Method for controlling at least one variable speed radiator fan rotor of a cooling device of a motor vehicle

Abstract

Method for controlling at least one radiator fan rotor (3) of a cooling device (2) of a motor vehicle (1) that can be operated at a variable speed, in which an interior noise in a passenger compartment (6) of the motor vehicle (1) is recorded, vibrations of a steering wheel of the motor vehicle (1) are detected, and the speed of the radiator fan rotor (3) and the speed of at least one further operating element that can be operated in a rotational mode are detected, wherein relevant frequencies that influence the interior noise and / or the vibrations are determined on the basis of the speeds and the interior noise and the vibrations are analyzed taking into account the determined frequencies in order to determine the noise and vibration component that is contributed by the operation of the radiator fan rotor (3), after which the speed of the radiator fan rotor (3) is changed taking into account a minimum required cooling capacity of the cooling device (2).

Description

[0001] The invention relates to a method for controlling at least one radiator fan rotor of a cooling device of a motor vehicle that can be operated at a variable speed.

[0002] A motor vehicle usually has a cooling system used to cool a drive unit, be it an internal combustion engine or an electric drive. The cooling system typically comprises a variable-speed radiator fan rotor through which air is pumped to the drive unit. The radiator fan rotor and its speed are controlled via characteristic maps generated, for example, as a function of the refrigerant pressure of a refrigerant within the cooling system's refrigerant circuit. The rotation of the radiator fan rotor creates unwanted noises in the passenger compartment of the vehicle, i.e., in the interior, such as humming or beats. These noises are usually caused by an imbalance in the radiator fan rotor. This results in acoustic excitation of the interior cavity and vibration excitation of the steering wheel, with excitation frequencies in the range of approximately 20–50 Hz.This frequency range is regularly perceived as particularly disturbing. This excitation sometimes coincides with so-called interior modes, which are associated with, for example, body components such as the tailgate or the like. In addition, there may be interference with other noises or vibrations generated by other rotating components or introduced into the body, resulting in beats that lead to a periodic rise and fall of noise and mutual amplification effects.

[0003] WO 2009 / 059 222 A1 discloses a vehicle roof cooling system for a commercial vehicle, comprising a radiator arranged on the roof of the vehicle, wherein the radiator contains a liquid coolant. Also provided is a fan coupled to the radiator and configured to draw hot air from the radiator. A sensor is provided on the radiator side to measure the coolant temperature. Furthermore, a controller controlling the system or the fan is provided, which is coupled to the sensor. The controller is configured to receive signals from the sensor, evaluate them, and output control signals in response to the evaluation. The fan is a variable-speed fan.Several sensors can be provided to record additional measured values ​​such as the temperature of the coolant entering the radiator, the temperature of the coolant leaving the radiator, the flow rate of the coolant, the coolant pressure, the ambient temperature and the like, which are included in the analysis.

[0004] DE 102 21 036 A1 describes a fail-safe algorithm for a hybrid electric vehicle. The method and system enable an HEV to continue operating without damage after the engine cooling system has been damaged, for example, if a total loss of engine coolant has occurred. Objectives include a fail-safe vehicle strategy to maintain acceptable engine temperatures and minimal noise and vibration emissions or harsh operation, while significantly extending the vehicle's range. First, a determination is made as to whether the vehicle can rely on the torque output of an electric drive motor to operate the vehicle. When engine operation is required, the fuel supply to the engine and the firing of the cylinders are regularly alternated to cool those cylinders when no combustion is taking place. The engine speed is also optimized.

[0005] Furthermore, parallel operation is prevented, i.e., with an applied generator-engine brake. Finally, the speed of at least one engine compartment cooling fan is optimized to minimize electrical load while maximizing airflow.

[0006] According to JP 2004-44507 A, a control device for an electric fan in a vehicle, which controls the drive of an electric fan that is part of a heat exchanger for cooling a vehicle comprising an internal combustion engine, causes the electric fan to resonate with the vibration of the internal combustion engine through the rotary drive. The fan is alternately switched between a reduced first speed and a second speed that can ensure a sufficient air volume for heat exchange.

[0007] DE 10 2013 223 987 A1 describes a thermal system comprising fluid-cooled devices, a control unit, and a thermal loop or loops, each with a cooling actuator and fluid passages. The control unit executes device-specific control logic to arbitrate between cooling demands with different relative priorities. The control unit receives raw speed demands and noise, vibration, and harshness (NVH) limits for each device and processes the raw speed demands and NVH limits to determine a relative cooling priority for each device. The control unit issues a speed command to the actuator(s) for each thermal loop to cool the devices at a value requested by the device with the highest relative cooling priority.A disclosed vehicle includes a traction motor, a transmission selectively connected to the traction motor, fluid-cooled devices each in electrical communication with the motor, and a controller configured to perform the aforementioned arbitration process.

[0008] The invention is based on the problem of providing an improved method for controlling a radiator fan rotor with regard to noise reduction.

[0009] To solve the problem, the invention provides a method for controlling at least one radiator fan rotor of a cooling device of a motor vehicle that can be operated at a variable speed, in which method an interior noise in a passenger compartment of the motor vehicle is recorded, vibrations of a steering wheel of the motor vehicle are detected, and the speed of the radiator fan rotor and the speed of at least one further operating element that can be operated in a rotational mode are detected, wherein relevant frequencies that influence the interior noise and / or the vibrations are determined on the basis of the speeds and the interior noise and the vibrations are analyzed taking into account the determined frequencies in order to determine the noise and vibration component that is contributed by the operation of the radiator fan rotor, after which the speed of the radiator fan rotor is changed taking into account a minimum required cooling capacity of the cooling device.

[0010] The method according to the invention allows for targeted control of the operation of the radiator fan rotor in order to reduce the contribution made by the radiator fan rotor to the overall noise development or vibration behavior. This targeted reduction also allows for the reduction of noise or vibration components resulting from overlapping with other noises or vibrations contributed by other operating elements, thus reducing the overall noise and vibration level in the vehicle interior. This is ultimately based on an examination of the causes that are responsible for the overall generation of noise and vibration and a corresponding frequency analysis that allows the noise and vibration component attributable to the operation of the radiator fan rotor to be determined.

[0011] According to the invention, the interior noise in the passenger compartment is recorded, for example, using a suitable microphone installed there, just as the vibrations of a steering wheel of the motor vehicle are detected using a suitable vibration sensor. Furthermore, the current speed of the radiator fan motor and the speed of at least one other operating element operable in a rotary mode are recorded. Operating elements considered are those operating elements that generate noise or vibrations during operation that contribute to the interior noise or to the haptically perceivable vibrations. Such an operating element can be, for example, an internal combustion engine or a refrigerant compressor as part of the or another cooling device, or the like.According to the method, the presence of frequencies that influence the interior noise or vibrations is determined based on the speeds of the radiator fan rotor and the operating element(s). These frequencies are essentially known. These are usually first-order frequencies or, in the case of an internal combustion engine, a second-order frequency. Once these relevant frequencies have been determined based on the speeds, the recorded interior noise or vibrations are analyzed, taking the determined frequencies into account. This analysis is used to determine the noise and vibration component contributed to the overall noise or vibration by the operation of the radiator fan rotor.If the noise component attributable to the cooling fan rotor is known, the speed of the cooling fan rotor, which ultimately determines its noise component, is changed accordingly to reduce the noise component. This in turn leads to a reduction in the overall noise or vibration level. This is because reducing the noise component of the cooling fan rotor inevitably changes its interaction with other noise components contributed by other operating elements. For example, the speed of the cooling fan rotor can be reduced, i.e. it rotates somewhat slower, if the analysis indicates that reducing the speed will lead to a corresponding reduction in the level. In principle, increasing the speed is also conceivable if this leads to the desired result. However, the speed variation always takes into account the minimum cooling capacity of the cooling system required or demanded by the situation or setting.This means that despite variations in the speed of the cooling fan rotor and thus a change in the air flow, it is always ensured that the components cooled by this are sufficiently cooled so that no damage occurs as a result of insufficient cooling performance.

[0012] The method according to the invention therefore allows for the optimization of interior acoustics with the aid of interior monitoring by specifically determining the contribution of a central disturbance component, namely the speed of the radiator fan rotor, and by specifically controlling the radiator fan rotor, as the causative operating element, in such a way that its contribution is reduced and, ideally, the interior noise can be largely or almost completely reduced. Supported by the analysis, it can also be determined whether the disturbance contribution, i.e. the disturbance frequency resulting from the imbalance of the radiator fan rotor, lies within a range that corresponds to normal rotational behavior of the radiator fan rotor or not. This means that possible damage to the radiator fan rotor can be predicted in a quasi-predictive manner based on the disturbance contribution or the determined disturbance frequency.

[0013] In a further development of the invention, it can be provided that the interior noise and vibrations are subjected to a frequency and / or modulation analysis to determine a frequency spectrum and / or an amplitude curve, wherein the analysis results are analyzed based on the determined frequencies to determine the noise and vibration component contributed by the operation of the radiator fan motor. According to the invention, an analysis of the interior noise and vibrations is carried out with regard to the frequency curve or amplitude response using a suitable analysis or evaluation algorithm. Thus, a frequency spectrum or an amplitude curve is determined, for which purpose a frequency and / or modulation analysis is performed.Frequency analysis can be carried out using FFT (Fast Fourier Transformation), for example, while modulation analysis can be carried out by calculating the modulation depth, for example using a Hilbert transformation. Frequency analysis can be used to create a frequency or amplitude spectrum of the interior noise or vibration behavior, so that it is known which frequencies the noise or vibrations are made up of, or what the amplitude response is over the frequency curve. Determining the modulation depth can also be used to identify any beats, i.e. a combination or superposition of different frequencies from different noise or vibration generators that are responsible for the swelling and fading of the noise or vibration. The determined frequency spectrum orThe amplitude curve is then analyzed taking into account or in conjunction with the frequencies determined from the various speeds of the various noise or vibration generators involved in order to then determine the noise and vibration component of the radiator fan rotor on the basis of this analysis.

[0014] Furthermore, when determining the changed speed of the cooling fan rotor, at least one operating parameter of the cooling device or of another cooling device can also be taken into account. As described, the aim of the method is to determine a changed speed of the cooling fan rotor at which it will be operated in the future to reduce its interference component, and with which it will subsequently be controlled. In addition to the variables or parameters described above that form the basis for the determination, further operating parameters of the cooling device or of another cooling device into which an operating element to be considered in the analysis, such as a refrigerant compressor or similar, is integrated can be taken into account. An example of this is an actual value and the maximum value of a temperature of an engine coolant used to cool the internal combustion engine. This engine coolant can circulate in the cooling device.Alternatively or additionally, it is also conceivable to consider an actual value and a maximum value of the pressure of a refrigerant as operating parameters. This refrigerant can, for example, be considered in another cooling device, which is part of an air conditioning system, for example, whereby this refrigerant is compressed via a refrigerant compressor, which in turn represents an operating element to be considered. These operating parameters can also be considered, as they can, for example, limit any change in the speed of the cooling fan rotor, since corresponding maximum values, as described above, which may be affected as a result of a change in the speed of the cooling fan rotor, must not be exceeded.For example, if the speed of the radiator fan rotor is reduced, resulting in less cooling air being delivered, this can lead to an increase in the engine coolant temperature. However, if the actual engine coolant temperature is already close to the maximum value, a speed reduction may not be possible, which is why another speed adjustment is required to reduce or suppress the noise component of the radiator fan rotor.

[0015] According to a particularly advantageous embodiment of the invention, the frequencies determined from the rotational speeds and, if applicable, the analysis result of the frequency and modulation analysis and the at least one operating parameter are provided as input variables to an algorithm trained by machine learning, which processes them analytically and outputs the rotational speed of the cooling fan rotor to be set as a controlled variable. According to the invention, the determined frequencies as well as, if applicable, the analysis result of the frequency and modulation analysis and, if applicable, the at least one operating parameter are provided as input variables to a self-learning or machine-learning-trained algorithm. This algorithm can, for example, be a neural network, if applicable in combination with reinforcement learning as a special form of machine learning.The input variables given to the algorithm are feature vectors that are analytically processed accordingly by the algorithm or the neural network. The algorithm is designed or trained in such a way that, based on these feature vectors, it determines and outputs a controlled variable that defines the speed of the cooling fan rotor to be set with regard to the desired reduction or complete suppression of the noise component associated with the cooling fan rotor. This controlled variable then forms the basis for the subsequent control of the speed of the cooling fan rotor. The algorithm is therefore an artificial intelligence that processes the feature vectors given to it accordingly. In doing so, the input variables or feature vectors described above are preferably fed to the algorithm or the neural network instead of the output variables such as the recorded interior noise or the measured vibrations or the corresponding speed values, etc.artificial intelligence, as this allows a much faster determination of the controlled variable.

[0016] The at least one operating element can, for example, be another cooling fan rotor, a refrigerant compressor, or a drive unit such as an internal combustion engine, with the rotational speeds of these operating elements forming the basis for the frequency determination. These operating elements are those that, due to their rotation, which is sometimes at a correspondingly higher frequency, generate noises and vibrations that contribute to the interior noise or any vibrations, but also interact with the interference frequencies or interference vibrations generated by the cooling fan rotor, whose rotational speed is to be optimized.

[0017] In a further embodiment of the invention, a first-order frequency can be determined from the speed of the radiator fan rotor, the additional radiator fan rotor, and / or the refrigerant compressor and fed as an input to the algorithm, and / or a frequency of a relevant engine order can be determined from the speed of the drive unit and fed as an input to the algorithm. The interfering first-order frequencies result, for example, from any imbalance in the radiator fan rotor or the additional radiator fan rotor as well as the refrigerant compressor; they can interact with each other. If the drive unit is an internal combustion engine, for example, a relevant engine order is determined as the interfering frequency. In a 4-cylinder internal combustion engine, the relevant frequency is, for example, the second engine or vibration order. The relevant engine or vibration order depends on the number of cylinders.This means that, based on the respective speeds, a corresponding frequency analysis is performed to determine the relevant frequency or vibration orders, characterized as interference frequencies. These determined frequency or vibration orders are passed on as input variables, i.e., as feature vectors, to the algorithm trained by machine learning, i.e., the artificial intelligence, for further processing and determination of the controlled variable.

[0018] Furthermore, according to the invention, at least one further vehicle- or environment-specific parameter can be taken into account when determining the speed of the radiator fan rotor to be set. This means that additional parameters related to the vehicle or parameters related to the environment are provided to the algorithm as further input variables or as feature vectors. An example of such a parameter can be a temperature value of a heating device of the motor vehicle indicating a desired interior temperature.Such a temperature value forms the basis, for example, for the operation of an air conditioning system, which in turn includes a refrigerant compressor, which is naturally operated according to the desired temperature value. This temperature value can ultimately also influence the speed of the refrigerant compressor and thus the noise or vibration spectrum it generates. Another example is an outside temperature value, which can also form the basis for controlling the air conditioning system. Information describing the position of the sun or the direction in which the vehicle is oriented towards the sun can also have an influence, as this parameter indicates any heating of the interior caused by solar radiation.The list of possible additional parameters to be taken into account is not exhaustive; rather, other parameters that may directly or indirectly influence the operation of the cooling fan rotor or another operating element to be taken into account can also be taken into account by the algorithm.

[0019] In addition to the method itself, the invention further relates to a motor vehicle comprising a cooling device with at least one radiator fan rotor operable at a variable speed, a steering wheel, at least one operating element operable in a rotational mode, and a control device for controlling the radiator fan rotor, wherein the control device is configured to carry out the method described above. The corresponding algorithm, in particular the algorithm trained by machine learning, i.e., the neural network, is stored in the control device as a corresponding software application, just as the control device is configured to determine the corresponding input variables described above, i.e., the feature vectors that are incorporated into the algorithm.

[0020] The motor vehicle may further comprise at least one further operating element in the form of a further radiator fan rotor, a refrigerant compressor or a drive unit, in particular an internal combustion engine, but also an electric motor.

[0021] Further advantages and details of the present invention will become apparent from the exemplary embodiments described below and from the drawings. In the drawings: Fig. 1 a schematic diagram to explain the method according to the invention, Fig. 2 a schematic diagram of an algorithm trained by machine learning (block AI controller) for determining the controlled variable, and Fig. 3 a schematic diagram of a motor vehicle according to the invention.

[0022] Fig. Figure 1 shows a schematic diagram in the form of a block diagram to explain the method according to the invention and the various determined feature vectors that are incorporated into the algorithm trained by machine learning, i.e., the artificial intelligence. The relevant data and the feature vectors determined based on them are represented by the individual box representations. These feature vectors, or sometimes the data itself, are passed to an algorithm trained by machine learning, i.e., an artificial intelligence, represented by box 1. Based on this, the algorithm outputs a controlled variable, based on which the speed of a cooling fan rotor is then varied accordingly to reduce noise and vibration.

[0023] The situation in a motor vehicle comprising a cooling device with at least one radiator fan rotor operable at a variable speed is considered as an example, wherein the cooling device serves, for example, to cool an internal combustion engine having a plurality of cylinders.

[0024] The motor vehicle has a passenger compartment in which a microphone is installed on the one hand and a steering wheel on the other. The corresponding cooling system with its operating components is also provided, although the motor vehicle may also contain another cooling system, such as an air conditioning system, with its specific operating components, or other operating components operable in a rotating mode. During operation of the motor vehicle, noise may be generated resulting from the operation of the radiator fan rotor as well as other operating elements of the motor vehicle operable in a rotating mode, such as the internal combustion engine itself or a refrigerant compressor, etc. Noise components or vibrations generated by the individual operating elements may overlap accordingly, resulting in beats, etc.During operation, a corresponding interior noise develops in the passenger compartment, which is naturally perceived by the vehicle's occupants, just as vibrations can be felt on the steering wheel. The aim is to reduce or dampen the interior noise and vibrations accordingly. This can be done by specifically determining the noise or vibration component contributed by a radiator fan rotor in the cooling system, on the one hand, and by specifically reducing this by varying the control of the radiator fan rotor, on the other. This reduction has a corresponding influence on the overall interior noise or vibrations due to the interaction with other vibrations introduced by other operating elements.the total vibrations on the steering wheel, so that by specifically reducing a specific noise or vibration component, an overall reduction in the interior noise or steering wheel vibrations can be achieved.

[0025] As shown in box a, the interior noise in the passenger compartment is recorded using a microphone. Box b shows the vibrations in the steering wheel being recorded using suitable sensors. This means that the two specific sources of noise to be dampened or reduced are measured.

[0026] Box c indicates that the speed of the radiator fan rotor, which is to be varied, is also recorded accordingly. Box d indicates that the speed of a second radiator fan rotor, which may be part of the cooling system or part of another cooling system, is also recorded.

[0027] Furthermore, any refrigerant compressor is considered an operating element operable in a rotary mode, for which the rotational speed is recorded as shown in box e. In addition, the internal combustion engine is also considered an operating element operable in a rotary mode, for which the rotational speed is also recorded as shown in box f.

[0028] Based on the data or information collected via boxes a - f, appropriate processing is carried out using appropriate frequency and / or modulation analyses.

[0029] As shown in box a', the interior noise captured by the microphone is processed using frequency and modulation analysis, with the processing result being passed to the algorithm shown in box I. Similarly, as shown in box b', the signal describing the steering wheel vibrations is also processed using frequency and modulation analysis, with this processing result also being passed to the algorithm shown in box I. The frequency analysis can be, for example, an FFT analysis, while the modulation analysis, which determines the modulation depth to detect potential beats, can be performed using a Hilbert transform, for example.

[0030] The acquired speed information of the cooling fan rotors, shown in boxes c and d, is also subjected to a corresponding frequency and / or modulation analysis to determine first-order frequencies, and this result is then fed back to the algorithm. Similarly, the speed of the refrigerant compressor, recorded according to box e, is also subjected to a frequency and / or modulation analysis to determine a first-order frequency for this, as shown in box e', and provide it to the algorithm.

[0031] The engine speed measured in accordance with box f is also subjected to a frequency and / or modulation analysis, whereby the frequency of a relevant engine order, which in this case depends on the number of cylinders, is determined and this result is fed to the algorithm.

[0032] As the block diagram according to Fig. 1 further shows, as shown in box g, the actual value of the refrigerant pressure of a refrigerant of the cooling device or devices can be recorded, as well as the temperature of an engine coolant, as shown in box h, wherein a respective maximum value is also recorded for each of these operating parameters, as shown for the maximum refrigerant pressure in box i and the maximum temperature of the engine coolant in box j. These variables determine the use of the radiator fan rotor for cooling, wherein in particular the operating parameters according to boxes i and j, i.e. the respective maximum values, serve to limit the corresponding actual values ​​in order to avoid damage to an operating component. For example, the refrigerant pressure must not exceed a certain maximum pressure, otherwise the refrigerant compressor will shut down.Of course, the temperature of the internal combustion engine must not exceed a maximum value, which is determined by the maximum value of the engine's coolant. The information or parameters from boxes g, h, i, j are also fed into the algorithm.

[0033] Furthermore, as shown in box k, additional parameters can be captured and fed into the algorithm. These additional parameters can be vehicle- or environment-specific parameters, such as a desired interior temperature, which can be controlled via a corresponding temperature value of a heating device, or an outside temperature value, or similar. These parameters or information can also influence the operation of the cooling device.

[0034] The algorithm, which is typically a neural network of appropriate characteristics and trained by machine learning, processes the given feature vectors according to boxes a'-f' and g-k and determines a controlled variable, which is represented by box m. This controlled variable defines the speed of the cooling fan rotor in question to be set, its noise and vibration component, which was determined by the algorithm and which is to be reduced. The noise and vibration component is determined by the algorithm based on the analysis results according to boxes a' and b', taking the other feature vectors into account. The determined and output controlled variable then further operates the cooling fan rotor accordingly. The speed can be increased or decreased relative to the previous actual speed value, depending on which change is appropriate for reducing noise and vibration.

[0035] The procedure, as exemplified in Fig. 1, is of course carried out continuously, ie the interior noise is continuously recorded with the microphone and the vibrations of the steering wheel are continuously recorded, as well as the corresponding operating parameters, so that a continuous monitoring of the actual state with regard to the development of noise and vibration takes place, as well as their corresponding damping by an adaptive control of the speed of the radiator fan rotor.

[0036] Fig. Figure 2 shows a schematic diagram of the algorithm according to Box I, which implements a block AI controller R. The individual supplied feature vectors M1, M2, ..., Mn are shown, as they are with respect to Fig. 1. These are first weighted using specific weights G1, G2, ..., Gn, which are specific to the respective feature vectors M1, M2, ..., Mn (see the example block diagram of the block AI controller R). The weighted values ​​are then subjected to a summation S, based on which the actual controlled variable m is determined and output via a transfer function Ü.

[0037] The block AI controller R, for example, is implemented using a neural network in combination with reinforcement learning. The block AI controller R learns the optimal control of the radiator fan rotor during operation, specific to the vehicle and the situation. It is designed for the specific application in the motor vehicle with regard to the feature vectors it must consider, as well as the assigned weights and the corresponding transfer function. The process is implemented in a suitably configured control unit, which is integrated into a vehicle-side communication structure to receive the relevant input data (e.g., recorded interior noise and vibration measurements, engine speeds, etc.), and which is configured accordingly in terms of software to perform the various frequency and / or modulation analyses, and in which the AI-based algorithm is also stored.The control device either controls the radiator fan rotor itself or outputs the controlled variable to a corresponding communication bus, where the controlled variable is read by a control device controlling the radiator fan rotor.

[0038] Fig.3 shows a schematic diagram of a motor vehicle 1 according to the invention, with a cooling device 2 (not shown in detail), part of which is a radiator fan rotor 3, which is assigned, by way of example, to an internal combustion engine 4 in order to cool it. Also provided is a microphone 5, via which an interior noise in a passenger compartment 6 of the motor vehicle 1 can be recorded, as well as a steering wheel 7 with associated sensors 8, via which any steering wheel vibrations can be measured. Also provided is a control device 9, which is integrated into a corresponding communication network 10 (not shown in detail), via which data from further operating elements, as sufficiently described above, can be fed to it.

[0039] The control device 9 is configured in terms of software to carry out the method described above based on data or measured values ​​etc. provided to it in order to finally determine a controlled variable via which the speed of the rotor 3, which can be controlled directly via the control device 9, for example, or otherwise via the communication network 10, is to be varied. List of reference symbols: 1 motor vehicle 2 Cooling device 3 Radiator fan rotor 4 internal combustion engine 5 Microphone 6 Passenger compartment 7 Steering wheel 8 Sensor technology 9 Control device 10 Communication network

Claims

[1] Method for controlling at least one radiator fan rotor (3) of a cooling device (2) of a motor vehicle (1) that can be operated at a variable speed, in which an interior noise in a passenger compartment (6) of the motor vehicle (1) is recorded, vibrations of a steering wheel of the motor vehicle (1) are detected, and the speed of the radiator fan rotor (3) and the speed of at least one further operating element that can be operated in a rotational mode are detected, wherein relevant frequencies that influence the interior noise and / or the vibrations are determined on the basis of the speeds and the interior noise and the vibrations are analyzed taking into account the determined frequencies in order to determine the noise and vibration component that is contributed by the operation of the radiator fan rotor (3), after which the speed of the radiator fan rotor (3) is changed taking into account a minimum required cooling capacity of the cooling device (2). [2] Method according to claim 1, characterized by that the interior noise and the vibrations are subjected to a frequency and / or modulation analysis to determine a frequency spectrum and / or an amplitude curve, wherein the analysis results are analyzed to determine the noise and vibration component contributed by the operation of the radiator fan rotor (3) on the basis of the determined frequencies. [3] Method according to claim 1 or 2, characterized by that in the context of determining the changed speed of the cooling fan rotor (3), at least one operating parameter of the or a further cooling device (2) is additionally taken into account. [4] Method according to claim 3, characterized by that an actual value and a maximum value of a temperature of an engine coolant and an actual value and a maximum value of the pressure of a refrigerant are taken into account as operating parameters. [5] Method according to one of the preceding claims, characterized bythat the frequencies determined on the basis of the rotational speeds, or the frequencies determined on the basis of the rotational speeds and the analysis result of the frequency and / or modulation analysis and / or the at least one operating parameter are given as input variables to an algorithm trained by machine learning, which processes them analytically and outputs the rotational speed of the cooling fan rotor (3) to be set as a controlled variable. [6] Method according to one of the preceding claims, characterized by that the at least one further operating element is a further radiator fan rotor, a refrigerant compressor or a drive unit (4), the speed of which is used. [7] Method according to claim 6, characterized bythat a first-order frequency is determined from the speed of the radiator fan rotor (3), the further radiator fan rotor and / or the refrigerant compressor and is given as an input variable to the algorithm, and / or a frequency of a relevant engine order is determined from the speed of the drive unit (4) and is given as an input variable to the algorithm. [8] Method according to one of the preceding claims, characterized by that at least one further vehicle- or environment-specific parameter is taken into account when determining the speed of the radiator fan rotor (3) to be set. [9] Method according to claim 8, characterized by that the parameter is a temperature value of a heating device of the motor vehicle (1) indicating a desired interior temperature or an outside temperature value or a position of the sun determined by means of a sensor. [10] Motor vehicle, comprising a cooling device with at least one radiator fan rotor (3) operable at a variable speed, a steering wheel (7), at least one operating element (4) operable in a rotational mode, and a control device (9) for controlling the radiator fan rotor (3), wherein the control device (9) is designed to carry out the method according to one of the preceding claims. [11] Motor vehicle according to claim 10, characterized by that the at least one further operating element is a further cooling fan rotor, a refrigerant compressor, or a drive unit (4).

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