Tire air pressure estimation device and tire air pressure estimation method

Abstract

A tire pressure estimation device and a tire pressure estimation method are provided that can estimate tire air pressure with high accuracy. [Solution] Tire pressure estimation device 1 is a tire pressure estimation device that estimates the air pressure of a vehicle tire. Tire pressure estimation device 1 includes extraction means 9 that extracts rotation-synchronous components from a wheel speed signal while the vehicle is running, frequency analysis means 10 that performs a predetermined frequency analysis on the output from the wheel speed signal after the rotation-synchronous components have been removed, and air pressure value acquisition means 11 that inputs vibration components in a predetermined frequency band obtained by the frequency analysis to relationship setting means 12 to acquire an air pressure value. Extraction means 9 calculates the rotation-synchronous components by averaging the wheel speed signal divided for each tire rotation.

Description

[Technical Field]

[0001] The present invention relates to a tire pressure estimation device and a tire pressure estimation method for estimating the air pressure of each tire mounted on a vehicle. [Background technology]

[0002] Tires have natural vibration modes that depend on their structure and material properties. Among these natural vibration modes, there are modes in which the tire elastically displaces in the radial and circumferential directions. Because the natural frequencies of these radial and circumferential modes are affected by tire air pressure, it is possible to estimate tire air pressure from fluctuations in wheel rotation speed. A method has been proposed in the past to estimate tire pressure by frequency-analyzing wheel speed sensor outputs containing wheel speed fluctuation components while the vehicle is running, and using vibration information in a predetermined frequency band containing the above-mentioned natural vibration (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 2836652 Summary of the Invention [Problem to be solved by the invention]

[0004] The wheel speed fluctuation information contained in the wheel speed signal contains a mixture of two types of components: components synchronized with rotation and components asynchronous with rotation. The rotation-synchronized component contains noise resulting from manufacturing errors in the wheel speed sensor, and this noise is an estimation-impeding factor that hinders accurate estimation of tire pressure. The manufacturing errors in the wheel speed sensor include, for example, pitch errors inherent to the sensor resulting from variations in the magnetization accuracy of the north and south magnetic poles.

[0005] An object of the present invention is to provide a tire pressure estimation device and a tire pressure estimation method that can estimate tire pressure with high accuracy. [Means for solving the problem]

[0006] The tire pressure estimation device 1 of the present invention is a tire pressure estimation device that estimates the air pressure of a tire in a vehicle, extraction means 9 for extracting rotation-synchronous components from wheel speed signals while the vehicle is running; a frequency analysis means for performing a predetermined frequency analysis on an output obtained by removing the rotation-synchronous component from the wheel speed signal; and an air pressure value acquisition means 11 that inputs the vibration components in the determined frequency band obtained by the frequency analysis into a relationship setting means 12 to acquire an air pressure value, The extraction means 9 calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire revolution. The predetermined frequency analysis is a frequency analysis that is arbitrarily determined by design or the like, and an appropriate frequency analysis is determined using, for example, either one or both of a test and a simulation. The determined frequency band is a frequency band that includes tire natural frequencies that are useful for tire pressure estimation, and an appropriate frequency band is determined using, for example, either one or both of tests and simulations.

[0007] With this configuration, tire pressure is estimated after removing rotation-synchronous components from the wheel speed signal while the vehicle is running. This allows tire pressure to be estimated after removing noise resulting from, for example, manufacturing errors of the wheel speed sensor Sa that outputs the wheel speed signal. Therefore, tire pressure can be estimated with higher accuracy than in the conventional example.

[0008] The relationship setting means 12 may be a machine learning model or a map that defines the relationship between vibration components in a specified frequency band and air pressure values. When a machine learning model is applied as the relationship setting means 12, the tire air pressure can be estimated with higher accuracy. When a map that defines the relationship between vibration components in a specified frequency band and air pressure values ​​is applied as the relationship setting means 12, the control system can be simplified compared to when a machine learning model is applied.

[0009] The machine learning model may be a convolutional neural network model, which may provide a more accurate estimation of tire pressure than multiple linear regression or decision tree methods.

[0010] The wheel speed signal may be acquired from a wheel speed sensor Sa that includes a magnetic pole 7 on the rotating wheel side and a Hall IC 8 on the fixed wheel side. In this case, an existing wheel speed sensor used for ABS control or the like can be used to extract rotation-synchronous components while the vehicle is running. When multiple Hall ICs 8 are provided on the fixed wheel side, the time resolution of the wheel speed sensor Sa is improved, and a spectrum in a frequency band affected by air pressure can be obtained even at low speeds. This improves the accuracy of air pressure estimation compared to a wheel speed sensor with a single Hall IC.

[0011] The tire air pressure estimation method of the present invention is a tire air pressure estimation method for estimating the air pressure of a tire in a vehicle, comprising: an extraction step of extracting rotation-synchronous components from wheel speed signals while the vehicle is running; a frequency analysis step of performing a predetermined frequency analysis on an output obtained by removing the rotation-synchronous component from the wheel speed signal; an air pressure value acquisition step of inputting the vibration components in the determined frequency band obtained by the frequency analysis into a relationship setting means to acquire an air pressure value, The extraction process calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire revolution.

[0012] With this configuration, tire pressure is estimated after removing rotation-synchronous components from the wheel speed signal while the vehicle is running, so tire pressure can be estimated after removing noise resulting from, for example, manufacturing errors of the wheel speed sensor Sa. Therefore, tire pressure can be estimated with higher accuracy than in the conventional example. [Effects of the Invention]

[0013] The tire pressure estimation device of the present invention is a tire pressure estimation device for estimating tire pressure on a vehicle, and includes: extraction means for extracting a rotation-synchronous component from a wheel speed signal while the vehicle is running; frequency analysis means for performing a predetermined frequency analysis on an output from the wheel speed signal after removing the rotation-synchronous component; and pressure value acquisition means for inputting vibration components in a predetermined frequency band obtained by the frequency analysis into relationship setting means to acquire a tire pressure value. The extraction means calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire rotation. This allows for more accurate tire pressure estimation than conventional methods. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram showing a conceptual configuration of a tire air pressure estimation device according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram of a control system showing an ECU equipped with the tire pressure estimation device, each tire mounted on a vehicle, and a wheel speed sensor installed on each tire. [Figure 3] 4 is a flowchart showing a process performed by the tire pressure estimation device in stages. [Figure 4] 4 is a diagram showing an example of a rotation-synchronous component extracted by the tire pressure estimation device. FIG. [Figure 5] 10A and 10B are diagrams showing the results of Fourier transform of wheel speed sensor outputs when rotation-synchronous components are removed and when they are not removed. [Figure 6] FIG. 10 is a diagram showing an amplitude spectrum waveform obtained by Fourier transforming the wheel speed sensor output without removing rotation-synchronous components. [Figure 7] FIG. 10 is a diagram showing an amplitude spectrum waveform obtained by performing a Fourier transform on the wheel speed sensor output after removing rotation-synchronous components. [Figure 8] FIG. 10 is a diagram showing the distribution of air pressure estimated values ​​by CNN for each correct air pressure value. DETAILED DESCRIPTION OF THE INVENTION

[0015] [First embodiment] A tire pressure estimation device according to an embodiment of the present invention will be described with reference to Figures 1 to 8. The following description also includes a description of a tire pressure estimation method. The tire pressure estimation device estimates the tire pressure of a vehicle such as an automobile, for example, a four-wheeled vehicle. The tire pressure estimation device may also estimate the tire pressure of a vehicle with six or more wheels, such as a large truck. In addition, the tire pressure estimation device can also estimate the tire pressure of vehicles such as two-wheeled and three-wheeled vehicles.

[0016] 1 and 2, for example, an apparatus including an electronic control unit (ECU) mounted on a vehicle, a plurality of tires FL, FR, RL, RR rotatably supported on the vehicle, and wheel speed sensors Sa installed on each tire, estimates the air pressures of the tires FL, FR, RL, RR by the ECU. As shown in Fig. 2, the tire air pressure estimation device 1 is configured as, for example, the whole or part of the electronic control unit ECU.

[0017] The ECU may be an electronic control unit dedicated to the tire pressure estimation device, or may utilize part of an electronic control unit already installed in the vehicle. The ECU may be configured with electronic circuits such as a microcomputer including a CPU, ROM, and RAM so that it can perform Fourier transform processing and inference using a machine learning model, or may be configured to be electrically connected to a computer (not shown) capable of high-speed parallel processing. Each wheel speed sensor Sa is electrically connected to the ECU.

[0018] <Wheel speed sensors, etc.> As shown in Figure 1, the wheel speed sensor Sa is installed, for example, in a wheel support bearing 2. This wheel support bearing 2 is an inner-ring rotating type, and this example shows a wheel speed sensor Sa installed in the center of a double row. The wheel support bearing 2 includes an outer member 3, which is a fixed ring that forms a double-row rolling surface, an inner member 4, which is a rotating ring that forms a rolling surface that faces each of these rolling surfaces, and double-row rolling elements 5 interposed between the rolling surfaces of the outer member 3 and the inner member 4, and supports a wheel 6 rotatably relative to the vehicle body. The wheel 6 includes wheels and tires FL, FR, RL, and RR (Figure 2). FL represents the front left wheel, FR the front right wheel, RL the rear left wheel, and RR the rear right wheel.

[0019] The wheel speed sensor Sa includes a magnetic sensor consisting of magnetic poles 7 on the rotating wheel side and a Hall IC 8 on the fixed wheel side. Alternating north and south ring-shaped magnetic poles 7 are provided on the outer periphery between the two rolling surfaces of the inner member 4. Radial sensor mounting holes 3a are provided in the outer member 3, and Hall ICs 8 are attached to these sensor mounting holes 3a. The Hall ICs 8 are mounted on the outer member 3 so as to face the magnetic poles 7 across a radial gap. The wheel speed sensor Sa in this example is equipped with one Hall IC 8.

[0020] To improve the time resolution, the wheel speed sensor Sa may be provided with a plurality of Hall ICs 8 spaced at predetermined intervals in the circumferential direction on the fixed wheel side. The wheel speed sensor Sa may be an existing wheel speed sensor used for ABS control or the like, or may be a wheel speed sensor dedicated to the tire pressure estimation device.

[0021] The tire pressure estimation device 1 includes extraction means 9, frequency analysis means 10, and pressure value acquisition means 11. The extraction means 9 extracts rotation-synchronous components from a wheel speed signal while the vehicle is running. The wheel speed signal is acquired from a wheel speed sensor Sa, which includes the magnetic pole 7 and a Hall IC 8. The frequency analysis means 10 performs a predetermined frequency analysis on the output from which the rotation-synchronous components have been removed from the wheel speed signal. The pressure value acquisition means 11 inputs the vibration components in the predetermined frequency band obtained by the frequency analysis into relationship setting means 12 to acquire the tire pressure value.

[0022] <Tire pressure estimation method> The tire pressure estimation method includes an extraction step of extracting rotation-synchronous components from wheel speed signals while the vehicle is running, a frequency analysis step of performing a predetermined frequency analysis on the output obtained by removing the rotation-synchronous components from the wheel speed signals, and an air pressure value acquisition step of inputting the vibration components in the predetermined frequency band obtained by the frequency analysis into relationship setting means 12 to acquire the air pressure value. The extraction step calculates the rotation-synchronous components by averaging the wheel speed signals divided for each tire rotation.

[0023] Specifically, the tire pressure estimation method is performed as shown in the flowchart in FIG. 3 for outputs acquired at predetermined time intervals from wheel speed sensors Sa installed on wheel bearings 2 supporting each tire. Hereinafter, the description will be made with reference to FIG. 1 as needed. For example, this process is started when an output is acquired from wheel speed sensor Sa. After this process starts, first, in step 1, a rotation-synchronous component is calculated from the time interval between edges of the pulse signal acquired from wheel speed sensor Sa (extraction process). The rotation-synchronous component is calculated by extraction means 9 in FIG. 1 using, for example, Equation 1 ([Mathematical Expression 1]). The extraction means 9 calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire revolution.

[0024]

number

[0025] An example of the rotation-synchronous component calculated by Equation 1 is shown in Figure 4. In Figure 4, the "edge number" on the horizontal axis corresponds to "i" in Equation 1. 3, in the next step 2, rotation-synchronous components are removed from the wheel speed signal. This removal of rotation-synchronous components is performed by the frequency analysis means 10 in FIG. 1 using, for example, equation 2 ([Mathematical Expression 2]).

[0026]

number

[0027] Next, in step 3 shown in Fig. 3, the output of the wheel speed sensor from which the rotation-synchronous components have been removed is subjected to Fourier transform processing (predetermined frequency analysis) to obtain an amplitude spectrum (frequency analysis step). The Fourier transform processing is performed by the frequency analysis means 10 shown in Fig. 1. FIG. 5 shows an example of the results of Fourier transform of the output of the wheel speed sensor Sa obtained when the vehicle is traveling at a constant straight speed, when the rotation-synchronous component is removed (left side of the figure) and when the rotation-synchronous component is not removed (right side of the figure).

[0028] Next, in step 4 shown in FIG. 3, the amplitude spectrum (vibration component) acquired in step 3 is used as input to estimate the tire pressure using a machine learning model that is the relationship setting means 12 (FIG. 1) that has been trained in advance (air pressure value acquisition process). This air pressure value acquisition process is processed by the air pressure value acquisition means 11 in FIG. 1. The frequency band of the amplitude spectrum input in step 4 of FIG. 3 may be determined to include only a low frequency band of several tens of hertz that includes tire natural frequencies useful for tire pressure estimation. The low frequency band of several tens of hertz that includes the tire natural frequencies is, for example, a frequency band in the longitudinal or vertical direction in the unsprung part of the vehicle.

[0029] The machine learning model may be a deep learning model such as a convolutional neural network model (abbreviated as CNN model), or a light gradient boosting machine (abbreviated as LightGBM) based on a decision tree algorithm.When the tire pressure value estimated in step 4 is less than a threshold value set for the appropriate value, the ECU in Figure 2 controls the vehicle to output a warning light or a warning sound to alert the driver of the vehicle, for example. The order of tire pressure estimation is not limited to the steps shown in Fig. 3. For example, tire pressure estimation may be performed starting from step 2 in Fig. 3 by using a rotation-synchronous component calculated in advance from the wheel speed sensor output using Equation 1.

[0030] <Effectiveness of removing rotation-synchronous components> Here is an example to demonstrate the effectiveness of removing rotational synchronous components. Of the wheel speed sensor outputs obtained when a four-wheeled vehicle is traveling at a constant straight speed, Fig. 6 shows the amplitude spectrum waveform obtained by Fourier transforming the sensor outputs of the left front wheel FL and the right front wheel FR without removing the rotational synchronous components, and Fig. 7 shows the amplitude spectrum waveform obtained by Fourier transforming the sensor outputs after removing the rotational synchronous components.

[0031] Although slight differences in the waveforms can be seen due to differences in weight between the left and right sides of the vehicle, Fig. 7 shows that the tendency for changes in the amplitude spectrum waveform due to air pressures A, C, and E is roughly the same for the left front wheel FL and the right front wheel FR. On the other hand, Fig. 6 shows that the noise contained in the rotation-synchronous component, which is derived from manufacturing errors in the wheel speed sensors, differs from sensor to sensor, and therefore the amplitude spectrum waveforms differ greatly for the left front wheel FL and the right front wheel FR.

[0032] Figure 8 shows the results of air pressure estimation using the spectral data of the left front wheel FL for estimation with a CNN model trained using the spectral data of the right front wheel FR. Figure 8 shows the distribution of the CNN air pressure estimates against the correct values ​​for air pressures A, B, C, D, and E in a box plot. The left side of Figure 8 shows the results using spectra obtained by Fourier transform after removing rotation-synchronous components for both training and estimation.

[0033] The right side of Figure 8 shows the results of using the spectrum obtained by Fourier transform without removing the rotation-synchronous component. Figure 8 confirms that removing the rotation-synchronous component reduces the deviation between the correct air pressure value and the median value Me of the estimated values, and also tends to narrow the interquartile range (IQR). Therefore, when estimating air pressure using a pre-trained machine learning model that uses a spectrum calculated from the wheel speed sensor output as input, if a spectrum from which rotation-synchronous components have not been removed is used, it is likely that different wheel speed sensors will be used for learning and estimation, which could significantly reduce the accuracy of air pressure estimation.

[0034] <Action and effect> 1 and 3, tire pressure estimation device 1 and tire pressure estimation method described above estimate tire pressure after removing rotation-synchronous components from wheel speed signals while a vehicle is running. This allows tire pressure to be estimated after removing noise resulting from manufacturing errors of, for example, wheel speed sensors Sa that output wheel speed signals. This allows tire pressure to be estimated with higher accuracy than conventional methods.

[0035] In the pressure value acquisition process, the amplitude spectrum acquired after removing rotation-synchronous components is used as input to estimate the tire pressure value using a machine learning model, which allows tire pressure to be estimated with higher accuracy than conventional methods. If the frequency band of the input amplitude spectrum includes a low frequency band of several tens of Hz, the tire's natural frequency, which is useful for estimating tire pressure, is included, making it possible to estimate tire pressure with even higher accuracy.

[0036] The wheel speed signal input to the extraction means 9 shown in FIG. 1 is acquired from a wheel speed sensor Sa, which includes a magnetic pole 7 on the rotating wheel side and a Hall IC 8 on the fixed wheel side. In this case, an existing wheel speed sensor used for ABS control, etc., can be used to extract rotation-synchronous components while the vehicle is running. When multiple Hall ICs 8 are provided on the fixed wheel side, the time resolution of the wheel speed sensor Sa is improved, and a spectrum in a frequency band affected by air pressure can be obtained even at low speeds. This improves the accuracy of air pressure estimation compared to a wheel speed sensor with a single Hall IC.

[0037] <Other embodiments> In the following description, parts corresponding to matters previously described in each embodiment are given the same reference numerals, and duplicated description will be omitted. When only a part of the configuration is described, the other parts of the configuration are the same as those in the previously described embodiment unless otherwise specified. The same configuration produces the same effects. It is possible to combine not only the parts specifically described in each embodiment, but also partially combine embodiments as long as there is no particular problem with the combination.

[0038] [Second embodiment] Instead of the above-mentioned machine learning model, the relationship setting means 12 may be a map that defines the relationship between an amplitude spectrum, which is a vibration component in a predetermined frequency band, and an air pressure value. The relationship between the amplitude spectrum and the air pressure value is determined, for example, by testing and / or simulation. The map is stored, for example, in the ROM or RAM of the ECU (FIG. 2). When a map that defines the relationship between a vibration component in a predetermined frequency band and an air pressure value is used as the relationship setting means 12, the control system can be simplified compared to when a machine learning model is used. Other effects similar to those of the above-mentioned embodiment are achieved.

[0039] <Other> The wheel speed sensor Sa may be of a so-called axial type in which a Hall IC faces a magnetic pole via an axial gap. An outer ring rotating type may be applied as the wheel bearing 2. In this case, the fixed ring is the inner member (inner ring) and the rotating ring is the outer member (outer ring). The wheel bearing 2 is not limited to an angular contact ball bearing, and various rolling bearings such as a deep groove ball bearing and a tapered roller bearing can be used.

[0040] The wheel speed sensor Sa may, for example, comprise a detection gear provided on a rotating body that rotates integrally with the wheel 6, and a magnetic sensor that faces the detection gear across a predetermined gap and is fixed to a brake caliper or the like. The tire pressure estimation device and the tire pressure estimation method may estimate the pressure of only some of the tires on a vehicle.

[0041] Although the embodiments of the present invention have been described above, the disclosed embodiments are illustrative in all respects and are not limiting. The scope of the present invention is defined by the claims rather than the above description, and it is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0042] 1...Tire pressure estimation device 3...Outer member (fixed ring) 4...Inner member (rotating wheel) 7...Magnetic pole 8...Hall IC 9...Extraction means 10...Frequency analysis means 11...Air pressure value acquisition means 12...Relationship setting means Sa...Wheel speed sensor FL,FR,RL,RR...Tires

Claims

1. A tire pressure estimation device for estimating tire pressure in a vehicle, extraction means for extracting rotation-synchronous components from a wheel speed signal while the vehicle is running; a frequency analysis means for performing a predetermined frequency analysis on an output obtained by removing the rotation-synchronous component from the wheel speed signal; and an air pressure value acquisition means for inputting the vibration components in the determined frequency band obtained by the frequency analysis into a relationship setting means to acquire an air pressure value, The tire pressure estimation device, wherein the extraction means calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire rotation.

2. 2. The tire pressure estimation device according to claim 1, wherein the relationship setting means is a machine learning model or a map that defines the relationship between the vibration component in a predetermined frequency band and the tire pressure value.

3. 3. The tire pressure estimation device according to claim 2, wherein the machine learning model is a convolutional neural network model.

4. 3. The tire pressure estimation device according to claim 1, wherein the wheel speed signal is acquired from a wheel speed sensor including a magnetic pole on a rotating wheel side and a Hall IC on a fixed wheel side.

5. A tire pressure estimation method for estimating tire pressure in a vehicle, comprising: an extraction step of extracting rotation-synchronous components from wheel speed signals while the vehicle is running; a frequency analysis step of performing a predetermined frequency analysis on an output obtained by removing the rotation-synchronous component from the wheel speed signal; an air pressure value acquisition step of inputting the vibration components in the determined frequency band obtained by the frequency analysis into a relationship setting means to acquire an air pressure value, The tire pressure estimation method, wherein the extraction step calculates the rotation-synchronous component by averaging the wheel speed signal divided for each tire rotation.

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