding to the real-time tire pressure) can be effectively identified. Next, calculate the difference result (i.e., the first difference result) after subtracting the natural frequency characteristics from the preset standard frequency corresponding to the target tire, and compare this first difference result with the second preset threshold. If the first difference result exceeds the second preset threshold, it can be determined that the target tire has an underpressure condition, and each tire of the vehicle is in an underpressure state. Among them, the second preset threshold can be preset by the user, obtained through a limited number of experiments, or obtained through a limited number of computer simulations, and no specific limitation is made here.

[0113] The process of indirect tire pressure monitoring for a vehicle in a loaded state will be described in detail below.

[0114] Optionally, in some embodiments, after determining whether the vehicle is in the no-load state based on the current mass, it further includes: if the vehicle is not in the no-load state, enter the load self-learning mode; based on the load self-learning mode, determine the load correction coefficient for each tire and the load standard frequency for each tire.

[0115] It can be understood that for a cargo vehicle, when the vehicle is loaded, the rolling radius of the tire will become smaller and the natural frequency will also change. At this time, even if the tire pressure is normal, the relative rolling radius characteristics and natural frequency characteristics are similar to those in the case of no-load underpressure, and it is easier to have false alarms of tire underpressure. Therefore, after the vehicle changes from no-load to load, it is possible to judge whether the tire has an underpressure condition by compensating for the two characteristics (relative rolling radius characteristics and natural frequency characteristics) or by re-obtaining new relative rolling radius characteristics and new natural frequency characteristics.

[0116] Specifically, if the vehicle is in a loaded state, it is necessary to re-perform the self-learning process, that is, enter the load self-learning mode, recalculate the correction coefficient (i.e., the load correction coefficient) and the standard frequency (i.e., the load standard frequency) for each tire according to the current tire condition of the vehicle, and after the load self-learning is completed, normally enter the load supervision mode.

[0117] As a possible implementation method, in some embodiments, based on the load self-learning mode, determining the load correction coefficient of each tire and the load standard frequency of each tire includes: obtaining the actual pulse number of each tire and the tooth number ratio of the wheel speed sensors of the second front and rear wheel groups in the load self-learning mode; calculating the average pulse number based on the actual pulse number of each tire and the tooth number ratio of the wheel speed sensors of the second front and rear wheel groups in the load self-learning mode; and determining the load correction coefficient of each tire based on the actual pulse number of each tire and the average pulse number in the load self-learning mode.

[0118] Specifically, in the load self-learning mode, the system can collect the actual pulse number of each tire and the tooth number ratio of the wheel speed sensors of the front and rear wheel groups (i.e., the tooth number ratio of the wheel speed sensors of the second front and rear wheel groups) within a fixed time period. Then, the average pulse number is calculated according to the actual pulse number of each tire and the tooth number ratio of the wheel speed sensors of the second front and rear wheel groups in the load self-learning mode, as shown in Equation (7). Specifically, when the tooth numbers of the wheel speed sensors of the front and rear wheel groups are different, the actual pulse number of each tire of the rear wheel group can be processed first, that is, the actual pulse number of each tire of the rear wheel group is multiplied by the tooth number ratio of the wheel speed sensors of the second front and rear wheel groups to obtain the normalized pulse number. In all subsequent calculation processes, the actual pulse number of each tire of the rear wheel group is calculated using the normalized pulse number.

[0119]

[0120] Where N avg is the average pulse number, and N fl , N fr , N rl , N rr are the actual pulse numbers of the left front tire, right front tire, left rear tire, and right rear tire in the load self-learning mode in sequence.

[0121] According to the actual pulse number of each tire and the average pulse number in the load self-learning mode, the load correction coefficient of each tire can be further determined, as shown in Equations (8)-(11).

[0122]

[0123] Where C fl , C fr , C rl , C rr are the load correction coefficients of the left front tire, right front tire, left rear tire, and right rear tire in sequence.

[0124] As a possible implementation manner, in some embodiments, determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode further includes: resampling and filtering the wheel speed signal of each tire in the load self-learning mode to obtain the processed load wheel speed signal; based on the processed load wheel speed signal, using a second-order autoregressive model to estimate the current natural frequency characteristics of each tire in the load self-learning mode, and using the current natural frequency characteristics of each tire in the load self-learning mode as the load standard frequency of each tire.

[0125] Specifically, in the load self-learning mode, the system can also resample and filter the wheel speed signal of each tire to obtain the processed load wheel speed signal; then, as shown in Figure 2 , using the AR second-order model (i.e., the second-order autoregressive model) to identify the processed load wheel speed signal. By analyzing the autoregressive characteristics of the signal, the current natural frequency characteristics of each tire can be effectively identified. As shown in Equation (12), these frequency characteristics are subsequently regarded as the load standard frequency of each tire, and the identification results can be as shown in Figure 3 and Figure 4 .

[0126]

[0127] Among them, a1 and a2 are two parameters of the AR model, and T is the sampling time of the resampled wheel speed signal.

[0128] Further, the embodiments of the present application can use the Yule-walker equation combined with the recursive least squares method to estimate the parameters of the AR model, including the following steps:

[0129] The first step: Define the regression vector, that is,

[0130]

[0131] The second step: Gain calculation, that is,

[0132]

[0133] The third step: Prediction error, that is,

[0134] e(n) = y(n) - φ T (n)a e (n - 1); (15)

[0135] The fourth step: System update, that is,

[0136] a e (n) = a e (n - 1) + k(n)e(n); (16)

[0137] Step 5: Covariance matrix update, i.e.,

[0138]

[0139] where a e (n) is the parameter estimation value of the AR model at time n, P(n) is the covariance matrix at time n, λ is the forgetting factor, and I is the identity matrix. The parameter estimation values a1 and a2 of the AR model can be obtained through the Yule - walker equations (i.e., Eqs. (13)-(17)), and then substituting them into Eq. (12), the current natural frequency characteristics of the tire can be obtained.

[0140] After the load self - learning mode is completed (i.e., obtaining the load correction coefficient and load standard frequency of each tire), the load supervision mode can be entered to monitor the relative rolling radius characteristics and natural frequency characteristics of the tire in real time to ensure that the under - pressure condition of the tire can still be accurately judged under the load state.

[0141] Further, in some embodiments, after determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self - learning mode, it further includes: entering the load supervision mode based on the load correction coefficient of each tire and the load standard frequency of each tire; determining the relative rolling radius characteristics of each tire in the load supervision mode and the natural frequency characteristics of the target tire in the load supervision mode; respectively comparing the relative rolling radius characteristics of each tire in the load supervision mode with a third preset threshold, and when the relative rolling radius characteristic of any tire in the load supervision mode is greater than the third preset threshold, determining that the corresponding tire of the vehicle is in an under - pressure state; or, calculating the difference between the load standard frequency and the natural frequency characteristics of the target tire in the load supervision mode, and comparing the second difference result with a fourth preset threshold, and when the second difference result is greater than the fourth preset threshold, determining that the target tire of the vehicle is in an under - pressure state.

[0142] Among them, the third preset threshold and the fourth preset threshold can both be preset by the user, or obtained through a limited number of experiments, or obtained through a limited number of computer simulations, and no specific limitation is made here.

[0143] Specifically, after obtaining the load correction coefficient and the load standard frequency of each tire, enter the load monitoring mode based on the load correction coefficient of each tire and the load standard frequency of each tire. In the load monitoring mode, the system can continuously obtain the actual pulse number of each tire and the inherent frequency characteristics of the target tire, and determine the relative rolling radius characteristics of each tire in the load monitoring mode according to the actual pulse number of each tire, so as to compare the relative rolling radius characteristics of each tire in the load monitoring mode with a third preset threshold. In the case where the relative rolling radius characteristic of any tire is greater than the third preset threshold, it can be determined that the corresponding tire of the vehicle is in an underinflated state, and a corresponding warning signal is issued.

[0144] Similar to the no-load monitoring mode, when the four tires of the vehicle are simultaneously in an underinflated state, the relative rolling radius method fails. Then, the inherent frequency method can be used. Subtract the inherent frequency characteristics of the target tire in the load monitoring mode from the load standard frequency, and compare the obtained second difference result with a fourth preset threshold. If the second difference result is greater than the fourth preset threshold, it can be determined that the target tires of the vehicle are all in an underinflated state, and the four tires of the vehicle are simultaneously in an underinflated state, and a corresponding warning signal is issued.

[0145] It should be noted that the method for obtaining the relative rolling radius characteristics of each tire in the load monitoring mode and the inherent frequency characteristics of the target tire in the load monitoring mode is the same as that in the no-load monitoring mode. To avoid redundancy, it will not be elaborated here.

[0146] To facilitate those skilled in the art to further understand the indirect tire pressure monitoring method proposed in the embodiments of the present application, the following will be further elaborated in combination with Figure 5 and examples.

[0147] As Figure 5 shown, the indirect tire pressure monitoring method may further include the following steps:

[0148] In step S501, estimate the current mass of the vehicle.

[0149] In step S502, determine whether the current mass is greater than the mass calibration threshold for distinguishing between the no-load state and the load state. If so, execute step S503a; otherwise, execute step S503b.

[0150] In step S503a, determine that the vehicle is in the load state.

[0151] In step S503b, determine that the vehicle is in the no-load state.

[0152] In step S504a, re-perform self-learning to obtain the load correction coefficient and the load standard frequency.

[0153] In step S504b, obtain the no-load correction factor (preset no-load correction factor) and the standard frequency (preset standard frequency) stored in the EEPROM.

[0154] In step S505a, enter the load monitoring mode.

[0155] In step S505b, enter the no-load monitoring mode.

[0156] In step S506, determine whether the change amount of the current mass is greater than the upper threshold of the mass change. If so, execute step S504a; otherwise, execute step S507.

[0157] In step S507, maintain the load monitoring mode.

[0158] In step S508, perform an undervoltage alarm.

[0159] Suppose the no-load mass of a certain model of freight vehicle is 1500 kg, and the mass calibration threshold for distinguishing the no-load state and the load state is 2000 kg. When the load mass of a certain model vehicle is within 500 kg, the influence on the tire rolling radius is small and will not cause false alarms in the system. Therefore, 500 kg is set as the upper threshold of the mass change. When the mass estimation value converges below 2000 kg, the system determines that the vehicle is in the no-load state; when the mass estimation value exceeds 2000 kg, the system determines that the vehicle is in the load state.

[0160] For the no-load state, when the vehicle is driving normally with a mass below 2000 kg, the system can directly call the no-load correction factor (preset no-load correction factor) and the standard frequency (preset standard frequency) stored in the EEPROM and enter the no-load monitoring mode. In the no-load monitoring mode, the system can monitor the relative rolling radius characteristics and the natural frequency characteristics of each tire in real time and compare them with the corresponding preset thresholds. If it is not greater than the preset threshold, the system maintains normal operation; if it exceeds the preset threshold, it is determined that the vehicle's tire has an undervoltage condition and a corresponding warning signal is issued.

[0161] For the load state, after the vehicle loads 700 kg of goods, the total vehicle mass becomes 2200 kg, exceeding the calibration threshold of 2000 kg for no-load and load. The system can determine that the vehicle is in the load state. The system first enters the load self-learning mode and recalculates the load correction factor and the load standard frequency according to the actual condition of the current tire. After the load self-learning is completed, the system enters the load monitoring mode and monitors the relative rolling radius characteristics of each tire and the natural frequency characteristics of the target tire in real time to ensure that the undervoltage condition of the tire can be accurately judged under the load state.

[0162] For the handling when the load changes, (1) when the vehicle mass increases from 2200 kg to 2800 kg, the change difference of the mass estimated value is 600 kg, exceeding the upper limit threshold of the mass change set by the system, which is 500 kg. At this time, the system determines that there is a loading situation for the vehicle and re-enters the load self-learning mode to obtain the load correction coefficient and the load standard frequency corresponding to 2800 kg. (2) When the vehicle mass decreases from 2800 kg to 2100 kg, the change difference of the mass estimated value is 700 kg, still exceeding the upper limit threshold of the mass change, which is 500 kg. At this time, the system determines that there is an unloading situation for the vehicle and re-enters the load self-learning mode to obtain the load correction coefficient and the load standard frequency corresponding to 2100 kg. (3) When the vehicle mass decreases from 2800 kg to 1800 kg, the system determines that the vehicle is in the unloaded mode. Then, there is no need to re-perform self-learning. In this case, the system can directly call the unloaded correction coefficient and the standard frequency stored in the EEPROM and enter the unloaded supervision mode.

[0163] According to the indirect tire pressure monitoring method proposed by the embodiments of the present application, by introducing the real-time estimation and self-learning mechanism of the vehicle load state, it is possible to dynamically distinguish the unloaded and loaded states of the vehicle, and select the appropriate tire pressure monitoring mode according to the current load situation, solve the problems of missed reports and false reports existing in the existing monitoring technologies, and improve the accuracy of tire pressure monitoring.

[0164] Next, the indirect tire pressure monitoring device proposed by the embodiments of the present application will be described with reference to the accompanying drawings.

[0165] Figure 6 It is a block diagram of the indirect tire pressure monitoring device according to an embodiment of the present application.

[0166] As Figure 6 shown, the indirect tire pressure monitoring device 10 includes: a first acquisition module 100, a second acquisition module 200, a calculation module 300, and a determination module 400.

[0167] Among them, the first acquisition module 100 is used to acquire the current mass of the vehicle and determine whether the vehicle is in the unloaded state based on the current mass;

[0168] The second acquisition module 200 is used to enter the unloaded supervision mode based on the preset unloaded correction coefficient and the preset standard frequency when the vehicle is in the unloaded state, and acquire the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets of the vehicle and the actual pulse number of each tire in the unloaded supervision mode;

[0169] A calculation module 300, configured to calculate the standard pulse number of each tire in the no-load supervision mode according to a preset no-load correction coefficient, the tooth number ratio of the wheel speed sensors of the first front and rear wheel sets, and the actual pulse number of each tire in the no-load supervision mode, and calculate the relative rolling radius characteristic of each tire in the no-load supervision mode according to the standard pulse number of each tire in the no-load supervision mode;

[0170] A determination module 400, configured to compare the relative rolling radius characteristics of each tire in the no-load supervision mode with a first preset threshold respectively, and determine that the corresponding tire of the vehicle is in an under-pressure state when the relative rolling radius characteristic of any tire in the no-load supervision mode is greater than the first preset threshold.

[0171] Optionally, in some embodiments, after entering the no-load supervision mode based on a preset no-load correction coefficient and a preset standard frequency, the second acquisition module 200 further includes:

[0172] Obtain the wheel speed signal of the target tire in the no-load supervision mode, and process the wheel speed signal of the target tire in the no-load supervision mode to obtain a processed no-load wheel speed signal;

[0173] Based on the processed no-load wheel speed signal, use a second-order autoregressive model to identify the natural frequency characteristic of the target tire in the no-load supervision mode;

[0174] Perform a difference calculation on the preset standard frequency and the natural frequency characteristic of the target tire in the no-load supervision mode, and compare the first difference result with a second preset threshold. When the first difference result is greater than the second preset threshold, determine that each tire of the vehicle is in an under-pressure state.

[0175] Optionally, in some embodiments, after determining whether the vehicle is in the no-load state based on the current mass, the first acquisition module 100 further includes:

[0176] A control unit, configured to enter a load self-learning mode when the vehicle is not in the no-load state;

[0177] A determination unit, configured to determine the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode.

[0178] Optionally, in some embodiments, the determination unit is specifically configured to:

[0179] Obtain the actual pulse number of each tire in the load self-learning mode and the tooth number ratio of the wheel speed sensors of the second front and rear wheel sets;

[0180] Calculate the average pulse number based on the actual pulse number of each tire in the load self-learning mode and the tooth number ratio of the wheel speed sensors of the second front and rear wheel sets;

[0181] Determine the load correction coefficient of each tire based on the actual pulse number and average pulse number of each tire in the load self-learning mode.

[0182] Optionally, in some embodiments, the determining unit is further configured to:

[0183] Resample and filter the wheel speed signal of each tire in the load self-learning mode to obtain a processed load wheel speed signal;

[0184] Based on the processed load wheel speed signal, estimate the current natural frequency characteristics of each tire in the load self-learning mode by using a second-order autoregressive model, and use the current natural frequency characteristics of each tire in the load self-learning mode as the load standard frequency of each tire.

[0185] Optionally, in some embodiments, after determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode, the determining unit is further configured to:

[0186] Enter the load supervision mode based on the load correction coefficient of each tire and the load standard frequency of each tire;

[0187] Determine the relative rolling radius characteristics of each tire in the load supervision mode and the natural frequency characteristics of the target tire in the load supervision mode;

[0188] Compare the relative rolling radius characteristics of each tire in the load supervision mode with a third preset threshold respectively. In the case where the relative rolling radius characteristics of any tire in the load supervision mode are greater than the third preset threshold, it is determined that the corresponding tire of the vehicle is in an underinflated state;

[0189] Or, calculate the difference between the load standard frequency and the natural frequency characteristics of the target tire in the load supervision mode, and compare the second difference result with a fourth preset threshold. In the case where the second difference result is greater than the fourth preset threshold, it is determined that all the target tires of the vehicle are in an underinflated state.

[0190] Optionally, in some embodiments, the first acquisition module 100 is specifically configured to:

[0191] Acquire the real-time speed, real-time motor torque, real-time transmission ratio, and tire radius of the vehicle when the vehicle is in the acceleration phase;

[0192] Obtain the real-time acceleration of the vehicle based on the real-time speed, and estimate the longitudinal driving force of the vehicle based on the real-time motor torque, real-time transmission ratio, and tire radius;

[0193] Calculate the current mass of the vehicle by using the Kalman filter algorithm based on the real-time acceleration and the longitudinal driving force.

[0194] It should be noted that the foregoing explanation of the embodiments of the indirect tire pressure monitoring method also applies to the indirect tire pressure monitoring device of this embodiment, and will not be elaborated here.

[0195] The indirect tire pressure monitoring device proposed according to the embodiments of the present application can dynamically distinguish the unloaded and loaded states of the vehicle by introducing real-time estimation and self-learning mechanisms of the vehicle load state, and select appropriate tire pressure monitoring modes according to the current load conditions, solving the problems of missed reports and false reports existing in the existing monitoring technologies and improving the accuracy of tire pressure monitoring.

[0196] Figure 7 It is a schematic structural diagram of a vehicle provided by an embodiment of the present application. The vehicle may include:

[0197] A memory 701, a processor 702, and a computer program stored on the memory 701 and executable on the processor 702.

[0198] When the processor 702 executes the program, it implements the indirect tire pressure monitoring method provided in the foregoing embodiment.

[0199] Furthermore, the vehicle further includes:

[0200] A communication interface 703 for communication between the memory 701 and the processor 702.

[0201] The memory 701 is used to store a computer program executable on the processor 702.

[0202] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0203] If the memory 701, the processor 702, and the communication interface 703 are implemented independently, the communication interface 703, the memory 701, and the processor 702 may be interconnected through a bus and communicate with each other. The bus may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 7 only a thick line is shown in, but it does not mean that there is only one bus or one type of bus.

[0204] Optionally, in a specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on a single chip, the memory 701, the processor 702, and the communication interface 703 can communicate with each other through an internal interface.

[0205] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0206] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the indirect tire pressure monitoring method as described above is implemented.

[0207] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the indirect tire pressure monitoring method as described above is implemented.

[0208] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0209] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0210] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An indirect tire pressure monitoring method, characterized in that: The following steps are involved: Acquiring a current mass of a vehicle, and determining whether the vehicle is in an unloaded state based on the current mass; If the vehicle is in the no-load state, the no-load monitoring mode is entered based on a preset no-load correction coefficient and a preset standard frequency, and the gear ratio of the wheel speed sensor of the first front and rear wheel groups of the vehicle and the actual pulse number of each tire in the no-load monitoring mode are obtained; Calculating the standard pulse number of each tire in the no-load supervision mode according to the preset no-load correction coefficient, the gear ratio of the wheel speed sensors of the first front and rear wheel sets, and the actual pulse number of each tire in the no-load supervision mode, and calculating the relative rolling radius characteristic of each tire in the no-load supervision mode according to the standard pulse number of each tire in the no-load supervision mode; The relative rolling radius characteristics of each tire in the no-load supervision mode are compared with a first preset threshold value respectively. When the relative rolling radius characteristics of any tire in the no-load supervision mode is greater than the first preset threshold value, it is determined that the corresponding tire of the vehicle is in an under-pressure state.

2. The method according to claim 1, characterized in that After entering the no-load supervision mode based on the preset no-load correction coefficient and the preset standard frequency, the method further includes: Acquiring a wheel speed signal of a target tire in the no-load monitoring mode, and processing the wheel speed signal of the target tire in the no-load monitoring mode to obtain a processed no-load wheel speed signal; Based on the processed no-load wheel speed signal, using a second-order autoregressive model to identify the natural frequency characteristics of the target tire in the no-load supervision mode; A difference calculation is performed between the preset standard frequency and the natural frequency characteristic of the target tire in the no-load supervision mode, and a first difference result is compared with a second preset threshold value. When the first difference result is greater than the second preset threshold value, it is determined that each tire of the vehicle is in the under-pressure state.

3. The method according to claim 1, characterized in that After determining whether the vehicle is in the no-load state based on the current mass, the method further includes: If the vehicle is not in the no-load state, entering a load self-learning mode; Based on the load self-learning mode, a load correction coefficient of each tire and a load standard frequency of each tire are determined.

4. The method according to claim 3, characterized in that The step of determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode includes: Obtaining the actual pulse number of each tire in the load self-learning mode and the wheel speed sensor gear ratio of the second front and rear wheel sets; Calculating an average pulse number based on the actual pulse number of each tire in the load self-learning mode and the gear ratio of the wheel speed sensors of the second front and rear wheel sets; The load correction coefficient of each tire is determined based on the actual pulse number of each tire and the average pulse number in the load self-learning mode.

5. The method according to claim 4, characterized in that The method of determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode further includes: Resampling and filtering the wheel speed signal of each tire in the load self-learning mode to obtain a processed load wheel speed signal; Based on the processed load wheel speed signal, a second-order autoregressive model is used to estimate the current natural frequency characteristics of each tire in the load self-learning mode, and the current natural frequency characteristics of each tire in the load self-learning mode are used as the load standard frequency of each tire.

6. The method according to claim 3, characterized in that After determining the load correction coefficient of each tire and the load standard frequency of each tire based on the load self-learning mode, the method further includes: Entering a load monitoring mode based on the load correction factor of each tire and the load standard frequency of each tire; Determining a relative rolling radius characteristic of each tire in the load monitoring mode and a natural frequency characteristic of a target tire in the load monitoring mode; respectively comparing the relative rolling radius characteristic of each tire in the load supervision mode with a third preset threshold value, and determining that the corresponding tire of the vehicle is in the under-pressure state when the relative rolling radius characteristic of any tire in the load supervision mode is greater than the third preset threshold value; Alternatively, a difference calculation is performed between the load standard frequency and the natural frequency characteristics of the target tire under the load supervision mode, and the second difference result is compared with a fourth preset threshold value. When the second difference result is greater than the fourth preset threshold value, it is determined that the target tires of the vehicle are all in the under-pressure state.

7. The method according to claim 1, characterized in that The obtaining of the current mass of the vehicle includes: Acquiring the real-time speed, real-time motor torque, real-time transmission ratio and tire radius of the vehicle when it is in the acceleration stage; Obtaining a real-time acceleration of the vehicle based on the real-time speed, and estimating a longitudinal driving force of the vehicle based on the real-time motor torque, the real-time transmission ratio, and the tire radius; The current mass of the vehicle is calculated based on the real-time acceleration and the longitudinal driving force using a Kalman filter algorithm.

8. An indirect tire pressure monitoring device, characterized in that: include: A first acquisition module, used for acquiring the current mass of the vehicle, and judging whether the vehicle is in an unloaded state based on the current mass; A second acquisition module is used for, when the vehicle is in the no-load state, entering the no-load supervision mode based on a preset no-load correction coefficient and a preset standard frequency, and acquiring a gear ratio of a wheel speed sensor of a first front and rear wheel set of the vehicle and an actual pulse number of each tire in the no-load supervision mode; a calculation module, configured to calculate a standard pulse number of each tire in the no-load supervision mode according to the preset no-load correction coefficient, the gear ratio of the wheel speed sensors of the first front and rear wheel sets, and the actual pulse number of each tire in the no-load supervision mode, and calculate a relative rolling radius characteristic of each tire in the no-load supervision mode according to the standard pulse number of each tire in the no-load supervision mode; The determination module is used to compare the relative rolling radius characteristics of each tire in the no-load supervision mode with a first preset threshold value respectively, and determine that the corresponding tire of the vehicle is in an under-pressure state when the relative rolling radius characteristics of any tire in the no-load supervision mode is greater than the first preset threshold value.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the indirect tire pressure monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the indirect tire pressure monitoring method as described in any one of claims 1-7.

11. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, is used to implement the indirect tire pressure monitoring method according to any one of claims 1 to 7.

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