Apparatus and method for estimating tire resonance frequency

An apparatus for estimating a tire resonance frequency may include a sensor for detecting rotation of a tone wheel; and a signal processor for calculating the detected rotation to produce a corrected wheel speed, filtering the corrected wheel speed in a predetermined manner to produce a filtered wheel speed from which engine noise is removed, and estimating a resonance frequency of a tire using the filtered wheel speed.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to and the benefit of Korean Patent Application No. 10-2018-0135481 filed on Nov. 6, 2018, which is incorporated herein by reference in its entirety.

FIELD

The present disclosure relates to a technique for estimating a tire resonance frequency; and, particularly, to an apparatus and method for estimating a tire resonance frequency, capable of detecting a low-pressure state of a tire regardless of engine noise.

BACKGROUND

It is generally determined whether a tire is in an insufficient pneumatic state by detecting a change in the radius of rotation and stiffness of the tire. More specifically, it is determined whether the tire is under low pressure by calculating a change in the radius of rotation and stiffness of the tire relative to the normal pressure thereof using a wheel speed sensor.

However, such a method lacks a technique for removing noise during engine explosion. That is, noise intervenes in an engine frequency. Hence, within a certain revolution per minute (RPM), the resonance frequency of the tire may be estimated in proportion to an engine RPM regardless of the air pressure of the tire.

Within the same tire pressure and a certain RPM (about 1700 RPM), the estimated frequency value also increases in proportion to the RPM. Hence, it is impossible to determine whether the tire is under low pressure through resonance frequency estimation within a certain RPM.

SUMMARY

One form of the present disclosure is directed to an apparatus and method for estimating a tire resonance frequency, capable of detecting a low-pressure state of a tire regardless of engine noise.

Another form of the present disclosure is directed to an apparatus and method for estimating a tire resonance frequency, capable of estimating a resonance frequency of a tire even in a low-speed region.

In some forms of the present disclosure, there is provided an apparatus for estimating a tire resonance frequency, capable of detecting a low-pressure state of a tire regardless of engine noise.

The apparatus includes a sensor for detecting rotation of a tone wheel, and a signal processor for calculating the detected rotation to produce a corrected wheel speed, filtering the corrected wheel speed in a predetermined manner to produce a filtered wheel speed from which engine noise is removed, and estimating a resonance frequency of a tire using the filtered wheel speed.

The filtering the corrected wheel speed may be performed using a notch filter designed using an engine frequency calculated from an engine revolution per minute (RPM).

The filtering the corrected wheel speed may be performed by respectively applying different predetermined weighted values to a band-pass filter, which is set in advance, and the notch filter.

The band-pass filter may have a predetermined radial vibration range of the tire.

The apparatus may further include a gyro sensor for measuring a longitudinal acceleration, a lateral acceleration, and a yaw rate of a vehicle, in addition to the sensor.

When the longitudinal acceleration or the lateral acceleration measured by the gyro sensor is larger than a predetermined first set value or the yaw rate measured by the gyro sensor is larger than a predetermined second set value, an operation of estimating the resonance frequency of the tire may not be executed.

When the signal processor receives position control information related to operating body position control of a vehicle, an operation of estimating the resonance frequency of the tire may not be executed.

When a speed of a vehicle is smaller than a predetermined third set value or larger than a predetermined fourth set value, the signal processor may not perform an operation of estimating the resonance frequency of the tire.

The corrected wheel speed may be produced by correcting a tone wheel angle and a tone wheel error angle and applying an average tone wheel error caused using an average filter for each pulse counter of each tone wheel.

The resonance frequency of the tire may be calculated by performing discretization and simplification using an auto-regressive model.

In another form of the present disclosure, there is provided a method for estimating a tire resonance frequency, which includes detecting rotation of a tone wheel by a sensor, calculating the detected rotation to produce a corrected wheel speed and filtering the corrected wheel speed in a predetermined manner to produce a filtered wheel speed from which engine noise is removed by a signal processor, and estimating a resonance frequency of a tire using the filtered wheel speed by the signal processor.

DETAILED DESCRIPTION

For example, without deviating from the scope and spirit of the present disclosure, a first element may be referred to as a second element, and, similarly, a second element may also be referred to as a first element. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Hereinafter, an apparatus and method for estimating a tire resonance frequency in some forms of the present disclosure will be described in detail with reference to the accompanying drawings.

FIG.1is a block diagram of an apparatus for estimating a tire resonance frequency in some forms of the present disclosure. Referring toFIG.1, the apparatus for estimating a tire resonance frequency may include a sensor110for detecting the rotation of a wheel, a signal processor120for producing a wheel speed according to the rotation detection of the wheel and estimating a tire resonance frequency by, for example, correcting and filtering the wheel speed to remove engine noise, a vehicle controller130for transmitting engine information, a vehicle signal, or the like to the signal processor120, and so on.

In addition to the sensor110, the apparatus may include a wheel speed sensor, a gyro sensor, and so on. The wheel speed sensor may be a wheel pulse counter. The gyro sensor measures the longitudinal/lateral acceleration and yaw rate of a vehicle. Therefore, the calculation of the tire resonance frequency is processed as an exception during turning or rapid acceleration/deceleration.

The signal processor120may include a wheel speed calculation module121for calculating the detected rotation of the wheel to produce a calculated wheel speed, a correction module122for correcting the calculated wheel speed to produce a corrected wheel speed, an interpolation module123for interpolating the corrected wheel speed to produce an interpolated wheel speed corresponding to the wheel speed at a certain sampling time Ts, a filtering module124for filtering the interpolated wheel speed in a predetermined manner to produce an engine noise-removed filtered wheel speed, a frequency calculation module125for estimating a tire resonance frequency using the filtered wheel speed, and so on.

The vehicle controller130functions to control the signal processor120and components for control of the vehicle. Especially, the vehicle controller130may be connected to an engine control unit (ECU) (not shown), which controls an engine (not shown), to acquire engine information. Examples of the engine information may include an engine revolution per minute (RPM), engine starting, and idling.

In addition, the vehicle controller130may be connected to an electronic stability controller (ESC)140. The electronic stability controller140functions to control the body position of the vehicle. To this end, the electronic stability controller140is connected to an antilock brake system (ABS), a traction control system (TCS), a vehicle dynamic control system (VDC), and the like to control the position of the vehicle. Therefore, the vehicle controller130may receive an ESC operation flag signal, which is position control information for operating the body position control of the vehicle, from the electronic stability controller140.

The vehicle controller130and the electronic stability controller140may include, for example, a microprocessor, a memory, and an electronic circuit to perform the control.

The term “module” described inFIG.1means a unit for processing at least one function or operation, which may be implemented by a combination of hardware and/or software. For hardware implementation, the processing unit may be implemented with application specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microprocessors, other electronic units, or a combination thereof, which are designed to perform the functions described above. For software implementation, the processing unit may be implemented with modules that perform the functions described above. The software may be stored in memory units and executed by processors. The memory units or the processors may adopt various means well known to those skilled in the art.

FIG.2is a conceptual view of installation of the sensor illustrated inFIG.1. Referring toFIG.2, the sensor110is installed to detect the rotation of a tone wheel210installed inside a vehicle wheel200. As the vehicle wheel200rotates, the tone wheel210rotates correspondingly so that the sensor110detects such rotation in real time. The sensor110may be a wheel speed sensor, especially a magnetoresistive (MR) sensor. Typically, the tone wheel210has a disk shape and in the form of a gear having teeth formed on the edge thereof. This is illustrated inFIG.5and will be described later.

FIG.3is a pulse diagram of an output signal of the sensor110illustrated inFIG.1. Referring toFIG.3, a wheel pulse counter operation310is the same as a generated time operation between rising edges320. That is, the number of rising edges generated within a logic sampling period (about 5 ms) is measured to count wheel pulses. Therefore, the wheel speed using the wheel pulse counter is expressed by the following equation:

FIG.4is a flowchart illustrating a process for estimating a tire resonance frequency in some forms of the present disclosure. Referring toFIG.4, as a sensor110detects the rotation of a vehicle wheel200(seeFIG.2), a signal is generated and transmitted to a signal processor120. When the signal is transmitted to the signal processor120, the signal processor120calculates that signal to produce a calculated wheel speed (S410).

Then, since the tone wheel angle has an error due to manufacturing dispersion, the signal processor120calculates and corrects the error to produce a corrected wheel speed (S420). That is, by correcting the error in the tone wheel angle, the wheel speed is calculated by the pulse counter of each tone wheel. In more detail, the wheel speed is calculated and corrected whenever a pulse counter is input. This may be defined as occurring in an event domain.

Then, the signal processor120linearly interpolates the corrected wheel speed to produce an interpolated wheel speed (S430). That is, the wheel speed is calculated at a certain sampling time Ts. This means a change from the event domain to a time domain.

Then, the signal processor120applies a band-pass filter of about 30 to 60 Hz to the interpolated wheel speed to produce a filtered wheel speed (S440). The frequency of 30 to 60 Hz typically refers to a radial vibration range of the tire.

Then, the signal processor120applies a notch filter, which is designed using the engine frequency calculated from the engine RPM, to the filtered wheel speed to remove engine noise therefrom (S450).

Then, the signal processor120calculates a tire resonance frequency through model-based parameter estimation (S460).

FIG.5is a conceptual view illustrating the general principle of generation of the tone wheel error. Referring toFIG.5, the tone wheel510has teeth510formed on the circumferential surface thereof and grooves520formed between the teeth510. Therefore, the tone wheel angle formed between two adjacent teeth510is 2π/number of tone wheels. Although it is assumed that these tone wheel angles are ideally the same, an error angle is due to manufacturing dispersion. Accordingly, it is necessary to correct a tone wheel error due to the error angle. This is expressed by the following equation:

Of course, it is assumed that the wheel speed is constant at one revolution. In addition, a one-revolution average wheel speed is assumed.

FIGS.6A to6Care detailed graphs of the correction operation process illustrated inFIG.4.FIG.6Ais a graph illustrating a wheel pulse counter operation. That is, the wheel speed is calculated whenever a pulse counter is input. That is, this is an event domain.

FIG.6Bis a graph illustrating a tone wheel error operation. The operation of the average tone wheel error using the average filter is as follows:

InFIG.6B, the term “deg” refers to a degree.

FIG.6Cis a graph illustrating a corrected wheel speed operation. That is, when the wheel speed is calculated by the pulse counter of each tone wheel through the correction of the tone wheel error angle illustrated inFIGS.6A and6B, the operation of the tone wheel error-corrected wheel speed for each tone wheel is performed. This is expressed by the following equation:

However, the operation of the corrected wheel speed is an exception if any of the following conditions is satisfied:vehicle body position control operation; andbraking operation and gear shifting.

InFIG.6C, “rad” refers to a radian.

FIG.7is a conceptual diagram for explaining linear interpolation of producing an interpolated wheel speed in the case of the corrected wheel speed illustrated inFIG.6C. Generally, in the case of a corrected wheel speed operation, the wheel speed is calculated whenever the rising edge of the tone wheel occurs. In this case, it corresponds to an event domain. Therefore, for the use of signal filtering and model-based parameter estimation techniques, there is required the wheel speed at a certain sampling time Ts.

That is, an interpolated wheel speed is produced using linear interpolation as illustrated inFIG.7. Referring toFIG.7, Ts is about 2 ms. That is, the tone wheel error-corrected wheel speed occurs in cycles of T0, T1, and T2, and the interpolated wheel speed occurs in cycles of t0, t1, t2, t3, and t4with a smaller certain sampling time Ts.

Therefore, the operation of the wheel speed at a certain sampling time is performed using the following equation:

Substituting a pulse time into the above equation is expressed by the following equation:

Therefore, the operation of the interpolated wheel speed y(ti) at a certain sampling time may be summarized as the following equation:

The parameter “α” is defined again as the following equation:

In addition, the time update is ti+1=ti+Ts (2 ms).

Accordingly, the linear interpolation process is as follows.

If the wheel speed interpolation is activated (=true), the interpolated wheel speed y(ti) is calculated while the remaining time is less than 0 (zero). Of course, the number of interpolations increases by +1, and the remaining time increases by +2 ms.

In contrast, if the wheel speed interpolation is not activated, the wheel speed interpolation is changed to be activated and it is set as “interpolated wheel speed [0]=corrected wheel speed [i]”, “number of interpolations=1”, and “remaining time=2 ms”. In this case, Y(k−1)=corrected wheel speed [i].

In addition, the operation of the interpolated wheel speed is processed as an exception during the calculation of the tire resonance frequency if any of the following conditions is satisfied (that is, the related variables being initialized):∥longitudinal acceleration∥>about 0.1 g;∥lateral acceleration∥>about 0.1 g;∥yaw rate∥>about 3 deg/s;vehicle speed<about 40 kph or vehicle speed>about 110 kph;vehicle body position control operation; andbraking operation and gear shifting detection.

FIG.8is a detailed graph of the band-pass filtering process illustrated inFIG.4. Referring toFIG.8, the band-pass filter of about 30 to 60 Hz is applied to the interpolated wheel speed for filtering it. When the band-pass filter820is applied to the interpolated wheel speed810for filtering it, a band-pass filtered wheel speed830is produced.

FIG.9is is a conceptual diagram of a notch filter for removing the engine noise illustrating inFIG.4. Referring toFIG.9, the 4-stoke explosion of an engine piston causes vehicle body vibration which becomes an engine frequency. That is, in the case of a 4-cylinder and 4-stroke engine, four explosions occur at two revolutions. Therefore, the engine frequency may be defined as the following equation:

Therefore, the transfer function of the notch filter for removing the engine noise may be defined as the following equation:

InFIG.9, notch frequency=engine frequency (920), and “mag” refers to a magnitude.

The final filtered wheel speed produced by applying the notch filter to the filtered wheel speed is defined as the following equation:
filtered wheel speed=weighted value×Band-Pass filtered wheel speed+(1−weighted value)×notch-filtered wheel speed  [Equation 11]
where the weighted value may be obtained by an experiment or be an arbitrary value set by the user in advance.

Of course, the Fast Fourier Transform (FFT) analysis and order analysis may be applied to the filtered wheel speed produced by application of the band-pass filter and the notch filter. Through the FFT analysis, it can be seen that the noise in the engine frequency region may intervene in the band-pass-filtered wheel speed. In addition, when the notch filter designed based on the engine frequency is applied to the wheel speed, it can be seen that the engine noise is removed therefrom.

FIG.10is a conceptual view for explaining the calculation of the tire resonance frequency illustrated inFIG.4. Referring toFIG.10, the vehicle wheel200travels on the ground1010by the rotational motion thereof. The rotational motion may be expressed by the following equation:

The wheel rotational motion equation may be expressed as a secondary transfer function indicated by the following equation:

Since it is aimed at only estimating the tire resonance frequency, the discretization and simplification of the above equation are performed using an auto-regressive model. This is expressed by the following equation:

Applying the above equation to a recursive least square (RLS) technique is as follows:

Meanwhile, the loss function V(θ) is defined as the following equation:

In addition, the LRS-based parameter estimation may be defined as the following equation:
{circumflex over (θ)}(t)={circumflex over (θ)}(t−1)+K(t)×{y(t)−ϕ(t)T×{circumflex over (θ)}(t−1)}  [Equation 17]
where K(t)=RLS gain.

Therefore, the estimation of the tire resonance frequency may be expressed by the following equation:

FIGS.11A to11Cillustrate an estimated frequency according to the tire pressure in some forms of the present disclosure.FIGS.11A to11Cillustrate a traveling speed and an estimated tire resonance frequency according to the tire pressures of 45 psi, 35 psi, and 25 psi. Here, “psi” refers to a pound per square inch.

In particular, as illustrated inFIGS.11A to11C, it can be seen that the variation of the estimated resonance frequency is large when the notch filter in some forms of the present disclosure is not used and when it is used.

FIG.12is a graph illustrating a state in which no engine noise is typically removed.FIG.12illustrates an estimated resonance frequency when the engine noise removal technique is not used.

FIG.13is a graph illustrating a state in which engine noise is removed in some forms of the present disclosure.FIG.13illustrates an estimated resonance frequency when the engine noise removal technique is used. Referring toFIG.13, it is possible to estimate the tire resonance frequency, which is robust to the variation of the traveling speed, by the engine noise removal technique in some forms of the present disclosure. In addition, it is possible to secure the low-pressure detection performance of the tire which is robust to the variation of the traveling speed.

In some forms of the present disclosure, it is possible to estimate the resonance frequency of the tire which is robust to the vehicle speed and/or the engine RPM.

In addition, it is possible to secure the low-pressure detection performance of the tire by estimating the tire resonance frequency of the tire even in the low-speed region.

The steps of the method or algorithm described in some forms of the disclosure may be implemented in the form of program commands executable by various computer means and recorded in a computer readable medium. The computer readable medium may include a program (command) cord, a data file, a data structure, or the like alone or in combination.

The program (command) cord recorded on the medium may be specially designed and configured for some forms of the present disclosure, or may be known to those skilled in the computer software for use. Examples of the computer readable medium may include magnetic media such as hard disks, floppy disks, or magnetic tapes, optical media such as CD-ROMs, DVDs, Blu-ray, and semiconductor memory devices, such as ROMs, RAMs, or flash memories, which are specifically configured to store and perform a program (command) cord.

Here, examples of the program (command) cord include a machine language code prepared by a compiler and a high-class language code executable by a computer using an interpreter, or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of some forms of the present disclosure, and vice versa.