Method and system for determining utilization of adhesion friction of a motor vehicle

Abstract

The invention relates to a method for determining the utilization of the adhesion friction of a motor vehicle. The method has the step of detecting (50) alternating sound pressure data within a tire cavity (16) of a wheel (10) of the motor vehicle. The method also has the step of determining (70) a current utilization of the adhesion friction depending on the alternating sound pressure data. The invention also relates to a system for determining the utilization of the adhesion friction of a motor vehicle.

Description

Technical Field

[0001] This invention relates to a method and system for determining the utilization rate of adhesion friction in a motor vehicle. Background Technology

[0002] Systems exist within motor vehicles that intervene in vehicle control. These systems aim to prevent uncontrolled driving conditions and / or transform them into controlled driving conditions. Such situations can occur, for example, when one or more tires of a motor vehicle transition from a state of static friction to a state of sliding friction. An example of such a system is the ESP (Electronic Stability Program) system. To control this system, the current coefficient of friction for the motor vehicle can be determined. This determination is based on sensor data, such as sensor data regarding the vehicle's corresponding wheel speeds and / or current acceleration vectors. The current coefficient of friction can then be estimated based on this. However, such determinations rely on many assumptions and can therefore be inaccurate. Thus, sufficiently accurate estimations can only be made, for example, in extreme areas or when the vehicle has entered a driving condition where sliding friction predominates over static friction.

[0003] The accuracy of the above estimation can be improved by classifying the intermediate layer between the ground or lane and the tire. For example, it can be determined optically or acoustically whether the lane is dry, wet, or covered with snow. However, this classification can only roughly correct the determination of the coefficient of friction and is still very costly.

[0004] DE102015217482A1, DE102015217474A1, and DE102015217472A1 each describe a vehicle tire with a tire module equipped with spikes. The spikes on the tire create unique rolling noise and vibrations at the tire's location, which are detected and analyzed using sensors within the tire module. These sensors could be, for example, specialized microphones or specialized accelerometers. The analyzed measurement data can be used to determine, for example, the state of the spikes in the tread or the friction value relative to the lane. However, these documents do not explain whether or how conclusions about the current traction friction utilization rate can be obtained. Determining the traction friction utilization rate would reveal how far the vehicle is from a risk of loss of control in a dynamic sense. Furthermore, these sensors must be discarded or recycled along with the tire when it is replaced, which is unsustainable.

[0005] EP1337404A1 describes a method and apparatus for determining the wear condition of a tire. For this purpose, the acoustic emission of the tire during rolling is recorded using a microphone.

[0006] DE10001272A1 describes an apparatus and method for obtaining forces in the tire of a wheel. For this purpose, rim deformation is detected.

[0007] DE19807004A1 describes a sensor system and method for monitoring the adhesion friction between a vehicle tire and its lane, as well as other physical data of the tire. It does not describe how to determine the current adhesion friction utilization rate. Summary of the Invention

[0008] The first aspect of the invention relates to a method for determining the adhesion friction utilization rate of a motor vehicle, for example, during driving. The motor vehicle may be configured as a passenger car, a truck, or a motorcycle. The motor vehicle, for example, has at least one wheel. The motor vehicle, for example, has a drive motor. The adhesion friction utilization rate can be a ratio of the current friction force of the lane to the maximum possible friction force. The adhesion friction utilization rate can have a one-dimensional value or can also have multiple characteristic values. The adhesion friction utilization rate can, for example, have values ​​in the longitudinal direction, the lateral direction, and / or the rotational direction about the height axis of the motor vehicle or wheel. The maximum adhesion friction utilization rate can be defined by the maximum adhesion friction potential. The maximum adhesion friction utilization rate can be the maximum friction force between the lane and the motor vehicle, the individual wheels, and / or all wheels. The maximum adhesion friction utilization rate can also have only one friction characteristic value or can have multiple friction characteristic values, for example, for the longitudinal and lateral directions of the motor vehicle and / or the wheels. For example, the maximum adhesion friction utilization rate can be achieved at a certain slip ratio. For example, the maximum adhesion friction utilization rate can be achieved under the following driving conditions: where one or more wheels of the motor vehicle are in a state of transition from static friction to sliding friction.

[0009] The method includes the step of detecting alternating sound pressure data within the tire cavity of the vehicle's wheels. This detection can be performed using sensing. The alternating sound pressure data may include one or more detected alternating sound pressures. The alternating sound pressure data can be derived, for example, from a pressure curve in the tire cavity and / or from the deviation from the static pressure in the tire cavity. The detection can be performed using a detection device, such as a sound pressure sensor. The sound pressure sensor can be configured as a microphone, particularly a MEMS microphone. The alternating sound pressure can correspond to pressure fluctuations in the air within the wheel, which is typically many orders of magnitude smaller than the static pressure in the wheel.

[0010] A wheel may have, for example, a rim and a tire. The tire may be fastened to the rim. The substantially airtight space between the rim and the tire may be the tire cavity. The sound pressure sensor may, for example, be fastened to the rim. Thus, the imbalance caused by the sound pressure sensor can be small and can be easily compensated for. The rim may, for example, be made of steel, aluminum, magnesium, and / or carbon fiber. The tire may have, for example, rubber as its main component. The motor vehicle may, for example, use the wheel to roll on a road.

[0011] By detecting alternating sound pressure levels within the tire cavity, many interfering variables can be avoided. For example, wind or ambient sound sources (such as other vehicles) have little or no impact on the alternating sound pressure data. This also protects the sensors from environmental influences and eliminates the need for additional construction space. A high signal-to-noise ratio is achieved. The alternating sound pressure data can contain information about airborne and structural noise. For example, oscillations (such as vibrations) within the tire and / or rim can also cause alternating sound pressure levels. This allows for the consideration of additional information regarding the utilization rate of the adhesion friction.

[0012] The method includes a step of determining the current adhesion friction utilization rate based on the alternating sound pressure data. The adhesion friction utilization rate can be determined in a one-dimensional or multi-dimensional manner, for example, separately for the longitudinal and lateral directions of the vehicle and / or wheels. The adhesion friction utilization rate can also be determined as a vector, extending, for example, in a plane extended by the vehicle's longitudinal and / or lateral directions. This vector can also be horizontal when the lane is level. There is typically a force between the tire and the lane caused by shear stress at the tire support surface. Here, combined slip conditions are normally present. For processing, this force can be divided into longitudinal and lateral vectors. Correspondingly, the adhesion friction and the adhesion friction utilization rate can exist, for example, as a vector or as two values ​​allocated for the lateral and longitudinal directions. By determining the adhesion friction utilization rate, it can be determined which driving dynamic margins are still available. Preventative vehicle control intervention can then be implemented. Thus, it is possible to reliably prevent the autonomous driving system from controlling the vehicle into the adhesion limit range. The current adhesion friction utilization rate can take into account the possible friction forces with the lane at present. Therefore, there is no need to classify intermediate layers. Instead, the detected alternating sound pressure data allows for the direct deduction of the possible friction between the tire and the lane, because the sound pressure variation in the tire cavity differs depending on the lane and the presence of an intermediate layer under otherwise identical driving conditions. The alternating sound pressure data can also account for tire wear and / or wheel imbalance, as the sound pressure variation in the tire cavity differs depending on tire wear and / or wheel imbalance under otherwise identical driving conditions and lane conditions.

[0013] The current adhesion friction utilization rate can be determined, for example, by means of an analysis device. The analysis device can be, for example, an onboard computer connected to the detection device. The adhesion friction utilization rate can be determined continuously, for example, at a high frequency. The analysis device can also be an integrated part of the sound pressure sensor and / or disposed within the tire cavity. The analysis device can also be, for example, fixed to the rim. The adhesion friction utilization rate can also be determined, for example, under normal driving conditions (such as straight-line driving) and is sufficiently accurate therein. For example, the adhesion friction utilization rate can be determined using half the measurement frequency of the corresponding sound pressure sensor. For example, the sound pressure can be determined using any detection frequency in the range of at least 0.5 kHz, 1 kHz, 5 kHz, 10 kHz, 20 kHz or greater up to 40 kHz, 50 kHz, 60 kHz, 80 kHz, 100 kHz or greater. Correspondingly, alternating sound pressure can be detected at frequencies within the detection range. Therefore, the detection frequency can be higher than that used by a normal tire pressure sensor to detect pressure loss due to leakage. In a standard tire pressure sensor, the pressure in the tire cavity is compared to the rated pressure, for example, every 30 seconds. The acoustic pressure sensor can have high sensitivity because, for example, it needs to detect small pressure fluctuations. The acoustic pressure sensor can, for example, be calibrated to the wheel's rated pressure.

[0014] The current adhesion friction of the wheel can be determined, for example, based on the alternating sound pressure data. The adhesion friction can be determined in one-dimensional or multi-dimensional terms, for example, separately for the longitudinal and lateral directions of the vehicle and / or wheel. The current adhesion friction can correspond to the circumferential forces transmitted to the wheel. The current adhesion friction coefficient can also be determined, derived from the normal force acting on the wheel. Additionally, the current adhesion friction potential of the wheel can be determined based on the alternating sound pressure data. The adhesion friction potential can be determined in one-dimensional or multi-dimensional terms, for example, separately for the longitudinal and lateral directions of the vehicle and / or wheel. The current adhesion friction potential can, for example, be the maximum possible adhesion friction that can be generated by the friction between the tire and the lane and the normal force at the wheel. The current adhesion friction potential can be influenced by the state of the tire, the lane, the intermediate layer between the tire and the lane, and the current driving dynamics. The current utilization rate of adhesion friction can be determined by the current adhesion friction and the current adhesion friction potential, for example, by division.

[0015] Overall, the determination of the adhesion friction utilization rate can be of high quality because only a small number of chained estimations are used, as conclusions about adhesion friction and adhesion friction potential can be directly derived from alternating sound pressure data. By using an alternating sound pressure sensor capable of measuring alternating sound pressure, an estimation of the power quantity that can be physically directly inferred from the adhesion friction coefficient can be achieved. This avoids the need for chaining various estimation algorithms. Furthermore, the air column in the tire cavity depends on the lane surface being excited to different high-frequency oscillations, which can be detected by the alternating sound pressure sensor. Therefore, the signal contains information about the lane surface, which can be incorporated into the estimation of the adhesion friction potential. The adhesion friction potential can also be estimated during non-critical driving operations (such as constant-speed straight-line driving) because the quality of the input data is independent of the utilization rate of the adhesion friction potential. Additionally, the effect of unevenness or tire rolling on alternating sound pressure is smaller than the acceleration of the wheel or tire, allowing the measurement range of the corresponding alternating sound pressure sensor to be selected to be significantly smaller compared to an acceleration sensor. Therefore, the sensor can be designed to be significantly more sensitive and better able to distinguish smaller events or effects.

[0016] The method may include a step of generating a control signal. This control signal can control vehicle functions, such as a driver assistance system or autonomous driving control. The driver assistance system may, for example, change its intervention behavior based on the control signal or the current adhesion friction utilization rate. The method can be configured to operate the driver assistance system and / or autonomous driving control. Similarly, a specific current adhesion friction utilization rate may be output, for example, to the driver via a display.

[0017] In one embodiment of the method, it is proposed to detect at least one first alternating sound pressure within the tire cavity of a first wheel of the motor vehicle and at least one second alternating sound pressure within the tire cavity of a second wheel of the motor vehicle. That is, the alternating sound pressure data may, for example, include the pressures detected from two different wheels. The alternating sound pressure data may have the corresponding detected alternating sound pressure or may be generated based on at least the alternating sound pressure. This allows information from multiple or all wheels of the motor vehicle to be considered when determining the adhesion friction utilization rate. For example, the relevant alternating sound pressure can be detected at each wheel of the motor vehicle.

[0018] The current traction friction utilization rate can be determined individually for each wheel. For example, a first current traction friction utilization rate can be determined for the first wheel and a second current traction friction utilization rate can be determined for the second wheel. Thus, for example, it is possible to know separately how far each of these wheels is from losing traction. This allows for better control to avoid loss of traction. For example, steering and / or braking or acceleration can be targeted at individual wheels to avoid loss of traction. Driving operations can also be adapted, for example, to transmit less force through certain wheels that are already close to their traction limits. For this purpose, for example, the current traction friction force and / or the current traction friction potential of each wheel can be determined individually, for example, depending on the detected associated alternating sound pressure. For example, the first current traction friction force and / or the first current traction friction potential can be determined for the first wheel based on a first alternating sound pressure. For example, the second current traction friction force and / or the second current traction friction potential can be determined for the second wheel based on a second alternating sound pressure.

[0019] Alternatively or additionally, the current adhesion friction utilization rate can be determined for the entire motor vehicle, for example, depending on the first and second alternating sound pressure levels. For this purpose, sensor data fusion can be performed, for example. However, it is also possible to first determine the current adhesion friction and / or the current adhesion friction potential for each wheel individually and then determine the current adhesion friction utilization rate for the entire motor vehicle based on these values. Alternatively, the total current adhesion friction and / or the total current adhesion friction potential can be determined, for example, depending on all detected alternating sound pressure levels. Determining the overall adhesion friction utilization rate for the motor vehicle can require less computational power and can be very accurate.

[0020] The following explanation primarily uses only one wheel and the detected alternating sound pressure. Corresponding implementations also apply to implementations where, if available, alternating sound pressure is detected for two or more tires and / or where the total current adhesion friction utilization rate and / or the current adhesion friction utilization rate for each individual wheel is determined.

[0021] In one embodiment of the method, it is proposed that the method further includes a step of determining the current adhesion friction force based on the alternating sound pressure data. This determination can be performed on a single wheel or on the entire vehicle. The current adhesion friction force can be determined analytically based on the alternating sound pressure data. The calculation can, for example, be based on an empirically obtained relationship between the alternating sound pressure in the wheel and the current adhesion friction force. Because no assumptions about the driving conditions need to be made, the determination can be simple and very accurate. Such an accuracy can, for example, be 97%. During the analytical determination, additional data besides the alternating sound pressure data, such as the temperature of the air in the tire cavity and / or the static air pressure in the tire cavity, can also be considered. The current adhesion friction force utilization rate can then be determined based on the determined current adhesion friction force. Alternatively, the current adhesion friction force can be determined, for example, by a neural network. Thus, it does not need to be obtained empirically. The neural network can, for example, be trained with simulated or experimental data.

[0022] A neural network can be, for example, a mathematical model. The neural network can be constructed using a computer and / or can be configured as an artificial neural network. The neural network can have input nodes, output nodes, and a plurality of intermediate nodes arranged between the input nodes and the output nodes. The corresponding connections between the nodes can, for example, have weights. The input node can, for example, be configured as a data interface through which input data can be input into the neural network. The output node can, for example, be configured as a data interface through which output data can be output from the neural network. The input nodes can be connected to the intermediate nodes, and the intermediate nodes can be connected to each other. The intermediate nodes can be connected to the output node. The neural network can, for example, be configured as a convolutional neural network.

[0023] In one embodiment of the method, it is proposed that the method further includes a step of determining the current adhesion friction potential based on the alternating sound pressure data. This determination can be performed on a single wheel or on the entire vehicle. The current adhesion friction potential can be determined using a neural network based on input data. The input data for the neural network can include at least the alternating sound pressure data. With sufficient training, the neural network can also simply consider intermediate layers and / or different lanes without requiring special identification and / or classification, as the alternating sound pressure data contains information about them. Furthermore, the costly analytical determination of such relationships can be omitted. For example, the adhesion friction potential can be obtained experimentally on a test bench, thus providing corresponding training data for the neural network. It has been shown that the current adhesion friction potential can be obtained using a neural network based on highly accurate alternating sound pressure data, for example, without using additional data about lanes and / or possible intermediate layers. During the determination, the input data can also consider additional data besides the alternating sound pressure data, such as the temperature of the air in the tire cavity and / or the static air pressure in the tire cavity. Alternatively, the current adhesion friction potential can be determined analytically.

[0024] The neural network can also be configured to determine the current adhesion friction potential and the current adhesion friction force, and output these as output data. This eliminates the need for separate determination processes. Alternatively, the neural network can be configured to directly determine the adhesion friction potential without previously determining the current adhesion friction force and the current adhesion friction potential. Firstly, determining the current adhesion friction potential and the current adhesion friction force allows for a particularly accurate determination of the adhesion friction utilization rate. It has been shown that analytically determining the current adhesion friction force and using artificial intelligence to determine the adhesion friction potential can achieve a particularly accurate determination of the adhesion friction utilization rate and can be implemented very simply. Artificial intelligence can be implemented here through the neural network. The neural network can, for example, output the current adhesion friction potential and / or the current adhesion friction utilization rate as output data, respectively for the entire vehicle and / or also for each wheel individually, wherein alternating sound pressure in the tire cavity is detected.

[0025] In one embodiment of the method, it is proposed that the method includes a step of filtering the alternating sound pressure data. For example, the data can be filtered based on the frequency and / or amplitude of the corresponding alternating sound pressure. This filtering can be performed before determining the current adhesion friction utilization rate, the current adhesion friction, and / or the current adhesion friction potential. The filtering can also be performed before transmitting the data to the analysis device and / or before the data reduction step. The filtering can remove interfering signals and / or extract useful signals. For example, the filtering can be performed using a bandpass filter, a high-pass filter, or a low-pass filter. The filtering can improve the accuracy of the determination and / or reduce the amount of data. The filter cutoff frequency can be adapted to the expected bandwidth of the useful signal. The filtering can be performed, for example, by a data preprocessing device. The data preprocessing device can be located at the wheel, for example, at the rim along with the associated sound pressure sensor. The data preprocessing device can have a microprocessor. The data preprocessing device can also be an integrated part of the sound pressure sensor.

[0026] In one embodiment of the method, it is proposed that the method includes the step of transmitting the alternating sound pressure data to an analysis device. The data transmission can be wired or wireless, for example. Data transmission can be performed wirelessly via the IEEE-802.11 standard, Bluetooth, or wired via a sliding ring in the wheel hub. The alternating sound pressure data may have been filtered, reduced in magnitude, and / or transformed before transmission. Through data transmission, computationally intensive analysis can be performed centrally in the vehicle to determine the current adhesion friction utilization rate, for example, also for multiple wheels and / or when considering the alternating sound pressure data of multiple wheels.

[0027] The method includes a step of reducing the alternating sound pressure data, for example, before transmitting the alternating sound pressure data to the analysis device. Data reduction reduces the amount of data. Data reduction can compress and / or transform the alternating sound pressure data. This allows for faster data transmission. Furthermore, it simplifies analysis, especially through neural networks. Data reduction can include feature extraction. This extracts only data relevant to the determination process (such as patterns) from the detected data. For example, data reduction from the field of speech recognition can be used, such as determining Mel-frequency cepstral coefficients (MFCCs). These coefficients are then transmitted only as alternating sound pressure data. Data reduction can be performed, for example, by transformations such as wavelet transforms or fast Fourier transforms, transformations to the graph plane, and / or transformations to the time-frequency plane. Subsequent data conversion can also be performed, for example, converting to a format that can be processed by the analysis device.

[0028] In one possible form of the method, Mel frequency cepstral coefficients (MFCCs) are determined based on the detected alternating sound pressure levels to generate data-reduced alternating sound pressure level data. This method is mature in the field of speech recognition and has shown that it can also be very suitable for determining the adhesion friction of vehicle tires acoustically. In the first step, the useful signal data is subdivided into time segments, each of which is multiplied by a window function. Overlap is achieved between the time windows. The windowed data is then transformed to the frequency domain using a Discrete Fourier Transform (DFT), followed by logarithmic transformation and multiplication with a triangular filter whose center frequency is chosen so that it maps the spectrum onto a Mel scale. Each triangular filter integrates the obtained amplitude, thereby producing the scale value for each Mel filter. Since the number of triangular filters is significantly smaller than the frequency sampling points obtained by the DFT, the amount of data is significantly reduced in this way. For example, 257 frequency sampling points are generated within a time window width of 512 pivots. These frequency sampling points can be reduced to 13 single-digit values ​​when typically 13 MFCCs are selected. In the next step, the data is transformed for transmission to the analysis apparatus. The data stream may include timestamps in addition to the actual data vectors with MFCCs at each time step, allowing the data to be temporally ordered for further processing.

[0029] In one embodiment of the method, it is proposed that the method additionally includes a step of detecting the vehicle state. The determination of the current adhesion friction utilization rate may additionally depend on the vehicle state. The vehicle state may, for example, be used to determine the current adhesion friction and / or the current adhesion friction potential. The vehicle state may be detected, for example, sensorily or by input. The vehicle state may include, for example, external temperature, vehicle location, or information about vehicle motion such as acceleration, vehicle speed, direction of travel, and / or the second derivative of vehicle speed. This vehicle motion information may exist for individual wheels or for the entire wheel. For this purpose, corresponding sensors, such as 9D inertial measurement units, may be provided. The vehicle state may also include wheel states. The vehicle state may also include, for example, information about the type of tire currently installed and information about the usage of that tire. The wheel states may, for example, be sensorily or by input. The wheel states may include, for example, inflation pressure, air temperature in the wheel, and / or wheel speed. For each wheel state, an associated wheel state may be detected.

[0030] The second aspect relates to a system for determining the utilization rate of adhesion friction of a motor vehicle. The system can be configured to implement the method according to the first aspect. Corresponding advantages and other features can be understood from the description of the first aspect, wherein the design of the first aspect also forms the design of the second aspect, and vice versa.

[0031] The system includes a detection device with at least one sound pressure sensor. The detection device can be configured to generate alternating sound pressure data for each wheel, with the sound pressure sensor disposed within the tire cavity of each wheel. This generation can depend on the detected alternating sound pressure. The sound pressure sensor can be secured within the tire cavity to the rim of the vehicle's wheel. Alternatively, the sound pressure sensor can also be secured to the tire. The detection device can have associated sound pressure sensors for two or more wheels, disposed within the tire cavity of the respective wheel. The sound pressure sensor can be configured to detect alternating sound pressure data within the tire cavity. The sound pressure sensor can, for example, be configured as a microphone. The frequency range and / or pressure range of the data detected by the sound pressure sensor can be adapted to a useful signal range. The useful signal range can be particularly well correlated with the adhesion friction of the tire and the lane and / or the adhesion friction potential of the friction pair formed by the tire and the lane.

[0032] The system may, for example, have an energy source, which is also secured to the rim and supplies energy to the sound pressure sensor. Power can also be supplied from outside the tire. Power can also be supplied, for example, via a sliding contact or induction.

[0033] The energy source can be configured as a power generation device. The power generation device can be configured to generate energy to supply energy at least to the acoustic pressure sensor. The power generation device can also be configured to power the data preprocessing unit, the entire detection unit, and / or the analysis unit. The power generation device can be configured to extract energy from wheel motion. For example, the power generation device can extract energy from the deformation and / or motion of the wheel or a portion thereof. The power generation device can, for example, have a piezoelectric element fixed in or on the wheel, such as in or on the tire or rim, and extract current for powering a part of the system through deformation and / or through vibration acting on the piezoelectric element. For example, a permanent magnet can be arranged at a fixed vehicle component near the wheel, inducing a current in a coil arranged within the rotating wheel. The power generation device can, for example, be configured to supply energy to at least the acoustic pressure sensor and / or other parts of the system, independent of the onboard power supply of the rest of the vehicle.

[0034] Compared to alternating sound pressure sensors on the vehicle side, alternating sound pressure sensors located within the tire cavity provide isolation from external tire noise. Interference noise is thus significantly reduced in volume. Furthermore, the alternating sound pressure sensor within the tire cavity is not surrounded by moving air, meaning there is no wind noise. These environmental factors result in a very high signal-to-noise ratio. The resulting high level of useful signal simplifies data preprocessing for separating useful signals from interference signals.

[0035] The system includes an analysis device configured to determine the current adhesion friction utilization rate based on the alternating sound pressure data. For example, the analysis device may be configured to analytically calculate the current adhesion friction for each wheel to which alternating sound pressure data has been detected. Alternatively or additionally, the analysis device may be configured to determine the current adhesion friction potential for each wheel to which alternating sound pressure data has been detected using a neural network.

[0036] In one embodiment of the system, it is proposed that the system has at least one data preprocessing device. The system may have one associated data preprocessing device for each sound pressure sensor. The data preprocessing device may be arranged together with the sound pressure sensor in the tire cavity and secured to the rim. Each sound pressure sensor and each data preprocessing device may, for example, be formed as a common component. The data preprocessing device may be powered together with the associated sound pressure sensor.

[0037] The data preprocessing device can be configured to filter the alternating sound pressure data. Alternatively or additionally, the data preprocessing device can be configured to perform data reduction on the alternating sound pressure data, for example, through feature extraction. Feature extraction can be the extraction of features. This data reduction can be performed before the data is transmitted to the analysis device. The data preprocessing device can be configured for such transmission. Data transmission can be, for example, wireless or wired. The data preprocessing device can have a microprocessor. Attached Figure Description

[0038] Figure 1 This illustration schematically demonstrates a system for determining the utilization rate of adhesion friction in motor vehicles.

[0039] Figure 2 This illustration demonstrates a method for determining the utilization rate of adhesion friction in motor vehicles. Detailed Implementation

[0040] Figure 1A system for determining the utilization rate of adhesion friction in a motor vehicle is schematically illustrated. For this purpose, a wheel 10 of the motor vehicle is shown in cross-sectional view in part. The wheel 10 has a tire 12 and a rim 14. The tire 12 is fastened to the rim 14 such that they together define an air-filled tire cavity 16. An acoustic pressure sensor 18 and an associated data preprocessing device 20 are arranged in the tire cavity. The acoustic pressure sensor 18 is configured as a microelectromechanical system (MEMS). The data preprocessing device 20 and the acoustic pressure sensor 18 are fastened to the rim 14 so that the tire 12 can be replaced without replacing these components. A power supply device 22 is also shown, configured to power the acoustic pressure sensor 18 and the data preprocessing device 20. In the example shown, the power supply device 22 has a battery. In other embodiments, the power supply device 20 is configured to connect to an onboard electrical network, for example, via a sliding contact. The power supply device 20 can also be configured to generate energy when the wheel 10 rotates, for example, by inducing a voltage in a coil within a magnet fixed to the vehicle. In the example shown, the power supply device 22 is arranged at the rim 14 outside the tire cavity 16. The power supply device 22 can also be arranged in the tire cavity 16 in a partially or fully protected manner.

[0041] The data preprocessing unit 20 is configured to transmit data wirelessly or wiredly (e.g., via a sliding contact) to the analysis unit 24 of the system. The analysis unit 24 is configured as an on-board computer of the motor vehicle and is, for example, securely mounted to the vehicle body. The system may have pairs of additional sound pressure sensors 18 and data preprocessing units 20, which are arranged at respective associated additional wheels 10 of the motor vehicle. Each of these pairs then has an associated power supply device 22. The data that has been detected and preprocessed there is also transmitted to the analysis unit 24 and analyzed together there.

[0042] Figure 2 A method for determining the utilization rate of adhesion friction in a motor vehicle is demonstrated, which is based on... Figure 1The system is executed as follows: In the first step 50, alternating sound pressure data is detected within each tire cavity of each wheel 10 of the motor vehicle, the tire cavity being equipped with a sound pressure sensor 18. For example, these raw data are detected at a frequency of at least 1 kHz and these raw data have pressure fluctuations of, for example, up to 10 Pa. The relevant frequencies for determining the utilization rate of the adhesion friction are, for example, between 0.5 kHz and 10 kHz or 20 kHz. The sound pressure sensor 18 outputs a voltage as a measurement signal, which is first converted into alternating sound pressure by the data preprocessing unit 20 using the stored sensitivity curve and calibration curve of the sound pressure sensor 18. Here, a first useful signal extraction has also been performed by filtering using a bandpass filter, which only allows a certain frequency range and / or a certain amplitude range to pass through. These alternating sound pressures are filtered and generated in step 52. In another step 54, the filtered alternating sound pressure data is reduced by the data preprocessing unit 20. Here, feature extraction is performed using Mel-frequency cepstral coefficients (MFCC). This reduces the amount of data to be transmitted to the analysis device 24 and the computing power required for analysis there. In step 56, the amount of alternating sound pressure data reduced by data reduction is converted by the data preprocessing device 20 for transmission to the analysis device 24. During this conversion, timestamps are associated with the actual data vectors with MFCC for each time period. These data are then transmitted to the analysis device 24, in the present case, for example, via Bluetooth or WiFi. In other embodiments, these steps 52 through 56 may be omitted and / or performed by the analysis device 24 only after the corresponding data has been transmitted to the analysis device 24.

[0043] The received data is then analyzed by analysis device 24 in two paths. In the first path, the received data is provided in step 58 to a neural network implemented in analysis device 24 to determine the current adhesion friction potential based on alternating sound pressure data. Optionally, the input data may also include detected data about the vehicle's state. The neural network is pre-trained with training data to correlate the pre-processed input data with the current adhesion friction potential, such as data obtained experimentally on a test bench for various pairs of tires and lanes (optionally with an intermediate layer). In one embodiment, the neural network obtains the adhesion friction potential individually for each wheel 10. Alternatively or additionally, the adhesion friction potential is determined for the entire vehicle.

[0044] Neural networks can, for example, output classification vectors with various categories of adhesion friction coefficients and associated probabilities as output data. Alternatively, they can output only the adhesion friction coefficients, such as the estimated adhesion friction potential. They can also output adhesion friction coefficients for the longitudinal direction of the vehicle and adhesion friction coefficients for the lateral direction of the vehicle.

[0045] Optionally, the system is configured to estimate the adhesion friction potential based on other sensor values ​​and / or other methods. For example, the adhesion friction potential can also be estimated using wheel speed and detected vehicle motion. Here, the adhesion friction potential can be based, for example, on the obtained wheel slip. Wheel slip can, for example, reliably determine the adhesion friction potential at least in the limiting region of adhesion friction utilization. In optional step 60, the determined individual adhesion friction potentials are fused by analysis device 24, for example, by averaging the classification vectors.

[0046] In the second path, the data received by the analysis device 24 is used to determine the current adhesion friction force through analysis and calculation, depending on the alternating sound pressure data. For this purpose, in step 62, the analysis device 24 first performs a power calculation for all wheels together or individually. Here, rolling power and thermal power are obtained from the tire tracks. The conversion from Mel coefficient to power is based on empirically derived relationships that show high variable correlation. In another step 64, the analysis device 24 calculates the current adhesion friction force coefficient based on the calculated power variables. Multiple adhesion friction forces and / or adhesion friction force coefficients can also be determined for each wheel 10, for example, for the longitudinal and lateral directions of the vehicle, respectively. In the case of sound pressure sensors 18 in multiple wheels 10, these adhesion friction force coefficients can be further processed individually for each wheel or, optionally, fused in step 66, for example, by averaging.

[0047] In step 70, the current adhesion friction utilization rate is determined by the analysis device 24, depending on the current adhesion friction potential determined by the neural network and the current adhesion friction determined by the analytical formula, and thus depending on the alternating sound pressure data. Here, the current adhesion friction utilization rate can be determined individually for each wheel 10 and / or for the entire vehicle. Therefore, the margins for longitudinal and lateral guiding forces can be obtained, and a more accurate estimation of the vehicle's condition can be achieved, as well as the derivation of possible interventions through the driving dynamics adjustment system.

[0048] List of reference numerals 10 wheels 12 tires 14-inch rims 16 tire cavities 18 sound pressure sensors 20 Data Preprocessing Units 22 power supply units 24 Analytical apparatus 50 Steps / Detecting Alternating Sound Pressure Data 52 Steps / Filtering the alternating sound pressure data 54 Steps / Data Reduction of Alternating Sound Pressure Data 56 Steps / Convert Alternating Sound Pressure Data 58 Steps / Determine the current adhesion friction potential 60 steps / Integrate the determined adhesion friction potential with adhesion friction potential determined in other ways 62 Steps / Power Calculation of Wheels 64 Steps / Determine the current adhesion friction force 66 Steps / Integrate the determined adhesion friction forces 70 steps / Determine the current utilization rate of adhesion friction

Claims

1. A method for determining the utilization rate of adhesion friction of a motor vehicle, wherein the method comprises at least the following steps: - Detect (50) the alternating sound pressure data within the tire cavity (16) of the wheel (10) of the motor vehicle; - The current adhesion friction utilization rate is determined based on the alternating sound pressure data (70).

2. The method according to claim 1, wherein at least one first alternating sound pressure within the tire cavity (16) of the first wheel (10) of the motor vehicle and at least one second alternating sound pressure within the tire cavity (16) of the second wheel (10) of the motor vehicle are detected. The current adhesion friction utilization rate is determined individually for each wheel; or The current adhesion friction utilization rate is determined for the motor vehicle as a whole.

3. The method according to claim 1 or 2, wherein the method has the step (62, 64) of determining the current adhesion friction force based on the alternating sound pressure data, wherein the current adhesion friction force is determined based on the analysis formula of the alternating sound pressure data, and wherein the current adhesion friction force utilization rate is determined based on the determined current adhesion friction force.

4. The method according to any one of the preceding claims, wherein the method has a step (58) of determining the current adhesion friction potential based on the alternating sound pressure data, wherein the current adhesion friction potential is determined by means of a neural network based on input data, wherein the input data for the neural network includes at least the alternating sound pressure data.

5. The method according to any one of the preceding claims, wherein the method comprises the step of filtering the alternating sound pressure data (52).

6. The method according to any one of the preceding claims, wherein the method has the step of transmitting the alternating sound pressure data to the analysis device (24), wherein the method has the step of reducing the alternating sound pressure data (54) before transmitting the alternating sound pressure data to the analysis device (24).

7. The method according to any one of the preceding claims, wherein the method additionally comprises a step of detecting the vehicle state. The determination of (70) the current adhesion friction utilization rate depends additionally on the vehicle condition.

8. The method according to claim 7, wherein the vehicle status may include at least one of the following: - Information about vehicle movement; - Information about the external temperature; - Information about the vehicle's location; and - Information about wheel condition, such as currently installed tires, tire usage, tire inflation pressure, and / or wheel speed.

9. A system for determining the adhesion friction utilization rate of a motor vehicle, wherein the system has a detection device having at least one sound pressure sensor (18), wherein the sound pressure sensor is fastened within a tire cavity (16) at a rim (14) of a wheel (10) of the motor vehicle and wherein the sound pressure sensor (18) is configured to detect alternating sound pressure data within the tire cavity (16), wherein the system has an analysis device (24) configured to determine the current adhesion friction utilization rate depending on the alternating sound pressure data.

10. The system according to claim 9, wherein the system has at least one data preprocessing device (20) configured to filter the alternating sound pressure data; And / or reduce the alternating sound pressure data before transmitting the alternating sound pressure data to the analysis device (24).

11. The system according to claim 9 or 10, wherein the system has at least one power supply device (20) configured as a power generation device, the power supply device being configured to supply energy to at least the acoustic pressure sensor.

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