Method for operating drive device of motor vehicle and corresponding drive device

Abstract

The invention relates to a method for operating a drive device of a motor vehicle, the drive device having a drive unit which generates exhaust gas and exhaust gas values which describe the composition of the exhaust gas are determined by means of an exhaust gas sensor, and state values which describe the state of the respective system are determined for each of a plurality of systems of the drive device in the course of system diagnostics. According to the invention, each of the plurality of systems is stored in each case in at least one system defect data set, whether a system defect of the respective system affects the exhaust gas value, and in the event that the exhaust gas value deviates from the exhaust gas value range, the system defect data set is stored in each of the plurality of systems. Firstly, system defect data sets of the system are put into an inspection list of the system defect data sets to be inspected, and then the system defect data sets for which the exhaust values are not influenced are removed from the inspection list. Wherein the system defect data set retained in the check list is diagnosed as a function of the state value in order to identify a system defect of the respective system. The invention also relates to a drive device of a motor vehicle.

Description

TECHNICAL FIELD

[0001] The invention relates to a method for operating a drive device of a motor vehicle, wherein the drive device has a power unit which produces exhaust gas and an exhaust gas sensor by means of which an exhaust gas value is determined which describes a component of the exhaust gas, and wherein, within the framework of a system diagnosis, a state value is determined for a plurality of systems of the drive device, respectively, which describes a state of the respective system. The invention also relates to a drive device of a motor vehicle. BACKGROUND

[0002] For example, the document DE 10 2021 003 415 A1 is known from the prior art. This document describes a controller for monitoring an emission characteristic of a machine, wherein the monitoring is based on a first emission influence of a component of the machine. The controller is designed and set up to carry out the following steps: determining a first defect level of a first component of the machine; determining a first emission influence on the basis of the determined first defect level; and monitoring the emission characteristic of the machine on the basis of the first emission influence. SUMMARY

[0003] It is an object of the invention to propose a method for operating a drive device which is superior to the prior art, in particular in that a system defect of one of the systems of the drive device can also be identified with high reliability when the system diagnosis of the respective system does not indicate such a defect.

[0004] According to the invention, this is achieved by a method for operating a drive device of a motor vehicle having the features of claim 1. It is provided here that for each of a plurality of systems, respectively, in at least one system defect data set, it is stored whether a system defect of the respective system influences the exhaust gas value, wherein in the event of a deviation of the exhaust gas value from an exhaust gas value range, the system defect data sets for which a system defect is stored which does not influence the exhaust gas value are initially placed in a check list of system defect data sets to be checked and subsequently removed from the check list, and wherein the system defect data sets remaining in the check list are subjected to a device diagnosis in accordance with the state value for identifying a system defect of the respective system.

[0005] Advantageous design features of the invention, together with suitable improvements, are given in the dependent claims. It is pointed out that the embodiments explained in the description are not limiting; rather, any variant of the features disclosed in the description, claims and drawings is possible.

[0006] The drive device serves to drive a motor vehicle, i.e. to provide a drive torque which is provided for driving the motor vehicle. The drive device is preferably an integral part of the motor vehicle, but of course can also exist separately from the motor vehicle. In order to provide the drive torque, the drive device has a power unit which is preferably designed as an internal combustion engine. During operation of the drive device, fuel and fresh gas are at least temporarily fed to the power unit, wherein the fresh gas at least temporarily comprises fresh air. Furthermore, the fresh gas can have exhaust gas if exhaust gas recirculation is implemented, wherein exhaust gas generated by the power unit is at least partially fed back to the power unit, i.e. as a component of the fresh gas. The fuel and the fresh gas fed to the power unit form a fuel-fresh gas mixture having a defined composition, which is reacted in the power unit.

[0007] During operation of the drive device, exhaust gas is generated due to the chemical reaction of the fuel with the fresh gas, which exhaust gas is discharged in the direction of the drive device or of the external environment of the motor vehicle. Since the exhaust gas generated by the power unit contains harmful substances, the exhaust gas is preferably first fed to an exhaust gas aftertreatment device before being discharged into the external environment. In the exhaust gas aftertreatment device, the harmful substances are at least partially converted into less harmful products. The exhaust gas is discharged into the external environment only after passing through the exhaust gas aftertreatment device. The exhaust gas aftertreatment device is for example a vehicle catalyst, in particular a three-way catalyst, an oxidation catalyst, a nitrogen oxide (NOx) storage catalyst or a selective catalytic reduction (SCR) catalyst. However, it can also be designed as a particulate filter, in particular a gasoline particulate filter or a diesel particulate filter, preferably with an integrated vehicle catalyst, for example with a catalytic coating. x ) storage catalyst or a selective catalytic reduction (SCR) catalyst. However, it can also be designed as a particulate filter, in particular a gasoline particulate filter or a diesel particulate filter, preferably with an integrated vehicle catalyst, for example with a catalytic coating.

[0008] The drive device has a plurality of systems, which each represent a part of the drive device. A system can for example be understood as a component of the drive device or of the power unit, however additionally or alternatively also as a software program running on a controller of the drive device. Thus, a system comprises any element of the drive device, in particular those elements which influence the composition and / or the flow of the exhaust gas. Flow is here understood as the amount of exhaust gas per unit of time, i.e. in particular the exhaust gas mass flow or the exhaust gas volume flow.

[0009] By way of example only, the systems of the drive device include one or more of the following systems: high-pressure fuel pump, fuel pressure sensor, load sensor, hybrid adaptation device, tank ventilation valve, exhaust gas aftertreatment device, lambda sensor / oxygen sensor (in particular pre-catalytic oxygen sensor or post-catalytic oxygen sensor), temperature sensor, ambient pressure sensor, supercharger (in particular compressor and / or exhaust gas turbocharger), supercharging pressure sensor, intake manifold leak detection, camshaft adjuster, crankcase ventilation device, fuel vapor emission system (in particular tank leak detection), expansion valve detection or tank shut-off valve detection, cooling system, cylinder imbalance sensor, idle speed controller, cold start strategy, valve lift switching device, exhaust flap, knock sensor, and ignition recognition device.

[0010] For the systems of the drive device, a system diagnosis is carried out for each system, respectively, and a status value is determined within the framework thereof. The status value describes the status of the respective system and is determined for monitoring the individual system. In the system diagnosis, only one of the systems is considered, respectively; a separate system diagnosis is carried out for each of the systems accordingly. This approach is generally referred to as on-board diagnosis (OBD). It has been possible to indicate system defects that can exist in the individual systems.

[0011] In addition, an exhaust value is determined by means of an exhaust sensor, which describes an exhaust composition. The exhaust value indicates, inter alia, a concentration of at least one exhaust component of the exhaust gas. The exhaust sensor can be provided and arranged, for example, for measuring raw emissions of the drive device or also for measuring tailpipe emissions. In the former case, the exhaust sensor is arranged in a flow-technical manner between the drive device and the exhaust gas aftertreatment device; in the latter case, the exhaust sensor is arranged downstream of the exhaust gas aftertreatment device, i.e. in a flow-technical manner between the exhaust gas aftertreatment device and the tailpipe, via which the exhaust gas is discharged into the external environment. From the exhaust value, a monitoring of the emissions of the drive device can be carried out. For example, a device defect in the drive device is identified when the exhaust value exceeds an exhaust threshold value.

[0012] However, the exhaust value determined using the exhaust sensor is primarily used to identify a system defect of one of the systems, in particular of the system that has caused the exhaust value to exceed the exhaust threshold value. Thereby, a potential system defect of one of the systems is indicated, in particular, without first having to check the plurality of systems, for example, within the framework of a service of the drive device. This process can also be referred to as "pinpointing".

[0013] Of course, only one exhaust value can be used. However, it is preferred to use a plurality of exhaust values, in particular for a plurality of different exhaust components. To this end, at least one exhaust value is determined by means of at least one exhaust sensor. More preferably, one of a plurality of exhaust value ranges is assigned to each of these exhaust values. If one or more exhaust values deviate from their respective assigned exhaust value range, the procedure described for the system defect data set is carried out, for example.

[0014] The diagnosis of the plurality of systems is carried out in the framework of the device diagnosis. In contrast to the system diagnosis, this diagnosis does not consider only one of the systems, but a plurality of systems. As a result, the device diagnosis provides feedback as to which of the systems has a system defect or at least as to which of the systems there is a certain probability of having a defect. In order to carry out the device diagnosis, it is stored for each of the systems whether a system defect of the respective system influences an exhaust value, in particular in a data memory. For example, a flag is stored in the data memory for each of the systems, which flag has a first value or a first state if a system defect of the respective system influences an exhaust value and which flag has a second value if it does not influence an exhaust value. The system defect data set is used for this purpose. At least one system data set is stored for each of the systems. However, it can also be provided that a plurality of system data sets is stored for one or more of the systems, which system data sets describe different types of system defects.

[0015] The system diagnosis is carried out, in particular only when, if the exhaust value deviates from the exhaust value range, i.e. lies outside the exhaust value range. In the framework of the device diagnosis, all system defect data sets or at least a part of the system defect data sets are first put into a check list, which check list indicates or lists the system defect data sets to be checked. Subsequently, all those system defect data sets are removed from the check list for which a system defect data set is stored which does not influence an exhaust value, i.e. whose system does not cause or at most with a small probability causes a deviation of the exhaust value from the exhaust value range. Those system defect data sets which potentially have caused a deviation of the exhaust value from the exhaust value range by their system are thus retained in the check list.

[0016] It is noted here that in the framework of the present description "systems", "system data sets" and "system defects" sometimes occur in the plural. It goes without saying here that the number of systems, system data sets and system defects can always be arbitrary here, i.e. there can be zero systems, zero system data sets and zero system defects, or only one system, only one system data set and only one system defect. This applies primarily to the case where system data sets are to be removed from the check list. In this case, depending on whether the respective condition applies or not, no system data set, only one system data set or a plurality of system data sets can be removed from the list,

[0017] After the check list has been provided, the system defect data sets remaining on the check list are further checked within the framework of the device diagnosis according to the status value assigned to the respective system in order to determine potential system defects. Here, according to the status value determination, the respective system has a probability of causing the exhaust value to deviate from the exhaust value range. In other words, a device diagnosis of the respective system is performed for each of the remaining system defect data sets. It is noted that the status value of the system considered during the device diagnosis corresponds to the status value determined within the framework of the system diagnosis. Thus, the status value is not determined anew for the device diagnosis, but the value determined previously for the system diagnosis is used.

[0018] Preferably, subsequently, i.e. at the end of the device diagnosis or after the system diagnosis, a system defect is identified for one of the system data sets remaining on the check list or for the system assigned to it according to its respective status value. This identification is performed, for example, for the system whose status value exceeds or is closest to the status threshold assigned to the system.

[0019] In this way, a system causing the exhaust value to deviate from the exhaust value range can be identified with high safety. Thus, within the framework of the repair of the drive device or motor vehicle, the replacement of the system can be initiated or performed without further diagnostic effort.

[0020] Optionally, it can also be provided that the system defect data sets removed from the check list are written into a checked list. The checked list can be emptied beforehand, for example. If the check list is completely empty, i.e. does not contain any system defect data sets, after the removal of the system defect data sets, the system defect data sets are preferably selected from the checked list for which the system stores the worst, usually the largest, status value. A system failure is identified for this system.

[0021] The above-described approach is preferably only taken if the status value additionally exceeds the threshold value assigned to the system. The threshold value is preferably chosen to be smaller than a status threshold value, beyond which a system defect has already been identified. Thus, it is necessary for the identification of a system defect that the exhaust value deviates from the exhaust value range, the check list is empty or all system defect data sets have been removed from the check list and, additionally, the status value of the system having the worst status value also exceeds the threshold value. The approach is preferably applied after the device diagnosis has been performed.

[0022] The improvement of the application provides that for each of the plurality of system defect data sets, it is stored in which direction the system defect of the respective system influences the exhaust value, and in the case of a deviation of the exhaust value in a defined direction from the range of exhaust values, those system defect data sets for which system defects not influencing the exhaust value in the defined direction are removed from the check list. In addition to the information as to whether the system defect of the respective system influences the exhaust value at all, the information as to in which direction the exhaust value changes as a result of the system defect is also stored. Thus, it can be determined with higher accuracy which system is responsible for a change in the exhaust value in a defined direction or rather for a deviation of the exhaust value in this direction from the range of exhaust values.

[0023] To this end, if the exhaust value lies outside the range of exhaust values, it is determined in which direction the exhaust value has deviated from the range of exhaust values, i.e. for example whether the exhaust value is below the lower limit of the range of exhaust values or above the upper limit of the range of exhaust values. Depending on the direction in which the exhaust value lies outside the range of exhaust values, those system defect data sets for which system defects not influencing the exhaust value in this direction or rather for which only the influence in the respective opposite direction is stored are removed from the check list. The accuracy of the method is thus further improved.

[0024] The improvement of the application provides that for at least one of the systems, a plurality of system defect data sets for different types of system defects and their influence on the exhaust value are stored. Different changes in the exhaust value can occur for different types of system defects or rather for different system defects of the system. For example, when the high-pressure fuel pump delivers too high a pressure, this has a different influence on the exhaust value than when it delivers too low a pressure. Depending on the exhaust component for which the exhaust value is to be determined, for example, too high a pressure leads to a change in the exhaust value, whereas too low a pressure does not influence or only insignificantly influences the exhaust value. Thus, by storing different types of system defects and their respective influence on the exhaust value, the accuracy can be further improved. Preferably, the system is stored in the check list a plurality of times, i.e. for each different type of system defect. This is implemented in the form of a plurality of system defect data sets for the system.

[0025] The improvement of the application provides that the exhaust value is one of a plurality of exhaust values determined for different exhaust components and / or different operating states of the drive device and / or different design variants of the power plant. Thus, instead of only one unique exhaust value, a plurality of exhaust values is considered for the device diagnosis. The device diagnosis is carried out in particular when one of the plurality of exhaust values deviates from its respectively assigned range of exhaust values, i.e. lies outside this range of exhaust values. For example, there are exhaust values for different exhaust components. These can be determined using different exhaust sensors. However, it is preferred to use the same exhaust sensor for the determination, i.e. in particular by exploiting the cross-sensitivity of the exhaust sensor. For example, the exhaust sensor is a NOx sensor with NH3 cross-sensitivity. Thus, the same exhaust sensor is used for the determination of the exhaust value for different exhaust components. This is possible in particular by exploiting the cross-sensitivity of the exhaust sensor. For example, the exhaust sensor is a NOx sensor with NH3 cross-sensitivity. Thus, the same exhaust sensor is used for the determination of the exhaust value for different exhaust components. This is possible in particular by exploiting the cross-sensitivity of the exhaust sensor. xprobe. Here, the exhaust value for NO x and the exhaust value for NH3 can be determined by means of the exhaust sensor.

[0026] Additionally or alternatively, a plurality of exhaust values is determined for different operating states of the drive device. Thus, one of the exhaust values is determined for a first operating state, and another exhaust value is determined for a second operating state different from the first operating state. For example, the first operating state is a warm-up operating state, and the second operating state is a normal operating state. The warm-up operating state is preferably entered as soon as at least one of the following conditions is met: the exhaust aftertreatment device has been heated; the temperature of the exhaust aftertreatment device is below a temperature threshold; the mass of air that has flowed through the exhaust aftertreatment device since the start of operation of the drive device is below an air mass threshold; and the amount of heat introduced into the exhaust aftertreatment device since the start of operation is below a heat threshold. For example, the warm-up operating state is entered as soon as at least one of the stated conditions is met. However, it can also be provided that a plurality or all of the stated conditions must be met. If the stated conditions or rather the warm-up operating state conditions are no longer met, the normal operating state is entered.

[0027] Additionally or alternatively, different design variants of the drive device or rather of the power plant can be stored for at least one of the systems. For example, if the power plant has a plurality of cylinder groups, a system defect and accordingly its respective influence on the exhaust value can be stored for each cylinder group. For this purpose, a plurality of system defect data sets is also stored for the systems affected.

[0028] For the case in which exhaust values are used for two different exhaust components and for two different operating states of the drive device, there are a total of four exhaust values. For the above example, these are: a first exhaust value for NO x during the warm-up operating state, a second exhaust value for NO x during the normal operating state, a third exhaust value for NH3 during the warm-up operating state, and a fourth exhaust value for NH3 during the normal operating state. For each of these exhaust values, a separate exhaust value range is defined, which, when deviated from, triggers a device diagnosis. Furthermore, for each exhaust value, it is stored for each of the system defect data sets whether the system defect of the respective system influences the respective exhaust value. This approach enables particularly specific device diagnoses.

[0029] The improvement of the application provides that the exhaust measurement values are measured by means of the exhaust sensor and the exhaust value is determined from the exhaust measurement values by means of an accumulation over the distance traveled by the vehicle and a normalization by means of the distance traveled and / or by means of a normalization by means of a model value determined by means of an exhaust model. That is, the exhaust value is not measured directly by means of the exhaust sensor, but is determined from the measured exhaust measurement values. Thereby, influences, for example, due to different distances traveled and / or different driving styles, are at least partially eliminated.

[0030] For determining the exhaust value, the exhaust measurement values are first accumulated, that is, summed or integrated, in particular since the start of the journey of the motor vehicle. The start of the journey is understood in particular as the start of the drive device or power unit after the previous shutdown of the motor vehicle. The accumulated exhaust value is then normalized by means of the distance traveled since the start of the journey, so that the exhaust value is ultimately represented as a mass or weight per unit distance, for example, grams per kilometer.

[0031] Additionally or alternatively, the exhaust value is determined from the exhaust measurement values by means of a normalization by means of a model value. The model value is a result of an exhaust model, by means of which the exhaust value that theoretically exists is calculated. For example, the exhaust model uses at least one operating variable of the drive device or power unit as an input variable, for example, an operating point of the power unit, which describes the driving torque currently provided by the power unit and / or the current rotational speed of the power unit. The model value provided by the exhaust model as an output variable describes the composition of the exhaust gas when all systems are functioning perfectly. For example, the exhaust model assumes that all systems are in a brand-new state.

[0032] It is particularly preferred that the exhaust value is derived from the exhaust measurement values by means of an accumulation over the distance traveled by the vehicle and a normalization both by means of the distance traveled and by means of the model value. Thus, the exhaust value does not directly describe the composition of the exhaust gas, but only indirectly, that is, by describing the deviation between the exhaust composition and the modeled exhaust composition. By means of the described approach, influences caused by unaffected boundary conditions, for example, the driving behavior of the driver, are largely eliminated, and a reliable device diagnosis is thus achieved.

[0033] The improvement of the application provides that the measurement of the exhaust measurement values is continuous, in particular over a journey cycle of the motor vehicle. The exhaust measurement values are measured here, for example, at short time intervals, that is, multiple times during the journey cycle. The journey cycle extends from the start of the journey of the motor vehicle until the end of the journey. It is particularly preferred, however, that the exhaust value of the journey cycle is calculated only once, for example, at the start of the journey cycle or at the end of the journey cycle. In the case of multiple journey cycles, the exhaust value is determined only once for each journey cycle. This makes it possible to reliably perform a device diagnosis.

[0034] The improvement of the application provides that at least for the system defect data sets retained in the check list, the state value of the corresponding system is normalized by means of a state threshold value, and if the state value exceeds the state threshold value, the system defect of the corresponding system is identified independently of the exhaust gas measurement value. Here, as described above, a corresponding state value is determined for each system. This applies at least to those systems whose system defect data sets are retained in the check list, but of course can also be provided for all systems. Furthermore, a state threshold value is defined for each system. If the state value exceeds the state threshold value, the system defect of the corresponding system is identified independently of the exhaust gas measurement value. This approach is provided in particular within the framework of the system diagnosis, i.e. when diagnosing individual systems. This approach can also be referred to as on-board diagnosis.

[0035] Within the framework of the device diagnosis, the state threshold value is preferably taken into account for the normalization of the state value. Thus, the normalized state value exists as the state value divided by the state threshold value / is obtained by dividing the state value by the state threshold value. The normalized state value enables a statement about the state of the corresponding system. Within the framework of the device diagnosis, the normalized state value is always preferred instead of the state value, even if this is not stated separately. This approach enables a further improvement in the accuracy of the device diagnosis.

[0036] The improvement of the application provides that within the framework of the device diagnosis, at least one of the following diagnosis types is performed for the system defect data sets retained in the check list: a correlation diagnosis, a combination diagnosis, a statistical diagnosis and an invasive diagnosis. In general, after evaluating the exhaust gas values, a plurality of system defect data sets will remain on the check list. In order to further reduce the check list and finally enable a reliable statement about the system defect in one of the systems, at least one of the diagnosis types is performed, and then the system defect of the system is identified from the system defect data sets still retained on the check list, for example using the state value of the corresponding system. It can be provided that only one of the diagnosis types is performed. However, it is preferred to use a plurality of diagnosis types, in particular all diagnosis types, for the system defect data sets retained in the check list. The diagnosis types are preferably used in the specified order, but in principle other orders can also be used. The use of at least one diagnosis type enables a reliable statement about the system defect of the one system.

[0037] A refinement of the present invention provides that, within the framework of a correlation diagnosis, a diagnosis is performed for the presence of a temporal correlation between the time profiles of exhaust gas values ​​and the time profiles of state values ​​for the corresponding system of at least one system defect data set remaining on a check list. If a correlation is present for at least one of the system defect data sets, all system defect data sets for which no correlation has been determined are removed from the check list. The correlation diagnosis is used to correlate the time profiles of exhaust gas values ​​and the time profiles of state values ​​for the system for which at least one system defect data set is present on the check list. Ultimately, each system defect data set, or at least the system defect data sets still remaining on the check list, is assigned a correlation degree that reflects the degree of dependency between the profiles of exhaust gas values ​​and the profiles of state values ​​for the corresponding system. This correlation degree is present, for example, in the form of a correlation coefficient.

[0038] If it is determined within the framework of the correlation diagnosis that the change in the time variation curve of the exhaust value is consistent with the change in the time variation curve of the status value for at least one system defect data group, it is assumed that the change in the variation curve of the exhaust value is caused by the corresponding system. Therefore, only system defect data groups for which the correlation has been determined (i.e., the degree of correlation or the correlation coefficient exceeds a certain threshold value) are retained in the check list. System defect data groups for which the correlation has not been determined (i.e., their degree of correlation or the correlation coefficient is less than or equal to the threshold value) are removed from the check list. It is particularly preferred to remove from the check list only when a correlation does exist for one of the system defect data groups. By using this approach, the number of system defect data groups retained in the check list can usually be significantly reduced, thereby enabling efficient identification of system defects.

[0039] A refinement of the present invention provides for storing a coherence condition for at least one of the system defect data sets of a first system of the system, the coherence condition representing the correlation between a first change in a state value and a second change in a state value assigned to a second system. Within the framework of a coherence diagnosis, system defect data sets that do not meet the corresponding coherence condition are removed from a check list. The coherence diagnosis utilizes physical associations to link the individual system defect data sets. This is based on the observation that the first system of the first system defect data set and the second system of the second system defect data set are physically connected to one another, so that a change in the state value of the first system necessarily also results in a change in the state value of the second system, and vice versa. This coherence is expressed using a coherence condition that links the two system defect data sets. A change in the state value of the first system is also referred to as a first state value change, and a change in the state value of the second system is also referred to as a second state value change.

[0040] For example, the conjunctive condition can be one of the following conditions: both the first state value change and the second state value change are not zero; both the first state value change and the second state value change are not zero and have the same sign; both the first state value change and the second state value change are not zero and have the same magnitude within a certain tolerance.

[0041] For example, for each of the system defect data sets, a respective conjunctive condition is set with respect to each of the other system defect data sets. By way of example only, a first system defect data set relates to a load sensor and a second system defect data set relates to a hybrid adaptive device. Load sensor defects inevitably affect the hybrid adaptive device, so that changes in the state value of the load sensor also result in changes in the state value of the hybrid adaptive device. This approach achieves an effective removal of system defect data sets from the check list.

[0042] The improvement of the application provides that, within the framework of statistical diagnosis, the dispersion of the time curve of the state value is determined for the respective system at least for the system defect data sets remaining in the check list, and that the system defect data sets having a greater dispersion are removed from the check list. The basis of the statistical diagnosis is the verification of the accuracy of the state value. Under this assumption, state values having a smaller fluctuation or, respectively, a smaller dispersion of the time curve are more reliable than state values having a greater fluctuation or, respectively, a greater dispersion. For example, for each system defect data set, the standard deviation on the time curve of the state value of the respective system is calculated.

[0043] It can be provided that all system defect data sets having a standard deviation that exceeds a certain threshold value are removed from the check list. Additionally or alternatively, only the system defect data set having the smallest standard deviation is retained, in particular only if the difference between the standard deviation of this system defect data set and the standard deviation of the system defect data set having the next smallest standard deviation is greater than another threshold value. Here, the statistical validity of the state value is verified by means of statistical diagnosis. This ensures that the device diagnosis is carried out effectively.

[0044] The improvement of the application provides that, within the framework of invasive diagnosis, for a third system defect data set of the system defect data sets, at least a fourth system defect data set is selected from the system defect data sets remaining in the check list, for the system assigned to this fourth system defect data set, a change in the operating state of the power plant results in a different, in particular opposite, change in the exhaust value and / or the state value than for the system of the third system defect data set, wherein the system defect data sets whose exhaust value and / or state value do not change after the change are removed from the check list. Invasive diagnosis includes an active intervention in the operation of the power plant, i.e. a change in the operating state. For example, the change in the operating state includes a change in the composition of the fuel-fresh gas mixture, a change in the drive torque provided and / or a change in the rotational speed of the power plant.

[0045] System defect data sets that react differently, in particular oppositely, to a change in the operating state are selected as third and fourth system defect data sets. Then an operating state change is carried out. Subsequently, the exhaust value and / or the state value of the system assigned to the system defect data set is evaluated. If no change in the stored exhaust value or no change in the stored state value occurs for one or more system defect data sets, the one or more system defect data sets are removed from the check list. Thereby, other system defect data sets are effectively ruled out. It is particularly preferred that an intrusive diagnosis is only allowed and carried out if the exhaust measurement value exceeds a determined threshold value. In this case, a fault message is usually already generated, in particular when the MIL (Malfuction Indicator Lamp) is activated.

[0046] The improvement of the application provides that the exhaust component concentration of at least one exhaust component of the exhaust is determined in the case of use of an exhaust component model and is corrected by means of a correction factor determined from the exhaust value, wherein an equipment defect of the drive device is identified when the exhaust component concentration exceeds an exhaust component threshold value. The exhaust component model is used to determine the exhaust component concentration, in particular in a flow-technical manner between the power plant and the exhaust aftertreatment system. The exhaust component model assumes that all systems of the drive device are fully functional, in particular intact.

[0047] The actual state of the system, in particular for one or more exhaust components for which no measurement value can be derived by the sensor, should be taken into account by means of the correction factor. To this end, the exhaust value is taken into account in order to determine the correction factor. This correction factor is then used to correct the exhaust component concentration, for example by multiplication. If the corrected exhaust component concentration exceeds the exhaust component threshold value, it is assumed that the exhaust emissions of the drive device are no longer within the permissible range and an equipment defect of the drive device is identified accordingly. Thereby, a reliable operation of the drive device within the specified specifications is achieved.

[0048] In other words, first the exhaust component concentration of at least one exhaust component of the exhaust is determined in the case of use of an exhaust component model, i.e. for a drive device that is free of faults, i.e. in which all systems are fault-free, in particular intact. Then the exhaust component concentration is corrected by means of a correction factor. This correction factor is therefore used to describe the exhaust component concentration of an exhaust component for which no measurement value is available.

[0049] The application also relates to a drive device for a motor vehicle, in particular for carrying out the method according to the embodiments within the framework of this description, wherein the drive device has a power plant that generates an exhaust, and has an exhaust sensor that is provided and designed to determine an exhaust value that describes an exhaust component, and has a system diagnosis that determines a state value that describes the state of a respective system for a plurality of systems of the drive device.

[0050] It is provided herein that for each of a plurality of systems, information is stored in at least one system defect data set as to whether a system defect of the corresponding system affects the exhaust gas value, wherein the drive device is further configured and designed to, when the exhaust gas value deviates from an exhaust gas value range, first place the system defect data set of the system into a check list of system defect data sets to be checked and then remove from the check list those system defect data sets for which the system defect data sets that do not affect the exhaust gas value are stored, wherein a device diagnosis is performed on the system defect data sets remaining in the check list based on the status value to identify the system defect of the corresponding system.

[0051] The advantages of this embodiment or this approach of the drive device have already been pointed out above. The drive device and its operating method can be further developed according to the embodiments within the scope of this description, so that reference is made to these embodiments.

[0052] The features and feature combinations described in the description, in particular those described in the following description of the figures and / or shown in the figures, can be used not only in the respectively specified combination without departing from the scope of the present invention. Therefore, embodiments that are not explicitly shown or explained in the description and / or the figures, but which nevertheless result from or are derived from the explained embodiments, are also to be considered as covered by the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be described in more detail below based on the embodiments shown in the accompanying drawings, which do not limit the present invention.

[0054] Figure 1 A method for operating a drive system of a motor vehicle is schematically illustrated. DETAILED DESCRIPTION

[0055] Figure 1 A schematic diagram shows a method for operating a drive system of a motor vehicle. The drive system includes a power unit that generates exhaust gas and an exhaust gas sensor, which is used to determine an exhaust gas value describing the composition of the exhaust gas of the power unit. In module 1 of the method, within the framework of system diagnosis, status values ​​describing the status of the respective system are determined for each of a plurality of systems of the drive system. Within the framework of module 2, the status values ​​obtained in this manner are normalized, i.e., using status threshold values ​​assigned to the respective systems.

[0056] Furthermore, multiple system-defect data sets assigned to the systems are stored in module 3. At least one system-defect data set is assigned to each system. Provision can also be made for multiple system-defect data sets to be assigned to at least one system. In module 4, exhaust gas measured values ​​are determined using exhaust gas sensors and transmitted to module 5. Module 5 has a model value as a further input value, which is determined in module 6. This model value is determined using an exhaust gas model and describes the composition of the exhaust gas when the drive system is fully functional. In module 5, an exhaust gas value is determined from the exhaust gas measured values ​​and the model value. This exhaust gas value is present in normalized form, which can also be referred to as a normalized exhaust gas value.

[0057] In module 7, the exhaust gas values ​​are checked for deviations from the exhaust gas value range. If a deviation exists, all stored system defect data sets that do not affect the exhaust gas values ​​are removed from the check list in module 8. Subsequently, within the framework of the system diagnosis, several different diagnostics are performed, which are performed in modules 9, 10, 11, and 12. A correlation diagnosis is performed in module 9, a conjunction diagnosis in module 10, a statistical diagnosis in module 11, and an intrusive diagnosis in module 12. Within the framework of each diagnostic, as many system data sets as possible are removed from the check list.

[0058] If multiple system defect data sets are subsequently retained in the check list, the system defect data set with the greatest status value for its assigned system is selected in block 13. Subsequently, a system defect is detected for the system of the retained system defect data sets in block 14. Furthermore, it is preferably provided that a system defect in the drive unit is immediately detected in block 15 if an exhaust gas value or exhaust gas measured value exceeds a threshold value. Furthermore, it can be provided that intrusive diagnostics are activated only in this case using block 16. Additionally or alternatively, it can be provided that evaluation of the remaining system defect data sets is activated only in this case using block 17; otherwise, evaluation is prevented.

[0059] Finally, it can be provided that the status values ​​of multiple systems are directly compared with status thresholds assigned to the respective systems. If the status value exceeds the status threshold, a system defect in the respective system is immediately identified. This is indicated by arrow 18. This approach effectively singles out systems with system defects. This eliminates the need for complex troubleshooting or system diagnostics for other systems.

[0060] List of reference numerals:

[0061] 1 module

[0062] 2 modules

[0063] 3 modules

[0064] 4 modules

[0065] 5 modules

[0066] 6 module

[0067] 7 module

[0068] 8 module

[0069] 9 module

[0070] 10 module

[0071] 11 module

[0072] 12 module

[0073] 13 module

[0074] 14 module

[0075] 15 module

[0076] 16 module

[0077] 17 module

[0078] 18 arrow

Claims

1. A method for operating a drive apparatus of a motor vehicle, wherein, The drive device has a power plant which generates exhaust gas, and an exhaust gas sensor which determines an exhaust gas value which describes a composition of the exhaust gas, and within the framework of a system diagnosis, state values are determined for a plurality of systems of the drive device which describe a state of the respective system, characterized in that for each of the plurality of systems, a system defect data set is stored in at least one system defect data set which describes whether a system defect of the respective system influences the exhaust gas value, wherein in the event of a deviation of the exhaust gas value from an exhaust gas value range, the system defect data sets of the systems are initially placed in a check list of system defect data sets to be checked and subsequently removed from the check list of those system defect data sets for which a system defect data set is stored which does not influence the exhaust gas value, wherein the system defect data sets remaining in the check list are subjected to a device diagnosis in accordance with the state values for the purpose of identifying a system defect of the respective system.

2. The method of claim 1, wherein, For each of the plurality of system defect data sets, it is stored in which direction a system defect of the respective system influences the exhaust gas value, and in the event of a deviation of the exhaust gas value in a defined direction from an exhaust gas value range, those system defect data sets are removed from the check list for which a system defect data set is stored which does not influence the exhaust gas value in the defined direction.

3. The method according to any of the preceding claims, characterized in that, The exhaust gas value is one of a plurality of exhaust gas values which are determined for different exhaust gas components and / or different operating states of the drive device and / or different design variants of the power plant.

4. The method according to any of the preceding claims, characterized in that, The exhaust gas value is determined from an exhaust gas measurement value which is measured by means of the exhaust gas sensor and is normalized by means of a distance traveled by the vehicle and / or by means of a model value which is determined by means of an exhaust gas model.

5. The method according to any of the preceding claims, characterized in that, Within the framework of the device diagnosis, at least one of the following diagnosis types is performed on the system defect data sets remaining in the check list: a correlation diagnosis, a combination diagnosis, a statistical diagnosis and an invasive diagnosis.

6. The method according to any of the preceding claims, characterized in that, Within the framework of the correlation diagnosis, the presence of a temporal correlation between a time curve of the exhaust gas value and a time curve of the state value is checked for the respective system of at least the system defect data sets remaining in the check list, wherein when a correlation is present for at least one of the system defect data sets, then all system defect data sets for which no correlation is determined are removed from the check list.

7. The method according to any of the preceding claims, characterized in that, At least one first system defect data set of the system defect data sets of a first system of the systems stores at least one combination condition which represents an association between a first state value change assigned to a state value of the first system and a second state value change assigned to a state value of a second system, wherein within the framework of the combination diagnosis, those system defect data sets are removed from the check list which do not satisfy the respective combination condition.

8. The method according to any of the preceding claims, characterized in that, Within the framework of the statistical diagnosis, at least for the system defect data sets remaining in the check list, a dispersion of a time curve of the state value is determined for the respective system, and the system defect data sets with a greater dispersion are removed from the check list.

9. The method according to any of the preceding claims, characterized in that, In the framework of the invasive diagnosis, at least from the system defect data sets remaining on the check list, a fourth system defect data set is selected for which the change in the operating state of the power plant leads to a change in the state value which is different, in particular opposite, to the system of the third system defect data set, wherein after the change the system defect data sets for which the state value has not changed are removed from the check list.

10. Drive arrangement for a motor vehicle, in particular for carrying out the method according to one or more of the preceding claims, wherein The drive device has a power plant which produces exhaust gases, and the drive device is provided and designed to determine, by means of an exhaust gas sensor, an exhaust gas value which describes an exhaust gas composition, and to determine, in the framework of a system diagnosis, for a plurality of systems of the drive device, a state value which describes a state of the respective system, characterized in that for each system of the plurality of systems, in at least one system defect data set, it is stored whether a system defect of the respective system influences the exhaust gas value, wherein the drive device is further provided and designed to, in the event of a deviation of the exhaust gas value from an exhaust gas value range, initially place the system defect data sets of the systems into a check list of system defect data sets to be checked and subsequently remove from the check list those system defect data sets for which it is stored that the system defect does not influence the exhaust gas value, wherein the system defect data sets remaining on the check list are subjected to a device diagnosis in terms of the state values for the purpose of identifying a system defect of the respective system.

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