DE102020132931A1 - Usage Profile Detection - Google Patents

Abstract

Method for determining the condition of use of a vehicle (100) or a vehicle component (101), in particular a motorcycle or a motorcycle component, comprising the following steps:a) Recording a time sequence and/or a value of at least two input variables ( 12) by a data recording unit (2) during operation of the vehicle (100),b) transmission of the recorded time sequence and/or the cumulative value of the input variables (12) from a time sequence to a computing unit (4),c) providing a vehicle-specific functional relationship (5) between the chronological sequence and/or the cumulative value of the at least two input variables (12) from a chronological sequence and at least one evaluation variable (14) of the vehicle (100) and/or vehicle component (101),d) calculation the at least one evaluation variable (14) from the transmitted chronological sequence and/or from ei ner time sequence cumulative value of the input variables (12) by means of the functional relationship (5) in the computing unit (4), e) transmission of the calculated evaluation variable (14) to a memory (6).

Description

The invention relates to a method for determining the state of use of a vehicle or a vehicle component, in particular a motorcycle or a motorcycle component, according to the preamble of claim 1. The invention also relates to a method for developing a functional relationship according to claim 18. The invention also relates to a Vehicle according to claim 19, on which the above method is carried out. The invention also includes a computer program product according to claim 20, a computer-readable data carrier according to claim 21 and a vehicle with a computer-readable data carrier on which the computer program product is stored according to claim 23. The invention also relates to a data carrier signal according to claim 22.

Methods for determining the condition of a vehicle or a vehicle component, in particular a motorcycle or a motorcycle component, are already known from the prior art. the DE 10 2014 215 258 A1 discloses a method and apparatus for automatically selecting a driving mode. In order to be able to select the driving mode automatically, the driver or the driver type is first identified. This is done using an identification device (e.g. via the vehicle key) or by analyzing driving behavior using sensors on the vehicle. The driver profile can also be learned independently, using a training set of driving information, information including driver behavior and/or state variables of the vehicle, on the one hand, and the (manual) selection of the driving mode, on the other hand. Site-specific information can also be used for evaluation.

In addition, the patent discloses EP 1 679 673 B1 a method of storing driving information from a motorcycle. In particular, the curve radius can be calculated by determining the inclination of the motorcycle from driving parameters such as the speed and rate of rotation of the motorcycle. A classification of the curves according to the curve radius is also disclosed. The stored data can be visualized on the motorcycle or transferred to a PC.

The patent specification EP 2 026 287 B1 relates to an event data recorder for recording the course of an accident for a motorcycle. Here, too, the condition of the vehicle is determined automatically.

The disadvantage of the above-mentioned methods for determining a vehicle condition is that only the current or recent condition of the vehicle is determined and no conclusions can be drawn as to the condition of use of the vehicle caused by the cumulative stress on the vehicle.

The object of the invention is to eliminate the disadvantages of the above methods and to specify a method with which it is possible to determine the state of use of the vehicle caused by the cumulative stress on the vehicle.

1. a) Aufzeichnung einer zeitlichen Abfolge und/oder eines aus einer zeitlichen Abfolge kumulierten Werts von mindestens zwei Eingangsgrößen durch eine Datenaufzeichnungseinheit während des Betriebs des Fahrzeugs, wobei es sich bei den Eingangsgrößen um einen Fahrparameter und/oder einen Fahrdynamikparameter und/oder klassifizierten und/oder komprimierten Werten von Fahrparametern und/oder Fahrdynamikparametern des Fahrzeugs und/oder des Fahrzeugbauteils handelt, wobei die Eingangsgrößen aus Sensordaten ermittelt werden und wobei die Sensordaten von mindestens einem am Fahrzeug angeordneten Sensor erfasst werden,
2. b) Übermittlung der aufgezeichneten zeitlichen Abfolge und/oder des aus einer zeitlichen Abfolge kumulierten Werts der Eingangsgrößen an eine Recheneinheit,
3. c) Bereitstellung eines fahrzeugspezifischen funktionalen Zusammenhangs zwischen der zeitlichen Abfolge und/oder des aus einer zeitlichen Abfolge kumulierten Werts der mindestens zwei Eingangsgrößen und mindestens einer Bewertungsgröße des Fahrzeugs und/oder des Fahrzeugbauteils, wobei es sich bei der mindestens einen Bewertungsgröße um eine für den Gebrauchszustand des Fahrzeugs oder des Fahrzeugbauteils repräsentative Größe handelt,
4. d) Berechnung der mindestens einen Bewertungsgröße aus der übermittelten zeitlichen Abfolge und/oder dem aus einer zeitlichen Abfolge kumulierten Wert der Eingangsgrößen mittels des funktionalen Zusammenhangs in der Recheneinheit,
5. e) Übermittlung der berechneten Bewertungsgröße an einen Speicher.

The object is achieved by a method for determining the condition of use of a vehicle or a vehicle component according to claim 1, a method for developing a functional relationship for a method according to claim 18, a vehicle according to claim 19 and 23, a computer program product according to claim 20, a computer-readable A data carrier according to claim 21 and a data carrier signal according to claim 22. Further advantageous embodiments of the invention are defined in the dependent claims. A method according to the invention for determining the condition of use of a vehicle or a vehicle component, in particular a motorcycle or a motorcycle component, comprises the following steps:

1. a) Recording a time sequence and/or a value cumulated from a time sequence of at least two input variables by a data recording unit during operation of the vehicle, the input variables being a driving parameter and/or a driving dynamics parameter and/or classified and/or compressed values of driving parameters and/or driving dynamics parameters of the vehicle and/or the vehicle component, the input variables being determined from sensor data and the sensor data being recorded by at least one sensor arranged on the vehicle,
2. b) transmission of the recorded chronological sequence and/or the cumulative value of the input variables from a chronological sequence to a computing unit,
3. c) Provision of a vehicle-specific functional relationship between the chronological sequence and/or the cumulative value of the at least two input variables from a chronological sequence and at least one assessment variable of the vehicle and/or the vehicle component, the at least one assessment variable being one for the state of use of the vehicle or the vehicle component is a representative variable,
4. d) calculation of the at least one evaluation variable from the transmitted chronological sequence and/or the cumulative value of the input variables from a chronological sequence by means of the functional relationship in the computing unit,
5. e) Transmission of the calculated evaluation variable to a memory.

The condition of use of a vehicle or vehicle component resulting from the cumulative stress on the vehicle can thus be determined using at least one evaluation variable.

A typical example of an evaluation variable is a pseudo-damage number for a vehicle component. This can be determined by means of a pseudo-damage calculation, in particular by means of a pseudo-damage calculation commonly used in strength theory.

A pseudo-damage list and/or a pseudo-damage matrix for a vehicle component can also be calculated as an evaluation variable. In this way, better resolved information about the condition of use of a vehicle and/or a vehicle component can be stored. For example, damage can be resolved depending on the parameter range, damage caused by driving on a certain type of route and/or damage caused by a certain driving style.

• - Ermittlung von mindestens einem kumulierten Wert aus einer zeitlichen Abfolge von Eingangsgrößen zur Bestimmung von Belastungsamplituden
• - Beziehen des mindestens einen kumulierten Werts auf einen weiteren funktionalen Zusammenhang zwischen Belastungsamplituden und Haltbarkeit des Fahrzeugs und/oder des Fahrzeugbauteils.

In the pseudo damage calculation, the condition of use of a vehicle and/or a vehicle component can be determined in the following steps:

• - Determination of at least one cumulative value from a time sequence of input variables for determining load amplitudes
• - Relating the at least one cumulative value to a further functional relationship between load amplitudes and durability of the vehicle and/or the vehicle component.

The at least one cumulative value can be determined by storing the dwell time in parameter areas. However, it can also be determined using counting methods known from strength of materials, such as the rainflow method.

The further functional relationship between load amplitudes and the durability of the vehicle and/or the vehicle component is usually determined by a fictitious Wöhler curve with a slope. The Wöhler curve establishes a functional connection between a load amplitude of load cycles and the number of load cycles that can be endured.

The cumulative values determined can correspond directly to the load amplitudes. However, the load amplitudes can also be calculated from the determined cumulative values. For example, a load amplitude of the valves in the engine can be estimated from the engine speed.

The assessment variables determined can be comparison variables which have to be compared with a reference value in order to be meaningful. For example, a pseudo-damage number of a vehicle and/or a vehicle component, which was determined during operation of the vehicle, can be compared with a pseudo-damage number, which was determined by vehicle and/or component tests.

Driving parameters are variables that describe a state of the vehicle per se, for example the gear engaged, the engine speed and/or the engine torque of the engine, or also the steering position or the throttle grip position.

Vehicle dynamics parameters are variables that describe the vehicle in relation to its surroundings, for example acceleration, speed, (lateral) inclination and/or the mass of the load. A location of the vehicle, a route type, a driving surface and driving ability and driving style can represent driving dynamics parameters or classified and/or compressed values of driving dynamics parameters. Vehicle dynamics parameters can often be derived from driving parameters, for example the speed of the vehicle can be determined from a measurement of the rotational speed of a wheel, a driving parameter.

The chronological sequence of the input variables can correspond directly to sensor data. For example, a tachometer can measure a speed profile, with the speed then corresponding to the input variable.

The chronological sequence of the input variables can be calculated from sensor data. For example, an acceleration can be calculated from a speed over time. However, it is also possible for one or more acceleration sensors to be provided, which measure the acceleration directly.

Calculations for the composition of input variables from sensor data can be made using multiple sensors.

The mass of the load can be calculated from the compression of the front and rear wheel suspension, for example. The mass of the load can also be calculated using an algorithm from the variables incline, acceleration and deceleration.

The lateral inclination of a motorcycle can be calculated from speed and steering angle, for example, or it can be measured directly using the vehicle's own inclination sensors. The distance to a vehicle driving ahead can be calculated, for example, by analyzing images from a camera or by distance sensors.

The calculation of input variables, which are composed of sensor data, can be carried out in at least one control unit. For example, the calculation of the mass of the load from the compression of the front and rear wheel suspension can be carried out in a spring control unit responsible for regulating the spring.

The inputs may include classified values. With a chronological sequence of input variables, a classification can be carried out at any point in time and/or a selection of points in time and/or in a running time window.

For example, a curve traveled by the vehicle can be classified using a number of criteria. A simple classification can contain the information as to whether a curve is present at all. The property "existence of a curve" can therefore have the values "yes" or "no". A minimum lateral slope and minimum curve length may be required for the presence of a curve.

If there is a curve, this curve can be classified according to various criteria after driving through the curve and recording the associated times. Examples of such a criterion are the curve radius with the exemplary values "less than 50 meters", "between 50 meters and 200 meters" and "greater than 200 meters", the curve length with the exemplary values "less than 100 meters", "between 100 meters and 200 meters” and “longer than 200 meters” and the slope of the curve with the example values “less than 3 degrees”, “flat” and “more than 3 degrees”. In addition, a criterion can be the direction of the curve, with the values "left" and "right".

The curve length can be calculated from the mean rotational speed of the rear wheel. The curve radius can be calculated from the lateral inclination and the turning speed. For this purpose, it is provided that the torque balance of the vehicle is assumed, with the torque being balanced by the weight force and the torque by the centrifugal force. The curve radius then corresponds to the cotangent of the slope times the speed squared by the acceleration due to gravity.

The slope of the curve can be determined by directly measuring the mean pitch of the vehicle.

The route type can also be classified. The "Route Type" can have multiple properties, such as "Freeway", "Country Road", "City" and/or "Rough Road".

The route type can be classified using a decision tree. Multiple decision rules based on limits for quantities such as speed, grade, and acceleration classify the track type. It can be provided that the classification is not carried out in every time step, ie the decision tree is not run through in every time step. Instead, the necessary quantities can be estimated by averaging several time steps.

The driving surface driven on can also be classified, for example the values “road”, “gravel”, “stone” and/or “sand” can be assigned to a driving surface.

If the slip between the front wheel and rear wheel, the difference between the speed of the front wheel and the speed of the rear wheel, and/or the spread of the speed of the front wheel or the rear wheel exceeds a certain limit value, the driving surface class can be changed from "road" to "gravel". “ to be changed.

In a further embodiment, several limit values can be present, with a higher limit value being used for a change in the driving surface class to “stone” and an even higher limit value for a change to “sand”. The slip and the above-mentioned scatter depend on the speed, which is why the absolute speed is preferably calculated out.

With the classification of the driving surface, a selection of the operating mode of the vehicle. For example, engine settings and chassis settings can be selected automatically depending on the driving surface or made available for selection by the driver.

The driving behavior can also be classified with regard to the criteria of driving style and driving ability. Exemplary values for driving style are "Sport", "Street" and/or "Comfort" and for driving ability "Beginner", "Standard" and/or "Pro".

For the classification of driving style and/or driving ability, it can be determined how large the maximum inclination angle is in a specific classified curve type. A greater angle of inclination can, for example, indicate an experienced and/or sporty driver (driving ability “professional” and/or driving style “sport”). In this case, average values of the maximum inclination angles can be formed for each curve type, for example.

For the classification of driving style and/or driving ability, the driving ability and/or driving style can be inferred from the discrepancy between the average values of the maximum inclination angles in a specific curve type and/or route type of a driver to a set of reference values. The set of reference values can come, for example, from test drives by a professional and/or from a mass of customer data and/or can be compiled manually. In addition, the drivers can compare their maximum inclination angles afterwards.

Other input variables can also be used to classify driving style and/or driving ability, for example the interaction of throttle grip position and rear wheel speed.

For the classification of driving style and/or driving ability, it can be determined how fast which classified curve type is driven through. Driving through tight corners quickly can, for example, indicate an experienced and/or sporty driver (driving ability “professional” and/or driving style “sport”). In this case, average values can be formed for each curve class, for example. Drivers can compare their (average) cornering speeds afterwards.

For the classification of driving style and/or driving ability, it can be determined how the braking behavior is designed depending on the driving surface. This allows for the fact that braking maneuvers on loose driving surfaces are generally more difficult and, for example, cause the rear wheel to lock even in the case of advanced drivers.

Correction factors for the evaluation variables can be determined from the determined driver profile. In particular, correction factors for pseudo-damage numbers can be determined. For example, it can be provided that the pseudo-damage number is corrected upwards for the “professional” driving ability and the pseudo-damage number is corrected downwards for the “beginner” driving ability.

The determined driver profile can also be used to generate preventive warnings, which, for example, call for more careful driving behavior. Safety warnings can also be generated if, for example, a driver is driving too fast for his driving skills.

Sensor data can be used directly for classification and/or compression, with the classified values being stored on the data recording unit after the curves have been classified. Additionally or alternatively, input variables from the data recording unit can be used for classification and/or compression.

A classification can be performed by a classification unit. A data processing unit can be used for this purpose, for example, by which input variables and/or sensor data are also determined. The classified values can be stored in the data recording unit. The classification unit and the computing unit can be designed as a physical and/or logical unit.

Compression can be done by a compression unit. A data processing unit can be used for this purpose, for example, by which input variables and/or sensor data are also determined. The compressed values can be stored in the data recording unit. The compression unit and the arithmetic unit can be designed as one unit.

Location-specific information, preferably from a GPS device attached to the vehicle, can be used as input variables or for validation. For example, a driving surface can be determined by sensor data, with the driving surface then being validated using location-specific data and a map. It can be checked whether the map shows a road with such a driving surface.

The transmission of the input variables from a sensor and / or a control unit to the Data recording unit can take place by means of a bus system, in particular a CAN bus. Provision can be made for the data recording unit to be able to read out stream data from the CAN bus in real time.

The data can be recorded in compressed form, for example as a cumulative value of a chronological sequence. A chronological sequence of at least two input variables from certain value ranges of the input variables can be stored as a frequency value. In the case of multidimensional histograms, the frequencies of certain value ranges of several input variables are stored, which means that a temporal correlation between the input variables is retained.

The data can be recorded in the form of a dwell time matrix. A dwell time matrix essentially corresponds to a histogram with a constant recording rate. Multi-dimensional residence time matrices, preferably two- or three-dimensional residence time matrices, can also be stored.

Data processed in this way can be converted into a damage matrix with the same format as the dwell time matrix and/or the histogram by means of a formal relationship, with the damage matrix corresponding to an assessment variable, the assessment variable being one for the condition of use of the vehicle and/or the vehicle component representative size. The formal relationship provided by the processing unit can correspond here, for example, to the component-by-component multiplication with a weighting matrix, which weights and possibly normalizes the elements of the dwell time matrix. The weight matrix can be created manually or automatically.

A damage matrix can be designed as a pseudo-damage matrix, for example. In this case, the formal relationship provided by the computing unit can correspond to a formula derived using the pseudo-damage calculation. A comparison of the pseudo-damage matrix with a reference matrix can also already be carried out by the computing unit.

In general, according to the invention, input variables are correlated and/or compared with one another in a processed, for example compressed, classified and/or cumulative and/or unprocessed form, in order to draw conclusions about the state of use using at least one evaluation variable.

In one exemplary embodiment, it is provided that the method relates to a vehicle with a chassis, the input variables being the mass of a driver and/or the mass of a passenger and/or the mass of luggage being transported. The evaluation variable is a variable that is representative of the condition of use of the chassis, preferably the front wheel suspension and/or the rear wheel suspension of a motorcycle

The measurement of the mass of a driver and/or a passenger and/or transported luggage can be carried out via a spring deflection measurement. Measuring strips, sensors and/or cylindrical capacitors can be used here. Alternatively, the mass can be estimated using an algorithm in the controller from measurement data such as inclination, acceleration and/or deceleration.

It can be provided that at least one of the input variables is an acceleration of the vehicle, preferably a longitudinal acceleration of the vehicle, and/or a speed of the vehicle, and that at least one of the evaluation variables is one for the state of use of the vehicle Chassis representative size, preferably relating to the front suspension and / or the rear suspension of a motorcycle is.

In one embodiment, it can be provided that the method relates to a vehicle with an engine, in particular an internal combustion engine, where the input variable is the engine speed and/or the engine torque of the engine, and where at least one of the Evaluation variables is a representative size for the condition of use of the engine or at least one component of the engine.

In one embodiment, it can be provided that the method relates to a vehicle with a transmission, with at least one of the input variables being an engaged gear of the transmission, and with at least one of the evaluation variables being one for the state of use of the Transmission representative size.

Further input variables can be recorded, with the further input variables not necessarily being used to calculate the at least one evaluation variable using the computing unit.

The other input variables can be used to control the computing unit. For example, depending on the value and/or the class of a further input variable, a specific functional relationship between the time sequence and/or the cumulative value from the time sequence of the at least two input variables and the at least one evaluation variable can be selected.

The additional input variable can be an indicator variable specifically for the driver of the vehicle. For example, a driver can be identified using his personal vehicle key.

The functional relationship can be selected specifically for a driver of the vehicle. Different functional relationships can thus be provided for different drivers. For example, the functional relationship can be tailored to the driving ability and/or the driving style of the driver. In this case, a further input variable, an indicator variable for the driver, is used to control the computing unit.

The other input variables can be forwarded separately to the memory and saved alongside the evaluation variables. In this way, a correlation and/or a comparison between the other input variables and the state of use of the vehicle can subsequently be established.

The driving ability and/or the driving style can be correlated and/or compared with the state of use of the vehicle by calculating evaluation parameters and a driver profile. For example, it can be analyzed whether vehicles driven by beginners are damaged more than vehicles driven by advanced drivers. Different functional relationships can also be provided for different driving styles and/or driving skill levels.

It can be calculated to what extent the use of the vehicle on different routes correlates with the damage to the vehicle. For example, it can be determined whether using the vehicle in the city is more harmful than using the vehicle on a country road, or to what extent a type of vehicle is driven off-road.

The driving parameter can be location-specific information, preferably location data and/or data relating to the driving surface and/or the type of road traveled. Location data is typically made available to the vehicle via a GPS device. Data relating to the driving surface and/or the type of road traveled can be calculated from driving parameters. The conclusions about the driving surface obtained in this way can be validated using location data.

The input variable can be a slip between a front wheel and a rear wheel of the vehicle. Since different levels of slip are to be expected on roads with a loose driving surface, such as gravel, stone and/or sand, conclusions can be drawn about the driving surface from the slip. For example, a classification can be made using predefined limit values.

The chronological sequence of at least one further input variable can be recorded and an evaluation variable specific to the driving style and/or the driving ability of the driver can be calculated in the computing unit.

In one embodiment, at least the (lateral) pitch of the vehicle is recorded during cornering. With this, an evaluation variable specific to the driving ability of the driver can be calculated based on the average inclination during cornering and/or the average speed during cornering. A curve type is preferably assigned to at least one curve and an average value of the maximum slope in the curve is recorded for each curve type. More preferably, the average value of the maximum slopes is updated after each curve.

• - Analyse des Fahrzeugs für den Hersteller für die Entwicklung der nächsten Generation. Insbesondere kann die fahrzeugspezifische Beanspruchung für die Auslegung von Bauteilen herangezogen werden.
• - Analyse des Fahrzeugs für den Hersteller für Ersatzteilbestellungen
• - Analyse des Fahrzeugs für den Fahrer, beispielsweise durch eine App auf einem mobilen Endgerät, für die private Fahrzeugkontrolle
• - Benachrichtigung für Serviceempfehlungen
• - Fahrzeugbewertung für den Wiederverkauf
• - Überprüfungen in Versicherungs- oder Garantiefällen

Assessing the condition of use of a vehicle can include the following applications:

• - Analysis of the vehicle for the manufacturer for next generation development. In particular, the vehicle-specific stress can be used for the design of components.
• - Analysis of the vehicle for the manufacturer for spare parts orders
• - Analysis of the vehicle for the driver, for example using an app on a mobile device, for private vehicle checks
• - Notification for service recommendations
• - Vehicle valuation for resale
• - Checks in insurance or guarantee cases

In contrast to earlier methods, the stress on a vehicle or a vehicle component can be determined with the method according to the invention.

• - Analyse des Fahrkönnens für den Fahrer zur Selbstoptimierung und zum Vergleich mit anderen Fahrern
• - Analyse der Verwendung eines Fahrzeugs für zielgenaues Marketing des Herstellers
• - Regelung des Fahrzeugs in Echtzeit, wie beispielsweise eine Dämpferanpassung je nach Fahruntergrund
• - Rekonstruktion eines Unfallhergangs
• - Erzeugen einer Visualisierung, beispielsweise einer kommenden Kurve, aufgrund von Nutzungsdaten, vorzugsweise bei mehrmaligem Fahren einer Strecke
• - Warnung bei einer detektierten Ermüdung eines Fahrers

In addition to evaluating the condition of use, other tasks can also be carried out with parts of the present method:

• - Analysis of the driver's driving skills for self-optimization and comparison with other drivers
• - Analysis of the use of a vehicle for targeted marketing by the manufacturer
• - Real-time control of the vehicle, such as damper adjustment depending on the driving surface
• - Reconstruction of the course of an accident
• - Generating a visualization, for example an upcoming curve, based on usage data, preferably when driving a route several times
• - Warning when driver fatigue is detected

The processing unit can be arranged in the vehicle. It can be provided that the computing unit corresponds to part of the control unit of the vehicle or represents part of the control unit. This dual function saves space and wiring effort on the vehicle.

The recorded chronological sequence of the input variable(s) can additionally or alternatively be transmitted to a processing unit that is separate from the vehicle. For example, the processing unit can be arranged in an external computer network (cloud), a computer and/or a mobile terminal device belonging to the driver and/or the manufacturer.

The evaluation variable can be transmitted to an external device, preferably a mobile device such as a smartphone. In this way, a driver can call up evaluated information, preferably the state of use, of his vehicle using an app on his smartphone, for example.

The term processing unit can be understood logically, in particular the processing unit does not have to be a physical unit. However, it can also be provided that the processing unit corresponds to a physical device. The situation is analogous with the data processing unit and the data recording unit.

The transmission of the calculated evaluation variables and/or input variables to a memory can be wireless and/or wired. The transmission can also be carried out manually, for example using a data carrier such as a USB stick.

The functional relationship for evaluating a usage status can be deterministic and/or stochastic.

The functional context for evaluating a condition of use can be developed on the basis of expert knowledge, for example experienced designers.

The functional context for evaluating a condition of use can be developed on the basis of data recorded on vehicles. Typically, data from a large number of vehicles and/or vehicle components are available for this purpose.

The functional relationship for evaluating a condition of use can be determined using a pseudo-damage calculation.

1. a) Aufzeichnung einer zeitlichen Abfolge und/oder ein aus einer zeitlichen Abfolge kumulierter Wert von mindestens zwei Eingangsgrößen durch eine Datenaufzeichnungseinheit während des Betriebs mindestens eines Fahrzeugs, wobei es sich bei den Eingangsgrößen um einen Fahrparameter und/oder einen Fahrdynamikparameter und/oder klassifizierten und/oder komprimierten Werten aus Fahrparametern und/oder Fahrdynamikparametern des mindestens einen Fahrzeugs und/oder mindestens eines Fahrzeugbauteils handelt, wobei die Eingangsgrößen aus Sensordaten ermittelt werden und wobei die Sensordaten von mindestens einem am Fahrzeug angeordneten Sensor erfasst werden,
2. b) Aufzeichnung mindestens einer Bewertungsgröße, wobei es sich bei der Bewertungsgröße um eine für den Gebrauchszustand des mindestens einen Fahrzeugs und/oder des mindestens einen Fahrzeugbauteils repräsentative Größe handelt, und wobei die mindestens eine Bewertungsgröße mittels mindestens einem am mindestens einen Fahrzeug und/oder am mindestens einen Fahrzeugbauteil angeordneten Sensor erfasst wird und/oder mittels eines Computermodells simuliert wird und/oder manuell eingegeben wird und/oder aus einer Datenbank geladen wird,
3. c) Ermittlung einer Korrelation zwischen der mindestens einen aufgezeichneten Eingangsgröße und der mindestens einen aufgezeichneten Bewertungsgröße.

A method according to the invention for developing a functional relationship for a method according to at least one of the preceding claims comprises the following steps:

1. a) Recording of a time sequence and/or a value cumulated from a time sequence of at least two input variables by a data recording unit during the operation of at least one vehicle, the input variables being a driving parameter and/or a driving dynamics parameter and/or classified and/or or compressed values from driving parameters and/or driving dynamics parameters of the at least one vehicle and/or at least one vehicle component, the input variables being determined from sensor data and the sensor data being recorded by at least one sensor arranged on the vehicle,
2. b) Recording of at least one assessment variable, the assessment variable being a variable that is representative of the condition of use of the at least one vehicle and/or the at least one vehicle component, and the at least one assessment variable using at least one on the at least one vehicle and/or on sensor arranged at least one vehicle component is detected and/or is simulated by means of a computer model and/or is entered manually and/or is loaded from a database,
3. c) determining a correlation between the at least one recorded input variable and the at least one recorded evaluation variable.

A functional relationship for determining at least one evaluation variable from input variables can thus be learned automatically.

The recorded evaluation variables can originate from sensors on the vehicle and/or on the vehicle component and essentially correspond to them or have already been processed further.

Evaluation variables can be variables that directly represent the condition of use of a vehicle component. With regard to the stress on a spring element, for example, the immersion behavior of a spring element can be measured as an assessment variable and this can be correlated in a time-resolved manner with input variables such as the load mass.

The recorded evaluation variables can be determined by means of stress tests on the vehicle and/or a component.

The recorded ratings can be simulated using a computer model. The computer model can simulate damage models for certain input variables, with the damage models containing evaluation variables which are then correlated and/or compared with the input variables used. The typically resource-intensive simulations then do not have to be carried out in order to carry out the method for determining the state of use.

The recorded evaluation values can be entered manually by a driver or at the factory, for example after an inspection of the state of use of the vehicle or a vehicle component, or can be transferred by means of an update.

The recorded rating variables can be loaded from a database. For example, evaluation variables from past factory test runs can be loaded.

A machine learning method, for example a neural network, decision trees and/or linear regression, can be used to establish a correlation between the recorded input variable and the recorded evaluation variable.

A computer program product according to the invention comprises instructions that cause a vehicle to operate a method described above.

A computer program product according to the invention is stored on a computer-readable data carrier according to the invention.

According to the invention, a data carrier signal is provided which transmits a computer program product.

A vehicle according to the invention has a stored functional relationship for a method described above.

A vehicle according to the invention can have a computer-readable data carrier on which a computer program product described above is stored.

The vehicle may include at least one programmable controller.

• 1 Blockdiagramm des Verfahrens zur Ermittlung des Gebrauchszustands
• 2 Blockdiagramm mit mehreren Datenaufbereitungseinheiten
• 3a-c Speicherung von Motordaten in einer a. Verweildauerliste, b. zweidimensionalen und c. dreidimensionalen Verweildauermatrix
• 4a-c Pseudo-Schädigungszahl in a. einer Liste und b. einer zweidimensionalen Matrix und c. Eingriffsgrenzen einer Pseudo-Schädigungszahl
• 5a,b Klassifizierung des Streckentyps, a. Aufzeichnung einer zeitlichen Abfolge der Geschwindigkeit, b. Klassifizierung mittels Klassengrenzen
• 6 Klassifizierung des Streckentyps mittels Entscheidungsbaum
• 7 Klassifizierung des Schlechtwegtyps
• 8a-c Klassifizierung des Fahrkönnens mittels Neigungswinkelprofil, a. Profi, b. Standard, c. Anfänger
• 9a,b Häufigkeitsverteilung von longitudinaler und lateraler Beschleunigung dargestellt im Kamm'schen Kreis

Preferred exemplary embodiments can be found in the following figures.

• 1 Block diagram of the method for determining the state of use
• 2 Block diagram with multiple data preparation units
• 3a-c Storage of engine data in a a. length of stay list, b. two-dimensional and c. three-dimensional dwell matrix
• 4a-c Pseudo damage number in a. a list and b. a two-dimensional matrix and c. Control limits of a pseudo-damage number
• 5a,b Route type classification, a. Recording a time sequence of the speed, b. Classification using class boundaries
• 6 Classification of the route type using a decision tree
• 7 Rough road type classification
• 8a-c Classification of driving ability using the inclination angle profile, a. pro, b. standard, c. Beginner
• 9a,b Frequency distribution of longitudinal and lateral acceleration shown in Kamm's circle

the 1 shows a block diagram of an embodiment of the method according to the invention for determining a usage state of a vehicle 100 and/or a vehicle component 101.

One or more sensors 1 arranged on vehicle 100 determine driving parameters and/or driving dynamics parameters of a vehicle 100 and/or a vehicle component 101 and output them as sensor data 11 . A data recording unit 2 records at least two input variables 12 , it being possible for the input variables 12 to correspond to the sensor data 11 . However, the input variables 12 can also be derived from sensor data 11 , that is to say they can be calculated from sensor data 11 . These processed data 13 are also in 1 apparent.

A data processing unit 3 can be arranged between the sensors 1 and the data recording unit 2 for processing sensor data 11 . The processed data 13 can then be forwarded to the data recording unit 2 . The data processing unit 3 can be designed as a classification unit 31 for classifying the sensor data 11, as a compression unit 32 for compressing sensor data 11 and/or as a calculation unit 33 for deriving sensor data 11 by means of a functional relationship.

Like in the 2 shown, several data preparation units 3 can be present. The data processing units 3 can be located before and/or after the data recording unit 2 in the information flow. At least one data processing unit 3 can also be located in the data recording unit 2 and/or can be controlled by the data recording unit 2, so that data can be sent from the data recording unit 2 to the data processing unit 3 and back.

A data processing unit 3 can be embodied as a calculation unit 33 for deriving sensor data 11 by means of a functional relationship and/or can be arranged between a sensor 11 and the data recording unit 2 . The mass of a driver can thus be calculated, for example, from sensor data 11, for example originating from the chassis of the vehicle.

A data processing unit 3 can be arranged in the data recording unit 2 and/or be responsible for the compression of the sensor data 11 or input variables 12 that have already been recorded. It is thus designed as a compression unit 32 . For example, a dwell time list and/or a multi-dimensional dwell time matrix can be determined from sensor data 11 such as engine speed n, gear G and engine torque M, with the dwell time matrix representing a compressed form of the data. A collective load can also be determined using counting methods known from strength theory.

A data processing unit 3 can receive data from the data recording unit 2 , process it and send it back to the data recording unit 2 . This data preparation unit 3 can be in the form of a classification unit 31, for example. Input variables 12 recorded in the data recording unit 2, such as the incline and the rear wheel speed, can be used in this data processing unit 3 for classifications, such as determining the curve type KT.

A data processing unit 3 can also be arranged in the information flow after the data recording unit 2, for example between the data recording unit 2 and the computing unit 4. In the exemplary embodiment in 2 this is designed as a calculation unit 33 .

Input variables 12 are present in data recording unit 2, input variables 12 being driving parameters and/or driving dynamics parameters and/or classified and/or compressed values of the driving parameters and/or driving dynamics parameters of vehicle 100 or vehicle component 101.

In a further step, a chronological sequence and/or a value of the input variables 12 cumulated from a chronological sequence is transmitted to a computing unit 4 . A vehicle-specific functional relationship 5 between the at least two input variables 12 and at least one assessment variable 14 for the state of use of the vehicle 100 or vehicle component 101 is provided in the computing unit 4 .

Further input variables 12 can also be used to control the arithmetic unit 4 . For example, a functional relationship 5 specific to the respective driver can be selected.

The processing unit 4 is arranged here in an external computer network 8, for example a cloud. However, the processing unit 4 can also be arranged on the vehicle 100 . This is in the 1 indicated by a dashed rectangle.

The evaluation variables 14 can be transmitted to a memory 6. Additional input variables 15 can also be transmitted directly to memory 6, as can be seen from FIG 1 apparent.

The memory 6 can be in the vehicle 100 and/or in an external computer network 8 (cloud). The memory 6 can also be in the form of a database, with the evaluation variables 14 and optionally further input variables 15 being stored in a database. The memory 6 can also be arranged with the end user 10, for example with the driver and/or with the manufacturer of the vehicle 100.

An end-user unit 9 can access the memory 6 and display and/or re-evaluate evaluation variables 14 and/or input variables 12 and/or other input variables 15 . The end user unit 9 can be in the form of a mobile terminal device 91 and/or a computer 92 and/or an internal computer network 93, for example a manufacturer's company network and/or a PC for a driver of the vehicle 100.

the 3a until 3c show an exemplary embodiment of the storage of engine data, in particular sensor data 11 of the engine, in a dwell time list 20 and/or dwell time matrix 21. This compressed storage can be carried out by a compression unit 32, for example.

the 3a shows a graphical representation of a dwell time list 20 for the engine speed n. The dwell time list 20 contains the respective dwell time T in a plurality of, preferably disjoint, engine speed ranges B(n). Seven engine speed ranges B(n), numbered one through seven, are shown here. These preferably cover the entire achievable engine speed range B(n) of the engine. For example, the engine speed ranges B(n) may include the following intervals in revolutions per minute units: 0 to 2000, 2000 to 4000, 4000 to 6000, 6000 to 8000, 8000 to 10000, 10000 to 12000, 12000 to 14000.

In order to determine the dwell time list 20 , sensors 1 on the vehicle 100 can determine a chronological sequence of sensor data 11 of the engine speed n and forward this to a data processing unit 3 . The data processing unit 3 counts the number of values that fall within an engine speed range B(n). This creates a frequency distribution which, given a constant data rate, can be converted into a dwell time list. This calculation can be performed in real time.

the 3b shows a two-dimensional dwell time matrix 21 for engine speed n and engine torque M. In the representation in FIG 3b the gray level encodes the dwell time T, with a dark field corresponding to a long dwell time T and a light field to a short dwell time T. The engine speed n is here as in the 3a divided into seven engine speed ranges B(n). Here, the engine torque M is divided into four engine torque ranges B(M). For example, the engine torque ranges B(M) may correspond to the following ranges in units of Newton meters: 0 to 65, 65 to 100, 100 to 115, greater than 115.

As in the one-dimensional case of the 3a a two-dimensional frequency distribution is generated by measuring a time sequence of sensor data 11 of engine speed n and engine torque M and converted into a dwell time T. A correlation is preserved in the off-diagonal matrix elements.

the 3c shows a three-dimensional dwell time matrix 21 for the engine speed n, the engine torque M and the gear G. The continuous variables engine speed n and engine torque M are again divided into areas; this is not necessary for the discrete gear G anyway. The length of stay matrix 21 is calculated from a discrete, three-dimensional frequency distribution. Of course, higher-dimensional length of stay matrices and/or frequency distributions are also conceivable.

A dwell time matrix 21 can be used to infer the state of use of the vehicle 100 and/or a vehicle component 101 with accuracy.

A damage matrix can be calculated with the residence time matrix 21, for example by multiplying the components by a weighting matrix. The weighting matrix can indicate, for example, the extent to which a long stay in a certain parameter range leads to damage.

The component-by-component multiplication with a weighting matrix represents a functional relationship 5 between the length of stay matrix 21 as an input variable 12 and the damage matrix as an evaluation variable 14.

A weighting matrix can be predefined and/or determined during operation of the vehicle on the basis of measurements. A weighting matrix can be determined using a pseudo-damage calculation.

the 4a and 4b show results of a pseudo-damage calculation in the form of a pseudo-damage list 22 and a pseudo-damage matrix 23. The 4a shows pseudo damage numbers in the form of a pseudo Damage list 22 of several engine speed ranges B(n). the 4b shows pseudo-damage numbers in the form of a pseudo-damage matrix 23 as a function of engine speed n and engine torque M.

A damage number D of a component for a specific load amplitude S can be defined as the ratio of the number of load cycles N to the number of endurable load cycles Nmax at the load amplitude S, D=N/Nmax. A component failure is therefore predicted for D = 1. However, the number of tolerable load cycles Nmax is often not directly known.

In the pseudo-damage calculation, a functional relationship between the number of tolerable load cycles Nmax at a certain load amplitude S is assumed. For example, it can be assumed that the number of load cycles Nmax that can be endured is proportional to the load S to the power of k (Basquin equation), with the positive number k corresponding to the Wöhler gradient known from strength theory and representing a material property. This relationship is approximately valid in the fatigue strength range of a metallic component. A Wöhler slope can be determined, for example, using material tests, known material properties, standards and/or estimates.

By means of the functional relationship, the number of tolerable load cycles Nmax can be estimated with knowledge of the load amplitude, and one gets an estimated number of tolerable load cycles Nmax* actual, as in the case of the Basquin equation mentioned above, the functional relationship is only known as the proportionality relation an estimated number of tolerable load cycles Nmax* is calculated, which is only suitable as a comparison value.

A pseudo-damage number of a component at a certain load amplitude can be defined as the ratio of the number of load cycles N to the number of tolerable load cycles Nmax* at the load amplitude S estimated using functional relationships, PSD = N / Nmax* Using the Basquin equation, a Pseudo-damage number PSD can then be defined, for example, as the (actual) number of stress cycles N times the stress amplitude S to the power of k, PSD=N*S^k. Since the Basquin equation is merely a proportionality relation, the pseudo-damage number defined in this way is only suitable as a comparison value with another pseudo-damage number calculated in the same way. In particular, it does not correspond to an absolute estimate, so that it cannot be expected that a component failure will occur at PSD = 1.

A pseudo-damage number determined through component tests can thus be compared with a pseudo-damage number from vehicle operation in order to obtain information about the state of use of a vehicle and/or vehicle component.

In order to calculate a pseudo-damage number, be it during a component test or when driving, it may be necessary to determine a collective load. In particular, the (actual) number of load cycles N should be known depending on the load amplitude S, for example N as a function of S. This can be done by transforming a load-time function into a load collective using known methods of strength of materials, in particular using counting methods such as the rainflow method , to be accomplished.

For example, the load amplitude is divided into classes which are defined, for example, by certain value ranges. It can be counted how many load cycles are run in a certain load amplitude class.

In the 4a a pseudo-damage list 22 of the damage to the valves in the engine as a function of the engine speed n is given as an example. The load amplitude S is the stress in the area of the valves caused by the movement of the piston. This is proportional to the force acting on the piston, and the force is in turn proportional to the acceleration of the piston in the area of the valves, with the valves being at the reversal point. The acceleration of the piston at the reversal point is in turn proportional to the engine speed n squared. A load amplitude S relevant to the valves is therefore proportional to the square of the engine speed n.

When the values are recorded by the data recording unit 3, the engine speed n is divided into engine speed ranges and counted by the data processing unit 3 while an engine speed range is staying. The (actual) number of load cycles N corresponds to the engine speed n times the dwell time T in this engine speed range B(n).

A pseudo-damage number of the valves in the engine can therefore be defined as the engine speed n squared to the power of the S-N k [n^(2k)] times the number of load cycles N=n*T, PSD = n^(2k)*n* T This calculation provides the functional context 5 for determination a condition of use of a vehicle part 101, the valves.

This calculation can also be performed as a component-wise multiplication of the weight list n^(2k+1) times the dwell list T (e.g. from 3a) be seen. Of course, multidimensional configurations are also conceivable, in which case a component-by-component matrix multiplication would then be used.

the 4b Figure 12 shows a two-dimensional example of a pseudo-damage matrix 23. The shades of gray encode a pseudo-damage number for the load on the gearbox, with darker areas indicating more damage and lighter areas indicating less damage.

A load amplitude S relevant for the transmission is the engine torque M. The engine speed n is also of interest for determining the (actual) number of load cycles. A two-dimensional dwell time matrix 21 is therefore determined from sensor data 11, which has engine torque ranges B(M) and engine speed ranges B(n) as axes. It is therefore counted how long a certain engine torque M and a certain engine speed n are dwelled on.

A pseudo-damage number of the transmission can be defined as engine torque M raised to the power of Wöhler slope k [M^k] times the number of load cycles N=n*T, PSD = M^k * n*T. This calculation establishes a functional relationship 5 for determining a usage state of a vehicle part 101, the transmission. The calculation can be carried out as a component-wise multiplication of the weighting matrix M^k*n with the dwell time matrix T (e.g. from 3b) be seen.

In the 4a and 4b the load amplitudes S are not measured directly, but calculated from other variables obtained from sensor data 11 .

In the 4a and 4b Pseudo-damage numbers have been described which can be calculated for a specific load amplitude, with which a pseudo-damage number can be calculated for each load amplitude or load amplitude class. However, it can also be provided that a total pseudo-damage number for a component is calculated by summing up several pseudo-damage numbers, for example using the linear damage accumulation hypothesis known from strength theory.

The condition of use of the vehicle 100 and/or a component 101 can be estimated by comparison with pseudo-damage numbers calculated in the same way from vehicle and/or component tests PSD*. For example, the ratio PSD/PSD* can be formed.

As in 4c shown, it can be provided, for example, that below a value PSD ₁ of a pseudo-damage number, only the pseudo-damage numbers themselves are monitored and/or above the value PSD ₁ of a pseudo-damage number, CAN signals are additionally monitored directly. In the 4c the value PSD ₁ is reached at a time t ₁ . At a point in time t ₂ the component has a defect.

If further input variables 15, such as a driver profile and/or an indicator value for the model of vehicle 100, are stored with the damage matrix in memory 6, the state of use of vehicle 100 can be compared for the further input variables 15, for example specifically for a driver and/or a vehicle model.

the 5a until 5b concern the classification of the route type ST. The classification of the route type ST is carried out by the data preparation unit 3, which is in the form of a classification unit 31.

the 5a shows a time profile 24 of an input variable 12, here the speed v over the distance s.

To classify the route type ST based on the speed v, the route can be subdivided into route sections. In each route section, it is determined within which predefined class limits 26 the mean value of the speed v is located. A respective route area can thus be assigned to a route type ST, and a time sequence 25 of a classification results.

High speed indicates a highway route type (ST = 3), medium speed indicates a rural route type (ST = 2), and low speed indicates a city route type (ST = 1). .

The route type ST cannot only be based on a single input variable 12 and/or sensor data 11 from a sensor 1, such as the speed v in 5a and 5b , to be grouped. A more precise classification can be made using a decision tree 28 as in 6 be reached.

In 6 it is first checked whether the sensor noise r of the rear wheel speed is above or below a predefined class limit 26. This is used to determine whether driving is on a road or a rough road (ST=0).

In a next step, the speed v can be assessed. If this is less than 50 km/h, the route type "City" (ST = 1) can be inferred. Otherwise the speed v is examined again.

If the speed v is greater than 100 km/h, in a final step it is determined on the basis of the angle of inclination α whether the route type is “country road” or “motorway”. If the angle of inclination α is greater than a predefined class limit 26, the route type is “country road”. If the angle of inclination α is less than a predefined class limit 26, the route type is “motorway”.

If the speed v is less than 100 km/h, in a final step it is determined on the basis of the angle of inclination α whether the route type is “country road” or “city”. If the angle of inclination α is greater than a predefined class limit 26, the route type is “country road”. If the angle of inclination α is less than a predefined class limit 26, the “city” route type is present.

the 7 shows a class diagram 27 for classifying the driving surface based on two input variables 12: the sensor noise of the rear wheel r and the speed v. Three class boundaries 26 divide the parameter space into four classes: "Road" (OT=0), "Gravel" (OT=1), "Stone" (OT=2), and "Sand" (OT=3).

The class boundaries 26 are increasing with the speed v. This conceals the effect that the sensor noise of the rear wheel r increases with increasing speed, even on the same driving surface. This is one reason why a two-dimensional class diagram 27 is advantageous here.

Instead of the sensor noise of the rear wheel r, the slip between the front wheel and the rear wheel, ie the difference between the speed of the front wheel and the speed of the rear wheel, can also be used as sensor data 11 here.

Classified values of the road surface can be obtained from sensor data 11 by means of the class diagram 27 . The chronological sequence of the classified values can be stored in the data recording unit 2 as input variables 12 .

In the 8a until 8c the average angle of inclination α is shown depending on the type of curve KT, an angle of inclination profile 30 . The 8a Data recorded by a professional who 8b data recorded by a standard driver and the 8c Data recorded by a beginner.

First, a curve that has been driven through is classified, ie assigned to a curve type KT. The classification is based on predefined areas of different curve properties: the curve radius, the curve length and/or the curve slope. This has already been explained in more detail in the main part of the description of this document.

In the simple example in the 8a until 8c there are nine curve types KT. The classification of curves traveled through into curve types KT is carried out here, for example, only taking into account the curve radius and the curve length. The curve radius and curve length are each divided into three areas. The curve types KT designated 1, 2 or 3 have a small curve radius, the curve types KT designated 4, 5 or 6 have a medium curve radius, and the curve types KT designated 7, 8 or 9 have a large curve radius. The curve types KT labeled 1, 4 or 7 have a small curve length, the curve types KT labeled 2, 5 or 8 have a medium curve length and the curve types KT labeled 3, 6 or 9 have a large curve length.

In a classification method, for example, classification can first be made with regard to the radius of the curve and then with regard to the length of the curve.

The maximum angle of inclination is determined in each curve. Then the average of the maximum inclination angles α is determined. The calculation of the average can be done cumulatively: each time a curve has been taken, the average can be updated with the new maximum bank angle. This means that the amount of stored data does not increase when using a vehicle.

The profile 30 of the average maximum inclination angle α in the 8a until 8c differ considerably. Therefore, by comparing the profile of the lean angle, it can be determined whether a rider is a pro, a standard rider, or a beginner. This can be done, for example, by calculating the difference between the individual average inclination angles of a driver and those of a professional ( 8a) be reached. If the difference is large and the tilt angles are less than the reference value, a beginner can be inferred.

the 9a and 9b show frequency distributions represented by a Kamm's circle with the axes longitudinal acceleration a _lon and lateral acceleration a _lat , with both variables being present as sensor data 11 and/or input variables 12 . Lines show contours of frequency reference values.

the 9a shows a frequency distribution of another driver like that 9b . A characteristic difference can be seen, in particular, the driver accelerates in the 9a often in the curve, from which one can conclude, for example, that the driver is an experienced driver. Conclusions about the driving ability and/or the driving style of a driver can therefore be drawn from this driver profile data, as above by means of curve classification. A driving behavior can be classified by the data processing unit 3 on the basis of such a frequency distribution.

Reference List

1

sensor

2

data recording unit

3

data preparation unit

31

classification unit

32

compression unit

33

calculation unit

4

unit of account

5

functional connection

6

Storage

8th

external computer network

9

end-user entity

91

mobile device

92

computer

93

internal computer network

10

end user

11

sensor data

12

input variables

13

processed data

14

rating sizes

15

other input variables

20

dwell time list

21

dwell matrix

22

pseudo damage list

23

Pseudo Damage Matrix

24

timing of an input variable

25

timing of a classification

26

class line

27

class diagram

28

Decision tree (multi-level classification procedure)

29

Frequency distribution in Kamm's circle

30

pitch angle profile

100

vehicle

101

vehicle component

n

engine speed

M

engine torque

B(n)

engine speed range

B(M)

engine torque range

G

gear

T

dwell time

PSD

pseudo damage number

ST

route type

KT

curve type

OT

off-road guy

s

route / way

v

speed

right

Rear wheel speed sensor noise

a

tilt angle

TOL(x)

Class boundary depending on the size x

alon

longitudinal acceleration

alat

lateral acceleration

QUOTES INCLUDED IN DESCRIPTION

This list of the documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• DE 102014215258 A1 [0002]
• EP 1679673 B1 [0003]
• EP 2026287 B1 [0004]

Claims (23)

Method for determining the condition of use of a vehicle (100) or a vehicle component (101), in particular a motorcycle or a motorcycle component, comprising the following steps: a) Recording of a time sequence and/or a value cumulated from a time sequence of at least two input variables (12) by a data recording unit (2) during operation of the vehicle (100), the input variables (12) being a driving parameter and/or a driving dynamics parameter and/or classified and/or compressed values of driving parameters and/or driving dynamics parameters of the vehicle (100) and/or the vehicle component (101), wherein the input variables (12) are determined from sensor data (11) and wherein the sensor data (11) are recorded by at least one sensor (1) arranged on the vehicle (100), b) transmission of the recorded chronological sequence and/or the value of the input variables (12) cumulated from a chronological sequence to a computing unit (4), c) Provision of a vehicle-specific functional relationship (5) between the chronological sequence and/or the cumulative value of the at least two input variables (12) from a chronological sequence and at least one evaluation variable (14) of the vehicle (100) and/or vehicle component (101) , wherein the evaluation variable (14) is a variable that is representative of the condition of use of the vehicle (100) and/or the vehicle component (101), d) calculation of the at least one evaluation variable (14) from the transmitted time sequence and/or the cumulative value of the input variables (12) from a time sequence by means of the functional relationship (5) in the computing unit (4), e) transmission of the calculated evaluation variable (14) to a memory (6). procedure after claim 1 , the at least two input variables (12) being determined by at least one data processing unit (3), the at least one data processing unit (3) preferably acting as a classification unit for classifying data, as a compression unit for compressing data and/or as a calculation unit for arithmetical derivation of data, wherein the at least one data processing unit (3) preferably - has a chronological sequence of sensor data (11) and/or input variables (12) as input data, and/or - in the information flow between at least one sensor (1) and the data recording unit (2), and/or - is arranged in the information flow between the at least one data recording unit (2) and/or the computing unit (4), and/or - is arranged in the data recording unit (2). Method according to at least one of the preceding claims, wherein the vehicle (100) has a chassis, the chassis preferably having front wheel suspension and rear wheel suspension, and one of the input variables (12) being the mass of a driver and/or the mass of a passenger and/or the mass of luggage being transported, and wherein at least one of the evaluation variables (14) is a variable that is representative of the condition of use of the chassis, preferably relating to the front wheel suspension and/or the rear wheel suspension of a motorcycle. Method according to at least one of the preceding claims, wherein the vehicle (100) has a chassis, and one of the input variables (12) is an acceleration of the vehicle (100), preferably a longitudinal acceleration of the vehicle (100), and/or a speed of the vehicle (100), and wherein at least one of the evaluation variables (14) is a variable that is representative of the condition of use of the chassis, preferably relating to the front wheel suspension and/or the rear wheel suspension of a motorcycle. Method according to at least one of the preceding claims, in which the mass of a driver and/or the mass of a passenger and/or the mass of luggage being transported is - a measurement of the spring deflection, and/or - a measurement of the angle of inclination and the acceleration, preferably by means of an algorithm is determined. Method according to at least one of the preceding claims, wherein the vehicle has an engine, preferably an internal combustion engine, and wherein one of the input variables (12) is the engine speed and/or the engine torque of the engine, and wherein there is at least one of the evaluation variables (14) is a variable that is representative of the condition of use of the engine or at least one component of the engine. Method according to at least one of the preceding claims, wherein the vehicle (100) has a transmission, and wherein min at least one of the input variables (12) is an engaged gear of the transmission, and at least one of the evaluation variables (14) is a variable that is representative of the state of use of the transmission. Method according to at least one of the preceding claims, wherein at least one of the input variables (12) is location-specific information, preferably location data and/or data relating to the driving surface and/or the type of road traveled on. Method according to at least one of the preceding claims, wherein at least one of the input variables (12) is a slip between a front wheel and a rear wheel of the vehicle (100). Method according to at least one of the preceding claims, in which at least one of the input variables (12) is a value which is representative of the driving style and/or the driving ability of the driver. Method according to the preceding claim, wherein at least the inclination of the vehicle (100) is recorded during cornering and wherein a value representative of the driving style and/or the driving ability of the driver is determined on the basis of the average inclination during cornering and/or the average speed is calculated while driving curves, with a curve type preferably being assigned to at least one curve and an average value of the maximum gradients in the curve being recorded for each curve type, with the average value of the maximum gradients being particularly preferably updated after each curve. Method according to at least one of the preceding claims, wherein one of the input variables (12) is an indicator variable specifically for the driver of the vehicle, the indicator variable preferably being determined from the vehicle key used and/or the mass of the driver. Method according to at least one of the preceding claims, wherein the functional relationship (5) is specific to a driver of the vehicle (100). Method according to at least one of the preceding claims, wherein the computing unit (4) is arranged in the vehicle (100). Method according to at least one of the preceding claims, in which the recorded chronological sequence of the input variables (12) is transmitted to a computing unit (4) separate from the vehicle (100). Method according to at least one of the preceding claims, wherein the evaluation variable (14) is transmitted to an end user unit (9), preferably a mobile terminal (91). Method according to at least one of the preceding claims, comprising the following steps: - Determination of at least one cumulative value from a time sequence of input variables (12) for determining load amplitudes, - Relating the at least one cumulative value to a further functional relationship between load amplitudes and durability of the vehicle and/or the vehicle component, with the further functional relationship preferably being given by a Wöhler curve. Method for developing a functional relationship (5) for a method according to at least one of the preceding claims, comprising the following steps: a) Recording of a time sequence and/or a value cumulated from a time sequence of at least two input variables (12) by a data recording unit (2) during the operation of at least one vehicle (100), the input variables (12) being one Vehicle parameters and/or a driving dynamics parameter and/or classified and/or compressed values from driving parameters and/or driving dynamics parameters of the at least one vehicle (100) and/or at least one vehicle component (101), the input variables (12) from sensor data (11) are determined and the sensor data (11) are recorded by at least one sensor (1) arranged on the vehicle (100), b) recording at least one value of an evaluation variable (14), the evaluation variable (14) being a variable representative of the condition of use of the at least one vehicle (100) and/or the at least one vehicle component (101), and wherein the Evaluation variable (14) is recorded by means of at least one sensor (1) arranged on at least one vehicle (100) and/or on at least one vehicle component (101) and/or simulated by means of a computer model and/or entered manually and/or loaded from a database , c) determining a correlation between the recorded input variable (12) and the recorded evaluation variable (14). Vehicle (100) with a stored functional context (5) for a method according to one of Claims 1 until 17 . Computer program product comprising instructions that cause a vehicle (100) to carry out a method according to one of Claims 1 until 17 operates. Computer-readable medium on which a computer program product claim 20 is saved. Data medium signal, which a computer program product according to claim 20 transmits. Vehicle (100) with a computer-readable data carrier on which a computer program product claim 20 is saved.

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