DE102015217110A1 - Method for performing a diagnosis in a motor vehicle - Google Patents

Abstract

The invention relates to a method for carrying out a diagnosis in a motor vehicle (50), in which, during operation of the motor vehicle (50), a time profile of at least one physical variable is detected, this time profile is subjected to a frequency analysis so that a frequency spectrum is obtained, which is characterized by frequency spectrum data, wherein the frequency spectrum data are evaluated and the evaluation in the operation of the motor vehicle (50) is taken into account.

Description

The invention relates to a method for carrying out a diagnosis in a motor vehicle and to an arrangement for carrying out the method.

State of the art

In order to ensure the functionality of a motor vehicle, it is necessary to monitor components in the motor vehicle and to record and evaluate vehicle internal and external variables. Thus, for example, be provided to monitor components of the electrical system of the motor vehicle.

As a vehicle electrical system, the totality of all electrical components is referred to in motor vehicles. Typically, in a vehicle electrical system in a motor vehicle, a voltage of 12 volts is available, which is provided by a battery of the electrical system. It should be noted that Bordnetze of modern motor vehicles and commercial vehicles are becoming increasingly powerful and complex. Thus, more and more electrical consumers come through completely new functions and by electrification of conventional, for. B. hydraulic functions, added. In addition, consumer profiles are becoming increasingly dynamic. This is reflected in fast alternating working modes, such as sleep or sleep, ultra-low power or ultra-low power operation, etc.

In addition, it should be noted that more and more components in the motor vehicle, the energy, eg. B. by recuperation, can feed back. As a result, the on-board network is decentralized, away from the classic battery-generator-consumer model to smart grids or the "smart grid", where each participant can act as a consumer or as a generator, depending on the mode. In addition, new voltage levels are added, such as 48 V, high voltage (HV), with bidirectional energy flows between the levels, etc. Furthermore arises due to increasing equipment options in the vehicle and placement options in the control unit, an ever greater variety of variants within a vehicle platform.

This results in countless operating and fault scenarios, all of which must be reliably covered by energy management.

• – Bereitstellen von Reserven im Kabelbaum unter dem Stichwort "mehr teures Kupfer",
• – ein zeitnaher Übergang zu 48 V- und/oder HV-Systemen,
• – Bereitstellen weiterer Schutzmaßnahmen in jedem Steuergerät und/oder weiterer Schutzelemente im Kabelbaum.

Although pure hardware measures are technically feasible, they can not be implemented at short notice from an economic point of view and in many cases are not sustainable. This can be, for example:

• - Provision of reserves in the wiring harness with the keyword "more expensive copper",
• A timely transition to 48 V and / or HV systems,
• - Provide further protective measures in each control unit and / or other protective elements in the wiring harness.

For this reason, complex on-board energy management software algorithms are used in the on-board power system, which should make optimum use of the networked hardware components.

• – die Sensierung wichtiger Komponenten wie Generator und Batterie,
• – die Batteriezustandserkennung und Batterieprognosen; der Zustand der Batterie(n) ist die Grundlage für Erhaltung der Startfähigkeit,
• – die Überwachung der Batterieströme,
• – die stufenweise Zu- und Abschaltung der Verbraucher.

The most important tasks for conventional energy management are:

• - the sensing of important components such as generator and battery,
• - the battery condition detection and battery forecasts; the condition of the battery (s) is the basis for maintaining the ability to start,
• - the monitoring of battery currents,
• - the gradual connection and disconnection of consumers.

It should be noted that on-board network diagnostics is nowadays understood as part of energy management.

Furthermore, it should be taken into account that currently used real-time fault analysis systems and security systems only own computing capacity and resources, such. B. the existing computing power, to implement the security objectives and other functions. In this case, either only individual function groups or certain parts of the vehicle electronics are monitored. However, no EMC-relevant data (EMC, electromagnetic compatibility) are used and used by the vehicle and vehicle environment. In addition, today's safety systems do not use self-learning algorithms and therefore have to be adapted by the manufacturer for the respective car model and to its individual equipment variant.

In addition, it should be noted that previously used monitoring functions of mechanical components focus on certain misbehavior. How to optimize z. B. the knock sensor injection, but detects no defects in adjacent modules.

It should be noted that there is no comprehensive monitoring of all mechanical vehicle parts, in particular no evaluation of noise or vibration.

Disclosure of the invention

Against this background, a method with the features of claim 1 and an arrangement according to claim 14 are presented. Furthermore, a computer program according to claim 15 and a machine-readable record carrier on which the computer program is stored are proposed. Embodiments result from the dependent claims and the description.

The presented method enables the diagnosis in a motor vehicle and is characterized in that detected signals are subjected to a frequency analysis in order to obtain a spectrum in this way. A frequency spectrum or spectrum indicates the composition of a signal from different frequencies and their amplitudes or weightings. The frequency data obtained in this way can be compared and / or combined with bus data (CAN, LIN,...) And data from the time domain.

This frequency spectrum or frequency spectrum data characterizing the frequency spectrum is or will be evaluated. The evaluation is typically taken into account in the operation of the motor vehicle immediately and in real time. This results in a closed circle.

Up to now, on-board on-board diagnosis in conventional vehicles has been using the recorded voltage and current measured values, which are further processed in the time domain, as a database. The proposed method now proposes to use the frequency spectra of the signals and to enable a number of applications for diagnosis by linking with other data available in the vehicle. These frequency spectra of the current and voltage signals, possibly combined with data from the time domain, represent the named new database.

It should be noted that in the method both electrical signals and other signals, for example. Acoustic signals, can be detected and evaluated.

The proposed method provides, in an embodiment, to detect the voltages and currents, which are referred to below as signals, in particular electrical signals, at relevant points in the on-board network in such a configuration in real time that the measured data obtained enable clear differentiation among consumers and generators and could be used for profile creation and recognition.

The detected time profile can be subjected to a Fourier transformation in order to obtain the frequency spectrum. However, it can also be decomposed in other ways into its frequencies and their amplitudes or components, for example by a Fast Hartley transformation or other suitable methods. Hereinafter, reference will be made to the Fast Fourier Transformation. Of course, other methods, as also exemplified herein, are applicable.

In one embodiment, the signal is only at one point, z. B. at the battery or generator, sensed with a high-resolution AD converter and transmitted to a computer. The next important step is the FFT (Fast Fourier Transformation) of the incoming digitized measurement data. This provides in design the frequency spectrum of the signal. This data can be used with other recorded measurement data, such as. As temperature data, speed sensor data and bus messages that are relevant to the energy flows in the vehicle, linked and, for example, further processed by software algorithms.

The aim is to be able to draw as many conclusions as possible about the energy flows in the vehicle electrical system and the switching behavior of the most important consumers and producers of safety with as few sampling points as possible.

For this purpose, the already existing infrastructure, such as, for example, measuring taps, sampling points, sensor data and computer capacities, can be used in order to avoid additional hardware costs. The prerequisite for this is that the bandwidth of the recorded signals for the on-board network analysis in the frequency domain is sufficient and that the real-time requirements are met, eg. B. with regard to the transmission of measured data and processing in the computer, for example. A computing unit in a control unit.

In this way, it is possible to reliably detect malfunctions in a branched and flexible on-board network topology. In doing so, a new database for the comprehensive diagnosis of electrical producers and consumers as well as consumer and producer profiling can be created. The new database can be linked to the existing database. Individual entries or patterns in the profiling database are so-called signatures. A signature consists of an event in the time domain, eg. B. switching on a motor, and its image in the frequency domain together. Every signature is as clear as a fingerprint. The fault diagnosis thus seeks anomalies in the frequency spectrum of the signatures and / or disturbances that can not be assigned to a signature.

Thus, new applications for error detection and error prevention, among others, in the electrical system and in the individual control units possible and conventional applications for on-board diagnostics easier and cheaper implemented. The aim is to save hardware protection measures and to further simplify the wiring harness structure. This is a clear difference from conventional systems and procedures. It can be detected errors that were previously unrecognizable or were detected much later, such as the detection of an impending failure of its characteristics before it occurs.

It should be noted that the tendency in the motor vehicle to more functional safety at the same time sinking costs. which basically represents a conflict of goals. With the presented method, it is now possible to solve this conflict of interests through intelligent use of resources or at least to defuse it.

Furthermore, the presented method allows the use of the computing capacity of various control devices and at least partially cloud services, so that larger amounts of data can be processed in better detail. It does not dispense with additional functions, just because the computing capacity of a single controller is not sufficient.

It should also be remembered that electronic security systems are generally at risk from EMC, which can lead to dangerous misconduct if they are disturbed and the incident occurs exceeds current EMC standards. By observing the vehicle environment in the spectral range, vehicle systems can become more robust to interference.

Self-learning algorithms reduce the configuration overhead of the monitoring arrangement described herein for different vehicle brands, models, and configurations. The presented monitoring arrangement adapts flexibly to its environment, yet distinguishes dangers and defects from the "normal" state of the vehicle by comparison with signatures or the data from the self-learning algorithms.

Furthermore, by spectral monitoring of mechanical vehicle parts by means of suitable sensors, previously unrecognized or too late detected defects can be identified. So z. B. a worn ball bearings are detected by its noise, before it possibly blocks the wheel and causes an accident.

In this way, the ASIL detection (ASIL: Automotive Safety Integrity Level) is facilitated and structural costs in the on-board network are simultaneously saved while at the same time providing a greater depth of analysis for errors. In addition, errors can be detected before their effects become visible to the driver.

In addition, the method can further increase diagnostic coverage and road safety through ongoing networking in and around the vehicle, while further reducing hardware and system costs. By including further signals, eg. B. by the use of microphones and antenna, the database can be significantly increased. Furthermore, a flexibility of the novel diagnostic arrangement and different options of partitioning this arrangement from simple to highly complex. Here, so-called cloud computing services can be included for processing and storing digital data.

Another field of application is, for example, a data processing on several control units, instead of a central device with very powerful and therefore expensive hardware, resulting in a cost savings.

It has been described above, for. B. integrate more taps into the diagnostic system. Each system expansion typically brings more data and requires more storage and computational resources, such as: B. RAM in MCU (Microcontroller Control Unit) or ECU (Electronic Control Unit). It should be noted that the developers of automotive ECUs can not keep the large reserves for the future for cost reasons, d. H. the centralized approach quickly hits its hardware and cost limits. At the same time there are always unused resources, eg. B. control units that are currently in standby or not fully utilized. It is proposed herein to dynamically distribute the data processing for the proposed diagnostic system to multiple controllers, taking into account the real-time requirements. This can be implemented using AUTOSAR or other approaches.

Furthermore, the use of cloud computing offers even more flexibility in diagnostic system partitioning. The data processing and storage is not tied to the location of the data acquisition, typically in the motor vehicle. Depending on the system load, the data can be processed in an available control unit or outside the vehicle. Above all, cloud computing is suitable for very large amounts of data over long periods of time as well as for highly complex algorithms or evaluation strategies.

Furthermore, the presented method allows the processing of signatures in the frequency domain with self-learning algorithms. It also allows the introduction of EMC monitoring the vehicle environment and its integration into the existing security concept. In particular, the extension of the fault diagnosis by spectral analysis to other, non-electronic systems proves to be advantageous.

In one embodiment, electrical variables are detected and evaluated. It should be noted that most electric consumers and generators in a vehicle have switched power modules, such as DC / DC converters, DC / AC converters and AC / DC converters, which, depending on the operating point, which is due to the current / Voltage level and the power consumption or output is defined, leave a unique signature in the time and in the frequency domain. These are, for example, tracks in the frequency spectrum due to a specific switching frequency, a specific current profile during startup and a certain power consumption in various operating modes such as Active or Sleep.

With regard to EMC safety, it must be taken into account that the anomalies or irregularities in the switching behavior of DC / DC converters not only have functional effects, but also cause serious EMC problems for the affected control device and its environment. The on-board diagnostics proposed herein can detect the conducted and radiation bound disturbances to a certain extent so that the OEM could quickly identify an EMC-conspicuous system configuration.

It should be noted that the need to capture more signals usually means, on the one hand, creating even more accurate profiles, which is called positive, and on the other hand having to process more data, which, because more computing power is needed than is called negative. It is not necessary to capture the data of every smallest consumer in the first stage of development very precisely. The degree of detail can be further deepened depending on the state of development and application.

• – Erkennung von Kabelbrüchen bzw. schlechter Kontaktierung durch Auswertung breitbandiger Störung des Bordnetzes, die zur Funkenbildung führen, ähnlich einem Kurzschluss, aber ohne Auswirkung auf allgemeinen Spannungspegel des Bordnetzes,
• – frühzeitige Erkennung von Anomalien im Steuergeräte-Verhalten. Beispiel für Echtzeit-Abgleich der Signaturen: z. B. nach dem CAN Befehl XY soll im Frequenzspektrum das Band zwischen 20 und 30 kHz charakteristische Störungen für x Sekunden enthalten, weil der Motor des Fensterhebers aktiviert wurde. Die hinterlegten Software-Algorithmen sollen entscheiden, was bei der Abweichung von der vorstehend genannten Signatur passieren soll. Eine denkbare Reaktion stellen ein Fehlerspeichereintrag und eine Meldung am Bordcomputer, dass ein Service fällig ist, dar,
• – frühzeitige Erkennung einer Fehlfunktion eines DC/DC-Wandlers noch vor dem Ausfall erkennen. Bspw. schaltet ein Wandler im Steuergerät XY normalerweise mit 170 kHz in einem Toleranzband von 160 bis 180 kHz, dann plötzlich ändert sich seine Schaltfrequenz, z. B. "wandert" sie in Richtung 200 kHz. Die vorgestellte Diagnoseanordnung kennt die typische Signatur dieses Wandlers im aktuellen Arbeitspunkt und kann das ungewöhnliche Verhalten durch Echtzeit-Analyse des Frequenzspektrums sofort erkennen, sie meldet die Überschreitung von Toleranzband an übergeordnete Software-Module, die das ungewöhnliche Verhalten z. B. mit dem Kurzschlussfall oder Überlastfall in Verbindung setzen könnten und sofort Gegenmaßnahmen einleiten, noch bevor die Ausgangsspannung einbricht und die herkömmlichen Diagnosemethoden einspringen. Folgeschäden können so vermieden werden,
• – Erkennung von Fehlfunktionen und Ausfall von Strängen in DC/DC-Multiphasensystemen erkennen, z. B. Asynchronität von Schaltvorgängen erkennen, ggf. Gegenmaßnahmen einleiten und Verfügbarkeit erhöhen,
• – Schützklebediagnose, wurden die Schütze tatsächlich geschlossen/geöffnet,
• – Erkennung von Elektromotorfehlern,
• – Bordnetz- und Steuergeräteoptimierung, nämlich Nutzung der gesammelten Daten für nächste Generationen oder für Software/Hardware-Upgrades,
• – Spektralanalyse als Teil eines ASIL-Nachweises als ein Baustein in ASIL/FuSi-Argumentationskette. Dies dient der Erleichterung des ASIL-Nachweises und des FuSi-Nachweises (FuSi: funktionale Sicherheit).

The following are application examples for energy-related on-board network diagnosis in normal operation and in the event of a fault:

• - Detection of cable breaks or bad contacting by evaluation broadband interference of the electrical system, which lead to sparking, similar to a short circuit, but without affecting the general voltage level of the electrical system,
• - early detection of anomalies in ECU behavior. Example of real-time synchronization of signatures: z. B. after the CAN command XY in the frequency spectrum, the band between 20 and 30 kHz characteristic interference for x seconds included, because the motor of the window regulator was activated. The stored software algorithms are to decide what should happen when the deviation from the above-mentioned signature. One conceivable reaction is a fault memory entry and a message on the on-board computer that a service is due,
• - Detect early detection of a malfunction of a DC / DC converter before the failure. For example. If a converter in the XY control unit normally switches at 170 kHz within a tolerance band of 160 to 180 kHz, then suddenly its switching frequency changes, eg. B. "wanders" in the direction of 200 kHz. The presented diagnostic device knows the typical signature of this converter in the current operating point and can recognize the unusual behavior by real-time analysis of the frequency spectrum immediately, it reports the exceeding of tolerance band to higher-level software modules that the unusual behavior z. B. could connect to the short circuit or overload case and immediately take countermeasures, even before the output voltage drops and jump in the conventional diagnostic methods. Consequential damages can be avoided
• - Detect malfunction and strand failure in DC / DC multiphase systems, eg. B. detect asynchronicity of switching operations, if necessary initiate countermeasures and increase availability,
• - contact adhesive diagnosis, the shooters were actually closed / opened,
• - detection of electric motor errors,
• - On-board network and ECU optimization, namely use of the collected data for next generations or for software / hardware upgrades,
• Spectral analysis as part of an ASIL detection as a building block in ASIL / FuSi argumentation chain. This is to facilitate the ASIL detection and the FuSi detection (FuSi: functional safety).

The inclusion of a contactless data acquisition, eg. With a microphone or an antenna, z. B. an RF antenna, leads to an expansion of the database. This additional data can be linked to the already existing data as described above.

Furthermore, a plausibility check of the diagnostic system by the redundant execution of FFT (Fast Fourier Transformation) and data evaluation on two or more computers with a subsequent comparison of the results in terms of ASIL requirement is possible.

Self-learning algorithms for the identification of anomalies in the frequency band are also presented. For this purpose it is proposed a profiling to extend self-learning algorithms. The arrangement for diagnosis learns to assign the signatures / profiles over time. For this purpose, this links, for example, the CAN messages with the data from z. B. the time and frequency range (current measurement, voltage measurement) and can detect the malfunction early in detected anomalies. Self-learning algorithms help to get a handle on the huge variety of options in terms of equipment and placement options and the always existing physical series production.

Furthermore, in a consistent use of self-learning algorithms no longer each trim level of a vehicle would have to receive their individual software, such as. Signatures, information on relationships, etc., but the system would be able to adapt to the trim level itself.

Furthermore, a self-monitoring function for safety ECUs has to be implemented. In the first stage of development, the diagnosis can concentrate on the energy flows within a control unit or a component, which does not have to monitor the entire vehicle electrical system globally at the beginning. A slimmed-down variant can, for. For example, safety-relevant control units keep an eye on their own frequency spectrum in order to enable the early detection of impending failures. Such a procedure is conceivable, for example, for low-cost vehicles and also enables the system supplier to gain experience with such systems as long as the system architecture is still clear. If one connects such ECUs with their own FFT analysis sent in the electrical system, also a possibly limited diagnosis of the entire electrical system is conceivable. This expansion stage can represent a kind of basis for further expansion stages.

It must also be taken into account that sources of interference exceeding the standard values pose an accident risk. These sources must be detected, reported and removed from the road until they are no longer hazardous. The vehicle can identify the external sources of EMC interference with its own antennas and link them with GPS data and camera data. This leads to the localization of the static and mobile sources of interference,

In one embodiment, the most critical systems, such as ABS / ESP / brake system, expediently even before the occurrence of strong interference from static sources such. As substations or radars, forewarned at previously known coordinates and z. B. the input sensitivity of the inputs temporarily reduced.

In a further embodiment, the vehicle can report the critical impairment of its own electronic systems by external sources of interference to the competent authorities, z. For example, the license plate of the radiating vehicle can be detected by the camera and reported to the police. Cameras of the self-steering vehicles or vehicles with assistance systems can be used for this purpose without obstructing extra cameras and sensors,

In yet another embodiment, the on-board EMC diagnosis is used to protect against vehicle tampering and thus can contribute to safety.

Finally, the error detection described herein can be extended to mechanical parts by spectral analysis. Thus, mechanical anomalies can be detected with a microphone, evaluated and linked to other data.

Further advantages and embodiments of the invention will become apparent from the description and the accompanying drawings.

It is understood that the features mentioned above and those yet to be explained below can be used not only in the particular combination indicated, but also in other combinations or in isolation, without departing from the scope of the present invention.

Brief description of the drawings

1 shows a schematic representation of an embodiment of the described arrangement.

2 shows a schematic representation of another embodiment of the arrangement described.

3 shows an embodiment of a motor vehicle for carrying out the method.

Embodiments of the invention

The invention is schematically illustrated by means of embodiments in the drawings and will be described in detail below with reference to the drawings.

1 shows an arrangement, which is also referred to herein as a monitoring arrangement and collectively with the reference numeral 10 is designated. The order 10 serves to monitor a vehicle electrical system and its components. In this case, the functionality shown can be distributed over several control units or integrated into a single control unit.

The illustration shows a wiring system 12 including sources and consumers. Furthermore, data sources for frequency information of the electrical system are included herein 12 contain. Furthermore, there are other data sources 14 shown. These include, for example, additional sensors, antennas, CAN bus and other bus systems and microphones. The data sources 12 and 14 provide analog and digital input data. Analog data is converted into a preferably fast analog-to-digital converter 16 entered. The converted data and digital data are subjected to a frequency analysis 18 subjected.

Subsequently, a data evaluation takes place 20 in which a linking of the data and an evaluation is carried out by means of a logic. Here stored and possibly learned frequency profiles are stored as far as possible of all relevant consumers and sources. It can also be provided a decision logic, which decides from when a detected result is defined as a problem and how to respond to it.

The result is about a data processing 22 , which may be provided in various vehicle components, returned. This may include actuators, power electronics, control units, safety systems, on-board computers, etc.

2 shows a further embodiment of the arrangement, in total with the reference numeral 30 is designated with too 1 comparable components that correspond accordingly 1 are provided with reference numerals. In the presentation is also a frame 32 plotted the frequency analysis 18 and the data evaluation 20 includes. The associated functionalities can be distributed to several control units and / or provided by means of a cloud.

The information or commands from the data processing 22 go in the 1 and 2 into the data sources 12 . 14 , so closes a rule or control circuit. This makes it possible not only to display the error, but also to respond to it. If a pure diagnosis is done without intervention, there is no connection between 22 and 12 ,

3 shows an embodiment of a motor vehicle 50 with a wiring system 52 , from which with a sensor 54 at one point 56 an electrical variable or a time course of this electrical variable is recorded. This time course is a control unit 58 provided with an analog-to-digital converter 60 digitalisisert, The digitized data is provided with a radio device 62 to a cloud 64 transmitted. In this cloud 64 The digitized data representing the time course is subjected to a frequency analysis. This results in a frequency spectrum that is evaluated.

Depending on the version, the sensor 54 and / or the job 56 and / or the converter 60 and / or the radio device 62 Be part of a single controller or spread over several ECUs or run as individual devices that are not control devices.

Based on this evaluation, the control unit acts 58 on the operation of the motor vehicle 50 one. During the evaluation, data from the time domain and / or relevant digital data, eg. B. bus data, eg. A CAN bus or a LIN bus or other suitable bus system, are taken into account.

The evaluation can only be in the cloud 64 , only in the control unit 58 or both in the control unit 58 as well as in the cloud 64 be performed. The digitization can of course also in the cloud 64 be performed. If there is no connection to a cloud, then all steps of the procedure in the control unit 58 be performed. The analog-to-digital converter 60 and / or the radio device 62 can also be in the controller 58 be included.

Of importance is that the diagnosis in the operation of the vehicle 50 and also onboard if necessary. The control unit 58 is equipped as standard. There is no need for further action.

Claims (15)

Method for carrying out a diagnosis in a motor vehicle ( 50 ), in which in operation of the motor vehicle ( 50 ) a temporal course of at least one physical quantity is detected, this time profile is subjected to a frequency analysis, so that a frequency spectrum is obtained which is characterized by frequency spectrum data, the frequency spectrum data are evaluated and the evaluation in the operation of the motor vehicle ( 50 ) is taken into account.  Method according to Claim 1, in which the frequency spectrum data is linked to data from the time domain for evaluation.  Method according to Claim 1 or 2, in which the frequency spectrum data is linked to digital data for evaluation. Method according to one of claims 1 to 3, which is used for fault diagnosis.  Method according to one of claims 1 to 4, which is used for EMC monitoring.  Method according to one of claims 1 to 5, wherein the evaluation is performed with self-learning algorithms. Method according to one of claims 1 to 6, wherein at least one motor vehicle ( 50 ) internal size is recorded and evaluated. Method according to one of claims 1 to 7, wherein at least one motor vehicle ( 50 ) external variable is detected and evaluated.  Method according to one of claims 1 to 8, wherein a signature is checked.  The method of claim 9, wherein the verification of the signature is used to detect an impending failure.  Method according to one of claims 1 to 10, which is used to facilitate a FuSi or ASIL detection. Method according to one of claims 1 to 11, wherein the frequency analysis at least partially in the motor vehicle ( 50 ) is carried out. Method according to one of Claims 1 to 12, in which the frequency analysis is at least partially carried out in a cloud ( 64 ) is carried out. Arrangement for carrying out a diagnosis in a motor vehicle ( 50 ), which is adapted to operate the motor vehicle ( 50 ) to record a time course of at least one physical quantity and to consider in the operation of an evaluation of frequency spectrum data obtained by a frequency analysis of the time course. Computer program with program code means, which executes a method according to one of claims 1 to 13, when it is stored on a computer, in particular a computer in a control unit ( 58 ), comes to fruition.

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