Device and computer-implemented method, for testing

Description

background

[0001] Testing a machine, which involves running the machine, requires creating a prototype and conducting tests under real-world conditions, which demands significant resources. Documents DE 10 2017 106919 A1, US 8 725 456 B1 and S. FOULARD ET AL: "Automotive drivetrain model for transmission damage prediction", MECHATRONICS, Vol. 30, September 1, 2015, pages 27-54, XP055489559, describe, for example, methods for testing a machine with multiple components or for testing a single component of a machine. Disclosure of the invention

[0002] A computer-implemented method for testing a machine with a plurality of components, or for testing a single component of a machine, involves providing a set of input variables to a model. This set of input variables represents stress factors on the machine or on at least one component of the machine. The method then selects a subset of the input variables and maps this subset—by the model—to an output variable of the model that represents the stress caused by the stress factors on at least one component of the machine. This enables, preferably at an early stage of development, a significantly more efficient determination of the component-specific stress and an evaluation based on stress distributions derived from a plurality of components, e.g.,Tens of thousands of different stress scenarios or repetitions of tests with the same stress scenario can be derived. A prototype of the machine is not required.

[0003] The method advantageously allows the derivation of functional loads for the evaluation and optimization of machine or component behavior, in particular an evaluation and / or adaptation / optimization of an operating strategy, for example for the optimization of functional system parameters, whereby the component can be part of this system, for example with regard to consumption behavior.

[0004] In one aspect, the machine component is, for example, a fuel cell component, an inverter for an electric machine, a battery or a transmission, particularly for an electric vehicle, or a fuel injection system, particularly for a hybrid vehicle. Furthermore, the component can be another component, particularly of a vehicle, such as a component of a powertrain, a steering system, a braking system, or a safety system, such as a camera or radar system.

[0005] In one aspect, the machine is a vehicle, a motorcycle or an electric bicycle, a train or an airplane, or a ship.

[0006] Preferably, a degree of damage, in particular a fatigue degree, of the at least one component is determined as a function of a set of input variables, which includes the input variable. Such a stress, or more generally a damage mechanism, can in particular include wear, corrosion, or more generally fatigue and statistical failure. A degree of damage is understood to mean, in particular, the extent of damage caused by a stress or a damage mechanism, especially with an adverse effect on the functionality of the component or the machine.

[0007] According to the invention, selecting the subset involves selecting an input variable that defines a route and selecting an input variable that defines a driver profile, wherein the input variable defining the route is selected from a plurality of input variables that define different routes, and wherein the input variable defining the driver profile is selected from a plurality of input variables that define different driver profiles. This enables the simulation of a large number of different driving cycles to determine stress and load instead of a multitude of real-world test drives.

[0008] Preferably, a plurality of different subsets are selected and mapped, whereby a distribution of stress or fatigue is determined from the output variables resulting from the mapping of the different subsets. This provides variations that improve the ability to identify combinations of input variables that cause higher fatigue than others or a specific class of fatigue.

[0009] Preferably, the method, in particular the determination of the degree of damage, involves adding or multiplying selected output variables from the set of output variables. This provides a consolidated damage assessment of the selected output variables.

[0010] Preferably, the method includes, in particular, determining the degree of damage, determining the frequency of occurrence of a route characteristic, especially a time of day, a start time, a region, a duration, a distance, or a type, either in machine-specific statistics or in a machine-specific journal, and determining a weight for the output variable depending on the frequency, as well as adding or multiplying the weighted output variables. This provides the consolidated damage for a specific route characteristic.

[0011] Preferably, the method includes, in particular, determining the stress level, selecting an operator, especially a driver or user of the machine, from the machine-specific statistics or the machine-specific log, determining a plurality of output variables for the operator, and preferably determining the degree of damage using the plurality of output variables. This provides the consolidated damage for a specific operator.

[0012] Preferably, the method, in particular the determination of the degree of damage, includes selecting the machine-specific statistics from a set of machine-specific statistics. This provides machine-specific weightings.

[0013] A device for testing a machine with a plurality of components or for testing a component of a machine is set up to carry out the steps of the procedure accordingly.

[0014] A computer program contains instructions which, when executed by a computer, cause the computer to perform the steps of the procedure accordingly.

[0015] Further advantageous aspects of the invention will become apparent from the following description and the drawing. The drawings show: the Fig. 1 schematically a part of a device 100 for testing, which Fig. 2 schematically, steps in a testing procedure that Fig. 3 schematically, a first example, the Fig. 4 A second example shown schematically.

[0016] The Fig. 1Figure 1 schematically shows a part of a testing device 100. The device 100 is set up or can be set up for testing a machine with a plurality of components or for testing a single component of a machine.

[0017] The device 100 includes a database 102, a model 104 and an analyzer 106.

[0018] Database 102 contains a set of input variables for model 104. Input variables identify load factors on the machine or load factors on at least one component of the machine. Database 102 contains multiple input variables that define different routes and different driver profiles. Database 102 can also include input variables that define different environmental conditions.

[0019] The input variables of model 104, which define a route, define, for example, a start of the route and an end of the route and / or a path of the route.

[0020] The route can be defined using geographic coordinates for real-world routes or telemetry data from actual journeys. The route can be selected from a trip log. Alternatively, the route can be defined using synthetically generated data representing geographic coordinates or telemetry data not derived from real-world routes.

[0021] The model's input variables, which define a driver profile, specify, for example, the frequency and / or mode of throttle and / or brake control operation. Preferably, further information about a general driving style, in particular tolerated speeds and the degree of consistency of a driving style, is also incorporated into the model.

[0022] The input variables of the model, which define an environmental condition, preferably define climatic, geographical, traffic-related and / or legal environmental conditions, for example at least one of a temperature, a wind speed, a wind direction, a speed limit, a speed limit position, a vehicle position and a traffic jam position.

[0023] The input variables for Model 104 can also be characteristic parameters of the machine under test or of one of its components. A parameter of the machine or its component can be selected from a range defined for that parameter. These parameters can define how Model 104 maps an input variable or variables to an output variable or variables. Model 104 can include parts that map an input variable or variables to an output variable or variables. Model 104 can include at least one part to map an input variable or variables to an intermediate variable or variables. Model 104 can include at least one part to map an intermediate variable to an output variable or variables. Model 104 can include at least one part to map intermediate variables to an output variable or variables.At least one part can be configured to be mapped by at least one of the following operations: a function, an estimation, a finite element simulation, a characteristic curve, or a table. This list is exemplary of operations and is not exhaustive.

[0024] Model 104 is configured to map a subset of input variables to a set of output variables of Model 104, which characterizes the stresses that cause the load factors in at least one component of the machine. In this example, Model 104 includes a first part 108 and a second part 110. The first part 108 is configured to simulate global load variables. The subset of input variables is mapped by the first part 108 to global load variables, which are then passed as input to the second part 110. The second part 110 is configured for system simulation. The second part 110 is configured to map the input from the first part 108 to the set of output variables. Local load variables can be an additional input to the second part 110. The local load variables and the input from the first part 108 are mapped to the output variables in this aspect.

[0025] The first part 108, for example, can be configured to simulate the machine or its component. The second part 110, in this example, can be configured to simulate a stress on the machine or its component.

[0026] A non-exhaustive list of examples of global load variables includes: speed, acceleration, and gear selection. A non-exhaustive list of component examples includes a vehicle's powertrain. A non-exhaustive list of examples of local load variables includes engine power or the pressure in an injection system.

[0027] The global load variable for the speed of a vehicle on a route is simulated, for example, by the first part 108, by sampling an input variable from the database that defines route data, e.g., telemetry data for the route, by determining driver behavior on the route with a driver model that is parameterized according to the input variables sampled from the database, by determining driving resistance on the route with a physical model that is parameterized according to the route data and preferably vehicle-specific data, by determining a tolerance with a stochastic model of tolerances that is parameterized according to the driver behavior and input variables sampled from the database that represent traffic, and by determining the speed with a data-driven model to generate a speed curve on the route as a function of the output of the other models.

[0028] The set of output variables for the speed curve is simulated by the second part 110, for example, using a simulation model that determines the load on components of the vehicle's powertrain when the speed curve is applied. In this example, the route data, specifically telemetry data that specifies a gradient curve of the route, is an additional input for the second part 110. The speed curve and the gradient curve are aligned in this example. In this example, the simulation model determines the acceleration from the speed curve and a load on the powertrain as a function of the speed, acceleration, and gradient of the route. In another example, engine torque, engine revolutions per minute, and / or engine power are determined from a reverse model of the powertrain as a function of the speed, acceleration, and gradient.

[0029] The analyzer 106 is designed to determine the degree of fatigue of at least one component, depending on the quantity of output variables.

[0030] The analyzer 106 can be configured to determine a time course of the stress.

[0031] The analyzer 106 can be configured to determine a stress distribution over a variation of inputs to the model 104. The analyzer 106 can be configured to determine a distribution of stress profiles over the variation of inputs.

[0032] The analyzer 106 is designed to determine the damage, in particular fatigue, of the machine or its components from the distribution or temporal profile of the stress. The analyzer 106 is designed to determine the damage, in particular fatigue, based on a count, e.g., a rainflow count, a linear damage accumulation, an analysis of high-cycle damage, in particular high-cycle fatigue, or low-cycle damage, in particular low-cycle fatigue. As stated above, such stress can include, in particular, wear, corrosion, or, more generally, fatigue and statistical failure.

[0033] In one example, the distribution results from variations in driver behavior and routes. Additionally, the distribution can be determined for variations in machine usage. A non-exhaustive list of examples of damage, particularly fatigue, includes damage, especially fatigue, to components caused by pressure changes in the engine's fuel injection system.

[0034] The analyzer 106 can be configured to determine combinations of input variables that cause greater damage, particularly fatigue, than other combinations. Critical combinations are determined, for example, by detecting a distribution that lies within a predetermined percentile compared to other distributions resulting from variations.

[0035] The analyzer 106 can be configured to determine a combination of input variables that are characteristic of a fatigue class.

[0036] The result of the analysis can be used to define further real-world measurements.

[0037] Device 100 is configured to select the subset. Device 100 is configured to select an input parameter defining a route, an input parameter defining a driver profile, and an input parameter defining at least one environmental condition for the subset. Device 100 is configured to provide the subset to model 104 and to provide the output resulting from the input to analyzer 106. Device 100 can be configured to output the damage, in particular fatigue. For example, Device 100 is configured to select a plurality of different subsets to be mapped and to determine a distribution of stress or damage, in particular fatigue, which is determined from the output parameters resulting from mapping the different subsets.

[0038] The device 100 can include at least one processor to operate the database 102, the model 104, the analyzer 106 and an output for distribution accordingly.

[0039] The device 100 is configured to perform the steps of the procedure described below with reference to the Fig. 2 The model 104 can be at least partially an artificial neural network. The analyzer 106 can be at least partially a classifier. The classifier can be an artificial neural network or contain one.

[0040] Artificial neural networks can be pre-trained to model the machine or a component thereof.

[0041] The procedure is computer-implemented. The procedure can be executed, at least partially, by dedicated hardware, at least for determining the output of model 104 or analyzer 106.

[0042] The procedure is performed to test a machine with multiple components or to test a single component of a machine. Model 104 and analyzer 106 are configured to model and analyze the machine or a component thereof. Model 104 identifies stress factors on the machine or stress factors on at least one component of the machine.

[0043] In step 202, a set of input variables for model 104 is provided.

[0044] In step 204, a subset of the set is selected. Selecting the subset involves choosing an input that defines a route, an input that defines a driver profile, and an input that defines at least one environmental condition.

[0045] The input variable that defines the route is selected from a plurality of input variables that define different routes.

[0046] The input variable that defines the driver profile is selected from a plurality of input variables that define different driver profiles.

[0047] The input variable that defines at least one environmental condition is selected from a plurality of input variables that define different environmental conditions.

[0048] At least one environmental condition can be selected depending on a time specification, in particular a season, a time of day, a day of the year or a day of the week.

[0049] In step 206, the model maps the subset to an output variable of the model that characterizes a stress which causes the load factors in at least one component of the machine. Different subsets are mapped by the model to different output variables. A set of output variables of the model contains a plurality of output variables that characterize a stress which causes the load factors in different scenarios in at least one component of the machine.

[0050] In one example, the set of output variables for different operators of the machine includes different output variables that are assigned to different characteristics of an operation of the machine.

[0051] The operator can be a driver or user of the machine.

[0052] In one example, the set of output variables for n different operators and o different properties includes a mapping to different fatigue levels D: Property 1, ..., property o Operator 1: D11, ..., D10 ... Operator n: Dn1, ..., Dno

[0053] Examples of vehicle characteristics include road types: "city", "countryside", "highway".

[0054] Examples of characteristics of a vehicle driving profile are: "duration", "distance of a trip".

[0055] Database 102 can contain a mapping of properties to the majority of input variables that define different routes or different driver profiles. The properties can be available from metadata associated with the input variables.

[0056] Telemetry data can define a temporal progression of the input variable.

[0057] In one example, the set of output variables for n different operators includes a mapping to a total fatigue level D: Operator 1: D1 ... Operator n: Dn

[0058] In step 208, a fatigue level of at least one component is determined as a function of the quantity of the output variables.

[0059] In one example, selected output variables are added together. Instead of adding the output variables, they can also be multiplied.

[0060] In one example, a weighted sum or a weighted product of selected output variables is determined in the set of output variables.

[0061] The weighting can be determined from machine-specific statistics or a machine-specific journal.

[0062] Machine-specific statistics can include a mapping of a machine to a distribution of various operational characteristics. A set of machine-specific statistics can contain individual distributions for different machines. The set of machine statistics for m machines and o characteristics can contain a distribution S per machine, which sums up to 100% per machine. Property 1, ..., property o Machine 1: S11, ..., S1o ... Machine m: Sn1, ..., Sno

[0063] The machine-specific log can contain multiple mappings from one operator to the characteristics of the operation. The machine-specific log can be a logbook in which different operators are mapped to the characteristics of their respective operations.

[0064] The logbook for n operators, m trips and o types of properties can include the following properties P: Property 1, ..., property o Operator 1 - Trip 1: P11, ..., P10 ... Operator n - journey m: Pn1, ..., Pno

[0065] In one example, the frequency of occurrence of a route characteristic, specifically duration, distance, or type, is determined—either in machine-specific statistics or in the machine-specific journal. The frequency of occurrence can be expressed as a percentage in the distribution. The weighting of the output variable can be determined based on the frequency.

[0066] In one example, the fatigue level is determined using a machine-specific statistic selected from the set of machine-specific statistics. More precisely, selected output variables from the set of output variables are mapped to an overall fatigue level D per operator and machine: Operator 1 - Machine 1: D11 ... Operator n - Machine 1: Dn1 ... Operator 1 - Machine m: D1m ... Operator n - Machine m: Dnm

[0067] In one aspect of the example, steps 204 and 206 are repeated to select a plurality of different subsets and map them individually to the plurality of sets of output variables. In one example, a stress or fatigue distribution is determined from the output variables resulting from the mapping of the different subsets.

[0068] In one of the Fig. 3 The example shown includes database 102: Georeferenced routes of real-world journeys from point A to point B; mobility studies including logbooks; systematically recorded field data, e.g., in a vehicle fleet, especially recorded by load counters; high-resolution telemetry data

[0069] From database 102, a first input variable 302, which provides the driver profile, is selected.

[0070] A second input variable 304, which provides the street type, is selected from database 102.

[0071] A third input, 306, is a route split. In this example, a street-type-specific split is provided.

[0072] The example uses road type-specific properties: "City" 308, "Country" 310 and "Highway" 312.

[0073] The road-type-specific breakdown is provided for a variety of different vehicles. Figure 3 This represents the split for a first vehicle 314 of the majority of vehicles and a last vehicle 316 of the majority of vehicles. Individual distribution values ​​are determined for the different vehicles for the properties "city" 308, "rural" 310 and "highway" 312. Figure 3 shows, as examples, the allocation values ​​314-1, 314-2, 314-3 for the first vehicle 314 and the allocation values ​​316-1, 316-2, 316-3 for the last vehicle 316.

[0074] The first input 302 and the second input 304 are mapped to the model 104 to road-type-specific results for a plurality of driver profiles. Figure 3 shows the road type-specific results 318-1, 318-2, 318-3 for a first driver profile 318 and the road type-specific results 320-1, 320-2, 320-3 for the last driver profile 320 of the majority of driver profiles.

[0075] The road-type-specific distribution and the road-type-specific results are overlaid by a function 322. According to one example, function 322 calculates relative damage values ​​for each driver profile based on the distance traveled. According to another example, after selecting a driver profile-vehicle combination from a pool of available profile-vehicle combinations, a weighted sum is calculated by multiplying the relative, road-type-specific values ​​by the respective road share, summing them, and extrapolating to a design target, represented by a target distance or target operating time. In this example, function 322 thus determines an overall damage, specifically the degree of fatigue, with a weighted sum per driver profile-vehicle combination for a plurality of different driver profile-vehicle combinations. Fig. 3shows a first total damage 324-1 for a first combination 324 and a second total damage 326-1 for a last combination 326.

[0076] In one of the Figure 4 In the example shown, database 102 contains a plurality of journey logs 402, e.g. driver logs.

[0077] The trip logs 402 contain a plurality of user-trip combinations. A first user-trip combination 404 and a last user-trip combination 406 of the plurality of user-trip combinations are in the Figure 4 The example illustrates this. Trips are identified by the following properties: day of the week, start time, region, duration, and distance. Figure 4 Figure 408 schematically shows one of these properties and Figure 410 a second. Other properties can also be defined. Figure 4shows a first duration 404-1 and a first distance 404-2 for the first user-trip combination 404 and a second duration 406-1 and a second distance 406-2 for the last user-trip combination 406.

[0078] The user-trip combinations and georeference routes 412 from the database 102 are linked with a linker 414 with a first input variable for the model 104.

[0079] The Linker 414 can match metadata of the georeference routes 412 with the properties of the trips from the user-trip combinations in order to find potential routes as the first input to the database 102 that have similar properties in their metadata as a trip from the trip log 402.

[0080] A large number of potential routes can be identified in a large amount of telemetry data. To reduce the data volume and maintain a representative selection, potential routes can be processed, for example, using a k-means clustering algorithm to group them into routes with similar properties. In this example, the first input is a center of the group with properties similar to the journey.

[0081] A second input variable, 416, is selected from database 102, providing the driver profile. In this example, one driver profile is selected per trip log.

[0082] The output variables of model 104 for different trips by the same user are superimposed with a function 418. According to an example, the sum of damage values ​​and total distance and duration is calculated for each individual user. The damage values ​​are then extrapolated to a design target, which is represented, for example, by a target distance or a target operating time. In the example, function 418 uses a sum of the output variables to determine an overall damage, in particular the degree of fatigue. Fig. 4 shows a first total damage 420-1 for a first user 420 and a second total damage 422-1 for a last user 422.

[0083] The examples shown describe the use for calculating a total damage amount, although the use is not limited to total damage amounts, as statistical values ​​such as mean values ​​of component loads, or histograms or load collectives can also be used.

[0084] The following section describes, using further examples, how the output quantity is determined in particular by the model 104 and the analyzer 106 with damage accumulation. 1) Fuel cell component:

[0085] An example of a design element of the fuel cell component is a turbine wheel of an electric air compressor, particularly for a mobile fuel cell system. In this context, "mobile" means that the dimensions of the fuel cell are suitable for powering a passenger car.

[0086] An example of a damage mechanism for the fuel cell component is fatigue based on centrifugal forces.

[0087] In this respect, model 104 includes the following parts: i. A section configured to calculate vehicle wheel power based on a driving resistance equation as a function of vehicle speed, gradient, vehicle mass, and drag coefficient. Inputs to this equation include, for example, acceleration resistance, air resistance, rolling resistance, and gradient resistance. ii. A section configured to calculate direct current (DC) power required to generate an alternating current (AC) supply for an electric machine to propel the vehicle. The AC power is calculated, for example, from the vehicle wheel power, taking into account detailed power losses in the drivetrain, such as transmission and differential losses. The DC power is calculated, for example, from the AC power, taking into account detailed power losses in an AC / DC inverter. iii.1. A part configured to calculate, based on an operating strategy for the vehicle's power split, which takes into account other requirements such as maximum fuel cell stack dynamics and battery state of charge, a power split between the fuel cell stack and the high-voltage battery from the DC power. 2. A part configured to calculate a stack current required by the stack to deliver the DC power. The stack current is calculated, for example, based on a detailed stack model or a characteristic curve model. 3. A part configured to determine a turbine speed (rpm) from the stack current. The stack current serves as a reference parameter for the fuel cell subsystems. Optionally, this part also determines an altitude above sea level, an ambient temperature, and an humidity level.

[0088] In this example, a time-resolved turbine speed (rpm) is determined. This turbine speed is then input into a damage model.

[0089] Analyzer 106 includes the damage model.

[0090] The damage model is designed to derive a centrifugal force from the turbine speed.

[0091] In this example, the damage model is set up to perform a rainflow count of the time-resolved turbine speed with a predefined resolution and to calculate the damage accumulation based on a Wöhler curve.

[0092] The output of the damage model is the initial variable that represents the damage accumulation. 2) Inverter for an electric machine

[0093] An example of an inverter design element is the B6 bridge of a power module. In this example, the inverter is an electric air compressor for mobile fuel cell systems. "Mobile" in this context means that the fuel cell's dimensions are suitable for powering a passenger car. Any other inverter can be tested in the same way.

[0094] One damage mechanism that can be used as an example for the inverter is based on thermal stress due to a high rate of temperature change.

[0095] In this respect, model 104 includes parts i), ii), iii), and iv) and the input variables as described above. Model 104 additionally includes v. a part designed to calculate the temperature of the B6 bridges based on the stack current and a voltage applied to the inverter.

[0096] In this example, a time-resolved temperature is determined. This temperature is then input into a damage model.

[0097] Analyzer 106 includes the damage model.

[0098] The damage model is set up to perform a rainflow count of the time-resolved temperature with a predefined resolution and to calculate the damage accumulation based on a Wöhler curve.

[0099] The output of the damage model is the initial variable that represents the damage accumulation. 3) High-voltage battery

[0100] In this example, the high-voltage battery contains a lithium-ion battery cell. An example of a structural element of the high-voltage battery is the cell housing.

[0101] An example of a damage mechanism in a high-voltage battery is a rupture of the casing due to swelling of the battery cell. When a battery cell is charged, it expands, i.e., it swells. This creates stress within the casing. This stress compresses the cell. The damage mechanism for a battery containing multiple cells can be tested in the same way.

[0102] Model 104 includes, in this respect, parts i), ii) and the input variables as described above. Model 104 also includes iii. A part configured to calculate a series of state-of-charge (SOC) values ​​for the battery based on battery control and limits in a circuit at the battery. iv. A part configured to calculate a stress series from the SOC series. In the example, a stress in the battery casing is determined as a function of a value from the SOC series in a finite element simulation. In the example, the values ​​from the SOC series are transformed into a stress series.

[0103] Analyzer 106 includes the damage model.

[0104] The damage model is configured to perform a rainflow count of the battery's load cycles and calculate damage accumulation based on a Wöhler curve. Load cycles can be counted based on the state of charge (SOC) series, with the start of an increasing SOC indicating the beginning of a load cycle.

[0105] The output of the damage model is the initial variable that represents the damage accumulation. 4) Transmission for an electric vehicle

[0106] In this example, the transmission for an electric vehicle has gears with teeth. An example of a component in the transmission is a tooth of one of the gears.

[0107] One example of a gearbox damage mechanism is tooth breakage, e.g., due to high torque. Another example of a gearbox damage mechanism is pitting on a tooth flank, e.g., due to high torque and a high number of revolutions per minute (rpm).

[0108] The Model 104 includes this aspect i. A section configured to calculate a series of driving forces based on vehicle characteristics, e.g., mass, air resistance, rolling resistance, speed, and gradient profile. Mass, air resistance, rolling resistance, speed, and gradient profile are input variables as per this example. ii. A section configured to calculate the torque at the tooth and / or the revolutions per minute based on a gear ratio and the efficiency of the transmission. The gear ratio and efficiency are input variables as per this example.

[0109] Additional effects that limit the potential performance of an electric vehicle's electric motor can be taken into account. This could include power loss at high speeds or overheating protection. iii. a section designed to derive a retention-time characteristic map of the electric motor, e.g., duration at specific RPM and torque values.

[0110] Analyzer 106 includes the damage model.

[0111] The damage model is configured to calculate the number of revolutions at specific torque levels from the retention-time characteristic map. The damage model is also configured to calculate the damage accumulation for each torque level based on a Wöhler curve.

[0112] In this example, individual Wöhler curves are defined for a tooth root and tooth flank.

[0113] The output of the damage model is the initial variable that represents the damage accumulation. 5) Fuel injection system in a hybrid vehicle

[0114] An example of a design element of the fuel injection system is a high-pressure pump, a fuel rail, or a fuel injector.

[0115] One example of a damage mechanism in the fuel injection system is damage, particularly fatigue, due to changes in fuel pressure. Changes in fuel pressure can be induced by hybrid vehicle-specific limitations or operating conditions.

[0116] The Model 104 includes this aspect i. A section configured to calculate vehicle wheel power as a function of vehicle speed, gradient, vehicle mass, and / or drag coefficient. Vehicle speed, gradient, vehicle mass, and drag coefficient are input variables according to this example. Vehicle wheel power is determined, for example, based on a driving resistance equation using acceleration resistance, air resistance, rolling resistance, and / or gradient resistance as input variables according to this example. ii. A section configured to determine, based on the calculated wheel power and its temporal sequence, whether the internal combustion engine or the electric motor is used for propulsion. iii. A section configured to calculate pressure changes in the injection system based on the vehicle wheel power and its temporal sequence.

[0117] The example determines desired pressure changes generated by the pressure control system and undesired pressure changes.

[0118] Undesired pressure changes are generated, for example, by thermal effects during periods of electric driving, i.e., when the combustion engine is switched off. Thermally induced pressure changes result from the thermal expansion of fuel within the closed injection system due to heat equalization effects between cold fuel and hot engine components during periods with the combustion engine off.

[0119] Undesirable pressure changes can occur, for example, due to hydraulic leaks, e.g. in containers, during periods when the combustion engine is switched off.

[0120] In this example, the aforementioned pressure changes, which can occur simultaneously, are combined.

[0121] Analyzer 106 includes the damage model.

[0122] The damage model is set up to perform a rainflow count of the pressure with a predefined resolution and to calculate the damage accumulation based on a Wöhler curve.

[0123] The output of the damage model is the initial variable that represents the damage accumulation.

Claims

1. Computer-implemented method for testing a machine having a plurality of components or for testing a component of a machine, comprising providing (202) a set of input variables for a model, wherein the set of input variables characterizes load factors on the machine or characterizes load factors on at least one component of the machine, selecting (204) a subset of the set, mapping (206) - by way of the model - the subset to an output variable of the model that characterizes a stress caused by the load factors in at least one component of the machine, characterized in that the selection (204) of the subset involves selecting an input variable that defines a route and selecting an input variable that defines a driver profile, and wherein the input variable that defines the route is selected (204) from a plurality of input variables that define different routes, wherein the input variable that defines the driver profile is selected (204) from a plurality of input variables that define different driver profiles, wherein preferably a degree of damage, in particular a degree of fatigue, of the at least one component is determined (208) on the basis of a set of output variables comprising the output variable.

2. Method according to one of the preceding claims, characterized in that a plurality of different subsets are selected (204) and mapped (206), wherein a distribution of the stress or fatigue is determined (208) from the output variables resulting from the mapping of the different subsets.

3. Method according to one of the preceding claims, characterized in that the method, in particular the determination (208) of the degree of damage, involves adding or multiplying selected output variables in the set of output variables.

4. Method according to Claim 3, characterized in that the method, in particular the determination (208) of the degree of damage, involves determining a frequency of the occurrence of a property of the route, in particular a time of day, a start time, a region, a duration, a distance or a type, either in machine-specific statistics or in a machine-specific journal, and involves determining a weighting for the output variable on the basis of the frequency and adding or multiplying the output variable weighted by the weighting.

5. Method according to Claim 4, characterized in that the method, in particular the determination (208) of the degree of damage, involves selecting an operator, in particular a driver or user, of the machine in the machine-specific statistics or the machine-specific journal, determining a plurality of output variables in the set of output variables for the operator, and preferably determining the degree of damage using the plurality of output variables.

6. Method according to Claim 4 or 5, characterized in that the method, in particular the determination (208) of the degree of damage, involves selecting the machine-specific statistics from a set of machine-specific statistics.

7. Method according to one of the preceding claims, characterized in that the component of the machine is a fuel cell component, an inverter for an electric machine, a battery or a transmission, in particular for an electric vehicle, a fuel injection system, in particular for a hybrid vehicle.

8. Method according to one of the preceding claims, characterized in that the machine is a vehicle, a motorcycle or an electric bicycle, a train or an aircraft, or a ship.

9. Device (100) for testing a machine having a plurality of components or for testing a component of a machine, comprising a database (102), a model (104) and an analyser (106), characterized in that the device is configured to carry out the steps of the method according to one of Claims 1 to 8.

10. Computer program, characterized in that the computer program contains instructions which, when executed by a computer, cause the computer to perform steps of the method according to one of Claims 1 to 8.