Determining clutch temperature of vehicle clutch by means of neural network
Predicting the vehicle clutch temperature through neural networks solves the complex and time-consuming problem of traditional models, achieving faster and more accurate temperature prediction, and improving vehicle safety.
Patent Information
- Application Number
- CN202380043924.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2022-06-02
- Filing Date
- 2023-05-25
- Publication Date
- 2025-01-07
AI Technical Summary
Existing rules-based mathematical models are used to determine that the temperature of a vehicle clutch is complex and time-consuming, making it difficult to accurately predict clutch temperature changes, affecting vehicle safety.
Using neural networks, especially multi-layer perceptrons (MLPs) or convolutional neural networks (CNNs), learn and predict clutch temperature by inputting the power and operating parameters of the vehicle clutch, replacing traditional models, reducing computational complexity and time.
It achieves faster and more accurate clutch temperature prediction, takes into account the time trend of temperature changes, reduces computing and storage requirements, and improves vehicle safety.
Smart Images
Figure CN119278323A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a method for determining a clutch temperature of a vehicle clutch by means of a neural network. The invention also relates to a method for generating a training data set for a neural network, which is designed to determine the clutch temperature of a vehicle clutch. The invention also relates to a corresponding control device for determining the clutch temperature of a vehicle clutch using a neural network. Background Art
[0002] The temperature of the vehicle clutch can be calculated using conventional rule-based mathematical models. The calculation can take place in the transmission control unit. Machine learning, such as neural networks, can be used for this purpose. To date, nonlinear and complex neural networks, such as so-called convolutional neural networks, have been used to determine the clutch temperature. Summary of the invention
[0003] In a first aspect, the invention relates to a method for determining a clutch temperature of a vehicle clutch by means of a neural network.
[0004] The vehicle clutch can be installed in a vehicle driven by an engine, such as a motor vehicle, a motorcycle or a two-wheeled vehicle driven at least partially electrically. The vehicle clutch can be used to transmit the driving force applied by the engine of the vehicle to the driving axle of the vehicle. The vehicle clutch can have at least two different switching states or gears, which can be defined by a predetermined ratio between the driven torque of the engine and the driving torque of the driving axle.
[0005] The clutch temperature can be understood as the temperature of at least one clutch element, such as the temperature of a clutch plate. Alternatively, the clutch temperature can also be understood as the temperature of the entire vehicle clutch, which can be based on, for example, determining an average value of the individual temperatures of the clutch elements. Due to the operation of the vehicle, the temperature of the vehicle clutch changes. For example, the temperature of the vehicle clutch will increase during a switching process or gear shift. If the temperature of the vehicle clutch increases above a critical temperature value, this may cause damage to or failure of the vehicle clutch. Therefore, determining the clutch temperature of the vehicle clutch contributes to the safety of the vehicle.
[0006] A neural network can be understood as a mathematical model that at least partially mimics the structure of neurons in the human brain. A neural network can be created with the aid of a computer. A neural network can have an input node, an output node, and a plurality of intermediate nodes, the intermediate nodes being arranged between the input node and the output node. The input node can be, for example, a data interface, via which, for example, input data can be input into the neural network. The output node can be, for example, a data interface, via which data can be output from the neural network. The input node can be connected to the intermediate node, and the intermediate nodes can also be connected to each other. The intermediate node can be connected to the output node. The input data can be historical data detected at a certain point in time. Alternatively or additionally, the input data can be synthetic data generated by processing the detected or measured data. Similarly, the output data can also be historical data or synthetic data.
[0007] Information can be buffered at least temporarily at the intermediate nodes. It can be provided that at least one arithmetic operation is performed at the intermediate nodes. Input data can be transmitted from the input node to the output node via the intermediate node. During the transmission, the input data can be mathematically processed, for example, converted into output data. The intermediate nodes of the neural network can be arranged in one or more layers or planes. The intermediate nodes can be connected to each other within a layer. In addition, the intermediate nodes of a layer can be connected to the intermediate nodes of other layers. The individual connections of the input nodes, the intermediate nodes and the output nodes can be provided with mathematical weights. Depending on the purpose of the neural network, the individual weights of the connections can be different. The weights can be changed during the training of the neural network. By adjusting the mathematical weights of the connections of the individual nodes during the training, the neural network can learn the relationship between the input data and the output data. During the use of the neural network for the set purpose, the neural network can apply the learned relationship to the input data so as to generate output data according to the predetermined purpose of the neural network.
[0008] For determining the clutch temperature, for example, a multilayer perceptron (MLP) can be used as a neural network. The multilayer perceptron has at least one layer of intermediate nodes and uses at least one nonlinear mathematical function to calculate the output data. Another exemplary neural network for determining the clutch temperature can be a fully connected layer (FCL) network. In such a neural network, all input nodes, intermediate nodes and output nodes are connected to one another. Furthermore, for determining the clutch temperature, a convolutional neural network (CNN) can also be used, in which the intermediate nodes of different layers are connected to one another at least partially in the form of a mathematical convolution function.
[0009] The method comprises the step of determining at least one input value, which represents the power supplied to the vehicle clutch. The at least one input value is determined based on the processing of temporally consecutive values of the power supplied to the vehicle clutch. The power supplied to the vehicle clutch can be understood as physical power, i.e. energy supplied to the vehicle clutch within a predetermined time period. For example, the power supplied to the vehicle clutch can be the switching power supplied to the vehicle clutch during a switching process or gear shift. The power supplied to the vehicle clutch can represent a change in the clutch temperature of the vehicle clutch.
[0010] The power supplied to the vehicle clutch can be detected at certain time intervals. The individual values of the power supplied to the vehicle clutch can be arranged, for example, in ascending order according to their detection time points. These temporally successive values of the power supplied to the vehicle clutch can be mathematically processed, for example by means of a predetermined arithmetic operation (such as averaging).
[0011] Furthermore, the method comprises the step of inputting at least one input value as input data into the neural network. The at least one input value can be transmitted to at least one input node of the neural network, for example, by means of an input device. Alternatively, the at least one input value can be transmitted from a control device of the vehicle to at least one of the input nodes of the neural network via a data interface. The at least one input value can be used as input data for determining the clutch temperature using the neural network. Alternatively or additionally, the at least one input value can be used as input data for training the neural network.
[0012] Furthermore, the method also comprises the step of inputting at least one value of at least one operating parameter of the vehicle clutch as input data into the neural network. The at least one value of the at least one operating parameter of the vehicle clutch can represent the power delivered to the vehicle clutch. Alternatively or additionally, the at least one value of the at least one operating parameter of the vehicle clutch can represent the clutch temperature of the vehicle clutch. The at least one value of the at least one operating parameter can be transmitted to at least one of the input nodes of the neural network, for example, by means of an input device. Alternatively, the at least one value of the at least one operating parameter can be transmitted to at least one of the input nodes of the neural network by a control device of the vehicle via a data interface. The at least one value of the at least one operating parameter can be used as input data for determining the clutch temperature using the neural network. Alternatively or additionally, the at least one value of the at least one operating parameter can be used as input data for training the neural network.
[0013] In addition, the method also has a step of determining the clutch temperature by means of a neural network. The neural network determines the clutch temperature based on the input data and the relationship between the clutch temperature and the temporal trend of the input data learned by the neural network. The neural network can process the input data according to the learned relationship, for example, at an intermediate node in order to determine the clutch temperature. When learning the relationship, the neural network can change the mathematical weights of the connections of the individual nodes in order to determine the clutch temperature. The clutch temperature determined by the neural network can be used by other components of the vehicle control unit. For example, the determined clutch temperature can be used to generate a control value for a control unit of the vehicle, for example a transmission control unit of an automatic transmission. In addition, the control value can also be displayed on a display unit of the vehicle. Alternatively or additionally, the control value based on the clutch temperature can be processed by an evaluation unit, for example, for monitoring the driving safety of the vehicle.
[0014] The proposed method enables the clutch temperature to be determined with the aid of a neural network. This makes it possible to dispense with the use of conventional mathematical models, which are generally very complex and computationally very time-consuming. By using a neural network, on the one hand, the computation time required for determining the clutch temperature can be reduced. In addition, the determination of the clutch temperature is based on the temporal progression of the input data. Temporal changes in the input data can therefore also be taken into account by the neural network. For example, a cooling phase or a heating phase of the vehicle clutch can be detected and taken into account when determining the clutch temperature. In addition, for example, situations in which the clutch temperature exceeds a limit value or deviates from the normal behavior of the clutch temperature during the heating or cooling phase can be taken into account. As a result, the neural network can also determine the clutch temperature more accurately. In addition, the temporal progression of the input data is not determined by the neural network itself, but is only forwarded to the neural network in the form of processed input values. This reduces the computational effort required for determining the clutch temperature. As a result, the structure of the neural network can be constructed to be less complex. As a result, the storage bits required for the neural network can also be reduced.
[0015] According to one embodiment, at least one input value is determined with the aid of a detection device that detects temporally successive values of the power delivered to the vehicle clutch. The detection device may be a device configured to receive and process electrical signals. The detection device may be provided with respective time information for the values of the power delivered to the vehicle clutch, which time information may represent the detection time points of the respective values. With the aid of the time information, the detected values may be arranged in chronological order. The detection device may cache the detected values of the power delivered to the vehicle clutch for processing. Alternatively, the detection device may transmit the detected values of the power delivered to the vehicle clutch to a control device of the vehicle, in which the detected values may be processed for determining at least one input value. The detection device is a particularly simple device for acquiring temporally successive values of the power delivered to the vehicle clutch.
[0016] According to another embodiment, the detection of the time-successive values of the power delivered to the vehicle clutch is performed from a predetermined starting time point until the input time point at which the input data is input into the neural network. The detection and time sequencing of the values of the power delivered to the vehicle clutch can be performed using at least one detection device. Alternatively, the values of the power delivered to the vehicle clutch can also be detected or time sequenced in other ways. The starting time point can be predetermined by a user (for example, the driver of the vehicle). Alternatively, the starting time point can be predetermined by the manufacturer of the vehicle. The starting time point can be selected arbitrarily. The input time point of the input data can be specified as the time point when at least one input value is input into the neural network. Alternatively or additionally, the input time point can be specified as the time point when at least one value of at least one operating parameter is input into the neural network. By predetermining the starting time point for the detection of the time-successive values of the power delivered to the vehicle clutch, the method for determining the clutch temperature can be matched to different types of vehicle clutches or different operating states of the vehicle.
[0017] According to another embodiment, the processing of the time-successive values of the power delivered to the vehicle clutch includes determining the mean value of the power delivered to the vehicle clutch. The mean value of the power delivered to the vehicle clutch can be understood, for example, as an arithmetic mean, a geometric mean or a quadratic mean. When determining the mean value, all the time-successive values existing at the time of determining the time point can be taken into account. Alternatively, when determining the mean value, only a subset of the time-successive values of the power delivered to the vehicle clutch can be taken into account. The mean value can be determined by a detection device that detects the time-successive values of the power delivered to the vehicle clutch. Alternatively, the mean value can be determined by a control unit of the vehicle to which the time-successive values of the power delivered to the vehicle clutch are transferred. Determining the mean value is a simple arithmetic operation, and in addition, it can also be used to determine the relationship between the individual values of the power delivered to the vehicle clutch, especially when performing static observation.
[0018] According to another embodiment, a plurality of detection devices are provided for detecting the time-sequential values of the power delivered to the vehicle clutch. Different starting time points for detection are determined for each of the detection devices. For example, a detection device for long-term detection of the value of the power delivered to the vehicle clutch can be provided. For long-term detection, the value of the power delivered to the vehicle clutch can be detected at large time intervals, for example at intervals of 1 minute, 5 minutes, 10 minutes or 30 minutes. Another detection device can be provided for short-term detection of the value of the power delivered to the vehicle clutch. For short-term detection, the value of the power delivered to the vehicle clutch can be detected at small time intervals, for example at intervals of 30 seconds, 1 second, 100 ms or 10 ms. Input values can be determined for each of the detection devices by processing the detected value of the power delivered to the vehicle clutch. For example, in this case, an input value can be determined for the value of the power delivered to the vehicle clutch for long-term detection, and another input value can be determined for the value of the power delivered to the vehicle clutch for short-term detection. Therefore, the temporal trend of the power delivered to the vehicle clutch can be determined in detail and taken into account by the neural network when determining the clutch temperature.
[0019] According to another embodiment, at least one detection device is selected from at least one of the following: a control unit configured for data detection, a low-pass filter and an operational amplifier. The low-pass filter can be understood as an electronic unit for signal processing: with which data in the form of electrical signals within a certain time period can be received. For example, the low-pass filter can be an electronic component for signal processing, which forwards signals with frequencies below the cut-off frequency substantially unchanged. Signals with frequencies above the cut-off frequency are forwarded by the low-pass filter in a manner that is attenuated in terms of their amplitude.
[0020] An operational amplifier can be understood as an electronic unit for signal processing that can receive data in the form of electrical signals within a certain time period. For example, an operational amplifier can be an electronic component for signal processing that can amplify the incoming signal in terms of its amplitude and forward it. For example, the amplitudes of multiple signals entering the operational amplifier can be added. In this case, an electrical signal whose amplitude is equivalent to the sum of the added amplitudes of the input signals can be output from the operational amplifier. By adding the amplitudes of multiple input signals, a time delay in the forwarding of these signals can be achieved. Therefore, low-pass filters and operational amplifiers are electronic components for signal processing that are low in procurement cost and easy to install, and with which a time delay of the signals entering these components can be achieved.
[0021] According to a further embodiment, at least one operating parameter of the vehicle clutch is selected from at least one of the following: the torque of the drive axle of the vehicle engine in mechanical connection with the vehicle clutch; the speed difference between two rotating clutch elements of the vehicle clutch; the speed of the rotating clutch element of the vehicle clutch; the mechanical pressure acting on the clutch element of the vehicle clutch; the current intensity of the current flowing through the clutch element of the vehicle clutch; and the oil sump temperature of the vehicle transmission at the beginning of the switching process of the vehicle clutch. The clutch elements of the vehicle clutch can be, for example, clutch plates of the vehicle clutch, which can be connected to transmit force from the engine to the drive axle. The above parameters can all represent the power delivered to the vehicle clutch. Alternatively or additionally, the above parameters can all represent the clutch temperature of the vehicle clutch. By using at least one of these parameters, it is thus possible to facilitate the determination of the clutch temperature.
[0022] In a second aspect, the invention relates to a method for generating a training data set for a neural network, which is designed to determine a clutch temperature of a vehicle clutch. The method comprises the following steps: providing a plurality of temporally successive values of a power supplied to a vehicle clutch; processing the temporally successive values provided in order to determine an input value representing the power supplied to the vehicle clutch; providing at least one value of at least one operating parameter of the vehicle clutch; and providing a value of the clutch temperature. The training data set comprises the input value and the value of the at least one operating parameter as input data, and comprises the value of the clutch temperature as output data. With the aid of the training data set, the neural network can learn a relationship between the clutch temperature and the temporal course of the input data.
[0023] The value of the power supplied to the vehicle clutch, at least one value of at least one operating parameter of the vehicle clutch and the value of the clutch temperature can be detected by means of a device for processing electrical signals which is designed for this purpose. The value of the power supplied to the vehicle clutch can be processed by means of a device for processing electrical signals which is designed for this purpose, and the detected or processed values can be provided to an input device for inputting input data into the neural network. Alternatively, the detected or processed values can be transmitted from the device which is designed for this purpose to an input node of the neural network via a data interface. Alternatively or additionally, the input data or output data can be present as synthetic data which can be generated by converting the detected or determined data.
[0024] The neural network according to the method of the first aspect can be trained using the training data set generated according to the method of the second aspect. Thus, the advantages of the two aspects can be combined.
[0025] In a third aspect, the present invention relates to a control device for determining the clutch temperature of a vehicle clutch. The control device includes a computer-readable storage medium, on which a neural network for determining the clutch temperature is stored. In addition, the control device also includes at least one detection device, which detects temporally successive values of the power delivered to the vehicle clutch. In addition, the control device also includes a determination device for determining at least one input value, which represents the power delivered to the vehicle clutch. The at least one input value is determined based on processing of temporally successive values of the power delivered to the vehicle clutch detected by the detection device. The control device also includes an input device for inputting input data into the neural network. The input data includes at least one input value and at least one value of at least one operating parameter of the vehicle clutch. In addition, the control device also includes an output device for outputting the clutch temperature determined by the neural network.
[0026] The above-mentioned device of the control device according to the third aspect can be configured to receive, process and forward electrical signals. The above-mentioned device of the control device according to the third aspect can be configured to perform the method according to the first aspect and / or the second aspect. Similarly, the method according to the first aspect or the second aspect can be performed by the control device according to the third aspect. Therefore, the embodiments, technical effects and advantages explained for the first or second aspect are also applicable to the control device according to the third aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 A flowchart diagram showing the steps of a method for determining a clutch temperature of a vehicle clutch by means of a neural network according to one embodiment of the present invention;
[0028] Figure 2 A flowchart diagram showing steps of a method for generating a training data set for a neural network configured to determine a clutch temperature of a vehicle clutch according to a further embodiment of the present invention;
[0029] Figure 3 A control device for determining a clutch temperature of a vehicle clutch using a neural network according to a further specific embodiment of the present invention is schematically shown. DETAILED DESCRIPTION
[0030] Figure 1 A flow chart diagram shows the steps of a method for determining a clutch temperature of a vehicle clutch by means of a neural network according to one specific embodiment of the present invention.
[0031] In a first determination step BS1, at least one input value is determined, which represents the power delivered to the vehicle clutch. The at least one input value is determined based on processing of temporally consecutive values of the power delivered to the vehicle clutch. Figure 1 In an embodiment of the present invention, the power supplied to the vehicle clutch is the switching power supplied to the vehicle clutch during a switching process or gear shift.
[0032] The value of the switching power is detected by means of a detection device such as a low-pass filter. The detection is performed from a predetermined starting time point until an input time point when the input data is input into the neural network. The detected values of the switching power are provided with time information by the detection device and are arranged in chronological order based on the time information. The temporally consecutive values of the switching power are processed in order to determine an average value of the switching power.
[0033] In a second determination step BS2, at least one input value is input as input data into the neural network. In this case, the at least one input value is transmitted to at least one input node of the neural network.
[0034] In a third determination step BS3, at least one value of at least one operating parameter of the vehicle clutch is input as input data into the neural network. The at least one value of the at least one operating parameter is transmitted to at least one input node of the neural network.
[0035] In a fourth determination step BS4 , the clutch temperature is determined by the neural network. The clutch temperature is determined based on the input data and the relationship learned by the neural network between the clutch temperature and the temporal profile of the input data.
[0036] In order to determine the clutch temperature, the input data transmitted to the input nodes of the neural network are transmitted to the intermediate nodes of the neural network via the connections mathematically weighted by the neural network. The transmitted input data are processed at the intermediate nodes and transmitted to the output nodes of the neural network via the connections mathematically weighted by the neural network. During a training process of the neural network preceding the determination method in time, the mathematical weights of the respective connections are adjusted by the neural network based on the training data set. At the end of the method, the value of the clutch temperature will be present at at least one output node of the neural network.
[0037] By determining at least one input value, the temporal profile of the switching power is taken into account when determining the clutch temperature. However, since this temporal profile is not determined by the neural network itself but is only forwarded to it, the computational effort required by the neural network for determining the clutch temperature can be kept low. The neural network can therefore be implemented less complexly. The memory locations required by the neural network can thus also be kept low.
[0038] Figure 2 A flow chart diagram showing steps of a method for generating a training data set for a neural network configured to determine a clutch temperature of a vehicle clutch according to a further embodiment of the present invention.
[0039] In a first training step TS1, a plurality of temporally successive values of the power delivered to the vehicle clutch are provided. These values are detected by a detection device, such as a low-pass filter or an operational amplifier. The detection of the values of the power delivered to the vehicle clutch can be combined with Figure 1 The detection performed in the embodiment of is carried out similarly. Figure 2 In an embodiment of the present invention, the power supplied to the vehicle clutch is again the switching power supplied to the vehicle clutch during the switching process or gear shift.
[0040] In a second training step S2, temporally successive values of the provided switching power are processed in order to determine an input value representing the switching power. Figure 2 In an embodiment of the present invention, the processing of the temporally successive values of the switching power provided is carried out in accordance with Figure 1 An embodiment of similarly includes determining an average value of the switching power.
[0041] In a third training step TS3, at least one value of at least one operating parameter of a vehicle clutch is provided, which value represents a clutch temperature of the vehicle clutch, and is transmitted to at least one input node of the neural network.
[0042] In a fourth training step TS4, values of the clutch temperature are provided. These values are measured values of the clutch temperature. In an embodiment not shown, the values of the clutch temperature are synthetically generated. These values are transmitted to at least one output node of the neural network.
[0043] The generated training data set is composed of input values and at least one value of at least one operating parameter as input data and a value of the clutch temperature as output data.
[0044] according to Figure 2 The training data set generated by the method of the embodiment can be used to train Figure 1 An implementation of a neural network.
[0045] Figure 3 A control device 10 for determining a clutch temperature of a vehicle clutch (not shown) using a neural network 12 is schematically shown according to a further specific embodiment of the present invention.
[0046] The control device 10 comprises a computer-readable storage medium 14 on which the neural network 12 is stored. The control device 10 also comprises an input device 16 which is designed to receive values 18a, 18b, 18c of operating parameters of the vehicle clutch. The input device 16 transmits the values 18a, 18b, 18c of the operating parameters of the vehicle clutch as input data 20a to the neural network 12.
[0047] Furthermore, the control device 10 comprises a detection device 22a, 22b which detects temporally successive values 24a, 24b or 24c, 24d of the power supplied to the vehicle clutch. Figure 1 and Figure 2 , the detection devices 22a, 22b are low-pass filters, and the values 24a, 24b, 24c, 24d of the power supplied to the vehicle clutch are the switching power supplied to the vehicle clutch during the switching process or gear shift. The detected values 24a, 24b, 24c and 24d of the switching power are transmitted from the detection devices 22a, 22b to the determination device 26. The determination device 26 determines at least one average value of the switching power by processing the temporally consecutive values 24a, 24b, 24c, 24d. The at least one average value of the switching power is transmitted by the determination device 26 to the input device 16 as input value 28. The input device 16 transmits the input value 28 as input data 20b to the neural network 12.
[0048] The clutch temperature KT of the vehicle clutch is determined from the input data 20a, 20b by the neural network 12. The determined clutch temperature KT is transmitted from the neural network 12 to the output device 30. The output device 30 can transmit the determined clutch temperature KT to other devices of the vehicle control unit.
[0049] Reference numerals list
[0050] 10 Control Device
[0051] 12 Neural Networks
[0052] 14 Storage Media
[0053] 16 Input Devices
[0054] 18a, 18b, 18c Values of operating parameters
[0055] 22a, 22b Testing equipment
[0056] 24a, 24b, 24c, 24d Time-sequential values of the power supplied to the vehicle clutch
[0057] 26 Determine the device
[0058] 28 Input value
[0059] 30 Output Devices
[0060] KT Clutch temperature
[0061] BS1 First Determination Step
[0062] BS2 Second determination step
[0063] BS3 Third Determination Step
[0064] BS4 Fourth Determination Step
[0065] TS1 First training step
[0066] TS2 Second training step
[0067] TS3 Third training step
[0068] TS4 Fourth training step
Claims
1. A method for determining a clutch temperature (KT) of a vehicle clutch by means of a neural network (12), wherein: The method comprises the following steps: - determining (BS1) at least one input value (28) representing the power supplied to the vehicle clutch, wherein the at least one input value (28) is determined based on processing of temporally consecutive values (24a, 24b, 24c, 24d) of the power supplied to the vehicle clutch; - inputting (BS2) the at least one input value (28) as input data (20b) into the neural network (12); - inputting (BS3) at least one value (18a, 18b, 18c) of at least one operating parameter of the vehicle clutch as input data (20a) into the neural network (12); and - Based on the input data (20a, 20b) and the relationship between the clutch temperature (KT) and the temporal profile of the input data (20a, 20b) learned by the neural network (12), the clutch temperature (KT) is determined (BS4) by the neural network (12).
2. The method according to claim 1, wherein: The at least one input value (28) is determined (BS1) by means of a detection device (22a, 22b) which detects temporally successive values (24a, 24b, 24c, 24d) of a power supplied to the vehicle clutch.
3. The method according to claim 2, wherein: From a predetermined starting time until an input time at which the input data (20a, 20b) are input into the neural network (12), temporally successive values (24a, 24b, 24c, 24d) of the power supplied to the vehicle clutch are detected.
4. A method according to any one of the preceding claims, wherein: The processing of temporally successive values ( 24 a , 24 b , 24 c , 24 d ) of the power supplied to the vehicle clutch includes determining a mean value of the power supplied to the vehicle clutch.
5. The method according to any one of claims 2 to 4, wherein: A plurality of detection devices (22a, 22b) are provided for detecting temporally successive values (24a, 24b, 24c, 24d) of a power supplied to the vehicle clutch, wherein a different starting time for the detection is determined for each of the detection devices (22a, 22b).
6. The method according to any one of claims 2 to 5, wherein: The at least one detection device (22a, 22b) is selected from at least one of the following: a control unit configured for data detection, a low-pass filter and an operational amplifier.
7. A method according to any one of the preceding claims, wherein: At least one operating parameter of the vehicle clutch is selected from at least one of the following: the torque of the drive axle of the vehicle engine that is mechanically connected to the vehicle clutch; the speed difference between the two rotating clutch elements of the vehicle clutch; the speed of the rotating clutch element of the vehicle clutch; the mechanical pressure acting on the clutch element of the vehicle clutch; the current intensity of the current flowing through the clutch element of the vehicle clutch; and the oil sump temperature of the vehicle transmission when the switching process of the vehicle clutch begins.
8. Method for generating a training data set for a neural network (12), the neural network being designed to determine a clutch temperature (KT) of a vehicle clutch, wherein: The method comprises the following steps: - providing (TS1) a plurality of temporally successive values (24a, 24b, 24c, 24d) of the power supplied to the vehicle clutch; - processing the temporally successive values (24a, 24b, 24c, 24d) provided by (TS2) in order to determine an input value (28) representative of the power delivered to the vehicle clutch; - providing (TS3) at least one value (18a, 18b, 18c) of at least one operating parameter of said vehicle clutch; and - Provides the value of (TS4) clutch temperature; The training data set comprises the input value (28) and the value (18a, 18b, 18c) of the at least one operating parameter as input data and the value of the clutch temperature as output data.
9. The method according to any one of claims 1 to 7, wherein: The neural network (12) has been used The training was performed using the training data set generated by the method according to claim 8.
10. A control device (10) for determining a clutch temperature (KT) of a vehicle clutch, the control device comprising: - a computer-readable storage medium (14) on which a neural network (12) for determining the clutch temperature (KT) is stored; - at least one detection device (22a, 22b) which detects temporally successive values (24a, 24b, 24c, 24d) of the power supplied to the vehicle clutch; - a determination device (26) for determining at least one input value (28) representing the power supplied to the vehicle clutch, wherein the at least one input value (28) is determined based on processing of temporally consecutive values (24a, 24b, 24c, 24d) of the power supplied to the vehicle clutch detected by the detection device; - an input device (16) for inputting input data (20a, 20b) into the neural network (12), wherein the input data (20a, 20b) comprises the at least one input value (28) and at least one value (18a, 18b, 18c) of at least one operating parameter of the vehicle clutch; and - an output device (30) for outputting the clutch temperature (KT) determined by the neural network (12).