Procedures for identifying and prioritizing monitoring units to be replaced

Abstract

A method is proposed for identifying and prioritizing monitoring units (22, 28, 30, 32) to be replaced when there are interactions between the monitoring units (22, 28, 30, 32), in which method an expected increase in the emitted mass is calculated as a function of a distance traveled by at least one emission component on the basis of driving parameters and diagnostic parameters of the monitoring units, from which a first prioritization list (45) is created, which is modified by an interaction matrix (46) and correction factors (KF) such that a second prioritization list (50) can be created, depending on which an exchange of the monitoring units (22, 28, 30, 32) can take place.

Description

[0001] The invention relates to a method for identifying and prioritizing monitoring units to be exchanged when interactions exist between the monitoring units.

[0002] Modern combustion engines in vehicles feature a multitude of monitoring units and sensors that control optimal combustion and subsequent exhaust gas aftertreatment. Faulty monitoring units and sensors therefore lead to errors in the engine control system and, consequently, to increased exhaust emissions and suboptimal combustion processes.

[0003] For this reason, today's vehicles are equipped with on-board diagnostic systems designed to identify faulty sensors or monitoring units. These diagnostics include, for example, checking the plausibility of the measured value, i.e., whether the value of the monitoring unit is within a permitted range for the respective operating state, whether corresponding values ​​of the monitoring units match each other, and whether measured increases in these values ​​are within a realistically possible range. These monitoring units are used for both system diagnostics and component diagnostics. These tests are intended to ensure that the engine is running correctly, thus preventing engine damage and ensuring compliance with legally permitted limits. If corresponding errors are detected, so-called emergency running programs are subsequently activated to prevent subsequent damage.

[0004] Furthermore, on-board diagnostics are used to simplify maintenance by identifying faulty monitoring units by reading the engine control units in the workshop.

[0005] However, it has been shown that monitoring units that are not defective are often replaced in the workshop, requiring additional monitoring units to be replaced until the correct fault is identified. The main reason for such incorrect replacements is that the individual monitoring units partially influence each other, resulting in a sensor being displayed as faulty, but its reading being incorrect due to a fault in another sensor. Examples of this include the influence of the oxygen sensor or the lambda sensor on the fuel injection system, as well as the detection of misfires.

[0006] The task therefore arises of creating a method for identifying and prioritizing monitoring units to be replaced when interactions exist between the monitoring units. This method significantly increases the probability of correctly identifying and, accordingly, replacing a truly defective monitoring unit. Accordingly, the replacement of still correctly functioning sensors or monitoring units should be avoided wherever possible.

[0007] This object is achieved by a method for identifying and prioritizing monitoring units to be replaced in the presence of interactions between the monitoring units having the features of the main claim 1.

[0008] In the method according to the invention for identifying and prioritizing monitoring units to be replaced when interactions between the monitoring units exist, driving parameters of a vehicle are first recorded and stored over a defined number of driving cycles. This typically occurs in the vehicle's engine control unit. In addition, diagnostic parameters of each monitoring unit are also recorded and stored from the diagnostic statistics of the monitoring units over the defined number of vehicle driving cycles, and a damage status of the respective monitoring unit is determined from this. The monitoring units monitor both individual components and entire systems, or simply existing parameters. This occurs during on-board monitoring of the vehicle.Subsequently, an expected change in the emitted mass of at least one defined emission component is determined and stored as a function of a distance traveled, taking into account not only the driving parameters but also the damage status and an aging factor of the vehicle's monitoring units. This change is therefore usually an increase, since if damage or aging of the monitoring units is detected, a deterioration in combustion or the efficiency of the exhaust gas aftertreatment units can be expected. Furthermore, an initial prioritization list is created from the existing damage states of the individual monitoring units. This means that, depending on the frequency and severity of the errors during the stored driving cycles, a value is available for each monitoring unit that represents a measure of the damage present in this monitoring unit.Furthermore, an interaction matrix is ​​stored, which determines whether a damage state of one monitoring unit influences the damage state of another monitoring unit. This matrix is ​​unchanged and simply contains every possible influence of a monitoring unit by damage of another monitoring unit. Additionally, a correction factor is stored, which, in the event of such an interaction and if a threshold value of the damage state of the influencing monitoring unit is exceeded, is used to correct the determined expected increase in the emitted mass of the affected monitoring unit as a function of the distance traveled. This correction factor is thus defined for every possible cross-influence between the monitoring units.A second prioritization list is then created for the monitoring units based on the magnitude of the corrected expected increases in emitted mass. The monitoring units are then replaced according to the order of this second prioritization list. In this process step, the previously calculated increase in emissions for the monitoring unit affected by damage to another monitoring unit is corrected. This affected monitoring unit is moved accordingly in the prioritization list based on the correction factor. As a result, identified damage states for one monitoring unit no longer directly lead to its replacement if another monitoring unit that cross-influences this monitoring unit also has a damage state that lies above a defined threshold.Since additional influences can also be stored in the prioritization lists or the correction factors, such as known, frequently occurring damages of a corresponding monitoring unit, this provides workshops with a tool with which the frequency of replacing non-faulty monitoring units can be significantly reduced.

[0009] For example, the costs of replacing monitoring units are preferably considered in a repair logic in addition to the second prioritization list. This can be done via correction factors or as an additional factor in the second prioritization list. This primarily reduces the unnecessary replacement of very costly spare parts.

[0010] The driving parameters used to calculate emissions are preferably the vehicle's load, speed, and distance traveled. These values ​​are usually sufficient to reliably determine emissions using the exhaust model.

[0011] Preferably, a driving data acquisition matrix is ​​created from the driving parameters and the integrated air mass, which is used as an input value to calculate the expected change or increase in the emitted mass as a function of the distance traveled by at least one defined emission component. Accordingly, the increase in the emitted mass is always derived from the actual driving parameters.

[0012] Furthermore, a misfire matrix is ​​preferably created from the engine speed, load, and number of misfires. This matrix is ​​used as an input value to calculate the expected increase in emitted mass as a function of the distance traveled by at least one defined emission component. This also serves to calculate the actual existing emissions.

[0013] In a further advantageous embodiment, an emission impact map for the emission load is stored for each monitoring unit. This map stores the load expected at the respective monitoring unit with the respective emission component, assuming a complete damage state and complete aging of the respective monitoring unit, as a function of the load state and engine speed. The data is corrected using the determined damage states and aging factors. The increases in emission load with the respective exhaust gas component at the respective monitoring unit are subsequently calculated by matrix multiplication with the driving data acquisition matrix. This emission impact map can be obtained using an exhaust gas model or real measurements and thus serves as the basis for calculating the following estimated emission impacts due to aging or damage states of the monitoring units.

[0014] The monitoring units preferably comprise at least one monitoring unit for measuring the oxygen concentration, one monitoring unit for the fuel supply system, one monitoring unit for detecting combustion misfires or misfires, and one monitoring unit for determining the air-fuel ratio per cylinder. These can be formed, in particular, by an oxygen sensor or a lambda sensor, a misfire detector for detecting combustion misfires or misfires, via which irregularities between the cylinders can be determined, and a lambda sensor with corresponding detectors for detecting irregularities in engine operation. In the latter case, an irregularity in the air-fuel ratio in the cylinders can be determined from the data.These sensors and monitoring units are the most important sensors for calculating emissions and for correctly controlling the combustion engine.

[0015] It is also advantageous if the repair logic is connected via a network to a repair statistics system that collects error frequencies of the monitoring units. These can then also be included in the prioritization list, allowing known errors to be taken into account, leading to further optimization of the order of the monitoring units to be replaced.

[0016] Furthermore, it is preferred if a plurality of driving cycles are stored in a memory unit, wherein the stored data includes at least the driving data acquisition matrix and the damage status data from the on-board diagnostics. Accordingly, a larger amount of data is available, which again leads to an improvement in the priority list.

[0017] In a particularly preferred embodiment, a defined number of driving cycles are stored in which at least one of the emission measurement values ​​is exceeded. If the defined number of driving cycles is exceeded, the oldest driving cycle is overwritten by the most recent driving cycle. This way, only the driving cycles in which errors occurred are taken into account. This allows the total number of driving cycles to be stored to be reduced, thus reducing the storage space used.

[0018] In a further advantageous embodiment, the aging factor is determined as a function of the oxygen storage capacity of the catalyst and taken into account accordingly.

[0019] Preferably, the driving data acquisition matrix, the emission impact maps, the interaction matrix, and a correction factor table with the correction factors are stored on a computer's repair logic or web-based. Workshops can then be provided with this data on their computers, and the data can be collected to potentially improve the correction factors using artificial intelligence.

[0020] Thus, a method is provided for identifying and prioritizing monitoring units to be replaced in the case of existing interactions between the monitoring units, by which existing interactions between the individual damage states of the monitoring units are taken into account, whereby actually defective monitoring units can be identified more precisely.

[0021] An embodiment of a method according to the invention for identifying and prioritizing monitoring units to be replaced in the presence of interactions between the monitoring units is described below with reference to the figures. The Fig. 1 shows schematically the sequence of the method according to the invention. The Fig. Figure 2 shows a schematic example of how to create a prioritization list when an oxygen sensor error occurs. The Fig. Figure 3 shows a schematic example of how to create a prioritization list when two errors occur on monitoring units and a multiple dependency.

[0022] In the method according to the invention, driving parameters are first recorded and stored in the vehicle 10 in a driving cycle N via an engine control unit 12. In the present exemplary embodiment, these include the load state 14 of the internal combustion engine, the speed 16, the distances 18 traveled, and the resulting mileage. Furthermore, misfire events 20 are detected via a monitoring unit 22 for detecting misfires. A driving data acquisition matrix 24 is created from the driving parameters, which contains an engine speed 21 and the load state 14 plotted against the associated integrated air mass. For monitoring misfires, such a matrix 26 can also be applied over the detected misfires.

[0023] In addition, the combustion engine and the exhaust system are monitored via a variety of monitoring units during on-board monitoring. In the present exemplary embodiment, these include, in addition to the aforementioned monitoring unit 22 for detecting misfires, an oxygen sensor or lambda probe 28, a fuel system monitoring unit 30, which may include, for example, a fuel pressure sensor and sensors for measuring fuel consumption or valve opening times, and a monitoring unit 32 for monitoring any irregularities in the air-fuel ratio per cylinder. If one or more of the monitoring units 22, 28, 30, 32 output an implausible value, this is stored as a damage condition (SOD) as a diagnostic parameter in diagnostic statistics 33.Plausibility refers to the verification of whether a value from the monitoring unit lies within a defined range in the respective operating state, whether corresponding values ​​match each other, and whether measured increases in the values ​​are within a realistically possible range. The more frequently implausible values ​​are output by a monitoring unit 22, 28, 30, 32 and the greater the deviation from the actual expected value, the higher the determined damage state (SOD) of the respective monitoring unit 22, 28, 30, 32.

[0024] The information stored in this way in the engine control unit 10 is stored for each driving cycle N. For this purpose, a storage unit 34 always contains, for example, ten driving cycles in which a limit violation of the measured emissions values ​​was determined, thus indicating a damage condition SOD of one of the monitoring units 22, 28, 30, 32. After more than ten stored driving cycles N, the oldest driving cycle is deleted and replaced by the most recent.

[0025] If the driver of vehicle 10 is subsequently alerted to a fault in the system, for example by the engine light coming on, he takes his vehicle to a workshop.

[0026] In the workshop, the data from the stored driving cycles N to N+9 are read out on a computer, and an emission load for the monitoring units 22, 28, 30, 32 for the stored driving cycles N to N+9 is calculated using an exhaust gas model. First, the emissions are calculated based on the driving data acquisition matrix 24. For this purpose, emission influence maps 38, 40, 42, 44 are stored for each monitoring unit 22, 28, 30, 32, from which the expected emissions in such a driving cycle N to N+9 can be read out. These emission influence maps 38, 40, 42, 44 are created on the basis that the respective monitoring unit 22, 28, 30, 32 is no longer functional, has a damage state of 100%, or is completely aged.These emissions of a defined emission component are then corrected depending on the damage state SOD of the respective monitoring unit 22, 28, 30, 32 and an aging factor AF. This means that if the monitoring unit 22, 28, 30, 32 has a damage state of less than 100%, the corresponding emission load value at this monitoring unit is corrected downwards. A subsequent matrix multiplication with the driving data acquisition matrix 24 calculates an estimated emission impact EMI of a defined emission component in mg / km. From this, an emissions table is created that shows, for example, by how many mg / km of nitrogen oxide the emission load of the monitoring units 22, 28, 30, 32 increases due to the previous calculations.From this, a first prioritization list 45 is created, with the monitoring unit 22, 28, 30, 32, which shows the largest increase based on the emissions table, taking first place in the prioritization list 45. The following monitoring units 22, 28, 30, 32 follow in descending order of emission increase.

[0027] Furthermore, the computer contains a repair logic 36 with an interaction matrix 46, which stores the extent to which a damage state SOD of one monitoring unit 22, 28, 30, 32 influences the determined damage state SOD of another monitoring unit 22, 28, 30, 32. This interaction matrix 46 represents the damage limit above which one monitoring unit influences another. The y-axis represents the monitoring units 22, 28, 30, 32 in a damage state SOD, and the x-axis represents those monitoring units 22, 28, 30, 32 that can be influenced. If the damage state SOD exceeds the threshold value specified in the interaction matrix 46, the corresponding monitoring unit 22, 28, 30, 32 specified on the x-axis is considered to be influenced.

[0028] A corresponding example is in the Fig. 2. The first table shows the first prioritization list 45, which contains the determined SOD damage states and the calculated nitrogen oxide emission increases of the four monitoring units 22, 28, 30, 32 specified here as examples. The limit value for an SOD damage state of a monitoring unit 22, 28, 30, 32, above which another monitoring unit 22, 28, 30, 32 is influenced, is specified in an interaction matrix 46. The only monitoring unit that exceeds one of the specified limit values ​​is the lambda sensor 28 with 80%, which exceeds the limit value 75 for a possible influence on the monitoring unit 22 for detecting misfires. Thus, in each row of the interaction matrix 46, it is read whether one of the threshold values ​​specified there is exceeded by the determined damage state of the respective monitoring unit 22, 28, 30, 32.This only applies to the misfire monitoring unit 22, where the threshold value is specified as 75%, which is exceeded by the 80% damage state of the lambda sensor 28. The value of the misfire monitoring unit 22 must be corrected accordingly.

[0029] For this purpose, a correction factor table 48 with correction factors KF is used, which can depend on various factors and are measured or defined during the development of the vehicle 10. Using artificial intelligence, this correction factor can be modified later in the field based on new field data, since the associated software runs not in the vehicle but on the computer's repair logic 36 or is web-based. This also allows consideration of whether the field data indicates that a monitoring unit 22, 28, 30, 32 is more error-prone or subject to faster aging.

[0030] In the present example, the relevant correction factor KF for the affected monitoring unit for the detection of misfires 22 is 0.4. For the monitoring unit 22 for the detection of misfires, an increase of 10 mg / km was calculated for the emission component nitrogen oxide, whereby this value is listed in rank 1 in the first prioritization list 45. By multiplying by the correction factor KF of 0.4, this increase is reduced to 4 mg / km, whereby the monitoring unit 22 for the detection of misfires is moved to third rank in a newly created second prioritization list 50, while the monitoring unit 32 for determining the air-fuel ratio per cylinder is now moved to first rank with an 8 mg / km increase due to the damage conditions.Thus, the cylinder air-fuel ratio monitoring unit 32 would be the first monitoring unit to be replaced in the workshop to correct the fault.

[0031] A further embodiment of this creation and calculation of the second priority list 50 is shown in the Fig.3. The calculated nitrogen oxide increase based on the measurement data is 10 mg / km for the monitoring unit 22 for determining misfires, 8 mg / km for the monitoring unit 32 for the air-fuel balance, 5 mg / km for the monitoring unit 28 for measuring the oxygen concentration, i.e., the oxygen sensor or lambda probe, and 1 mg / km for the monitoring unit 30 for the fuel system. Here, the damage state of both the monitoring unit 28 for measuring the oxygen concentration and the monitoring unit 32 for the air-fuel balance partially exceeds the specified threshold values. Both values ​​are 80% here. This means that the determined increase in nitrogen oxide of the monitoring unit 22 for determining misfires must be corrected due to the damage state SOD of the monitoring unit 28 for measuring the oxygen concentration, as in the first exemplary embodiment.The SOD damage condition of the monitoring unit 32 for measuring the air-fuel ratio per cylinder means that, in addition to the nitrogen oxide increase determined by the monitoring unit 22 for detecting misfires, both the nitrogen oxide increase determined by the monitoring unit 28 for measuring the oxygen concentration and the fuel system monitoring unit 30 must also be corrected. Thus, only the increase value of the monitoring unit 32 for measuring the air-fuel ratio remains unaffected, while the other three values ​​are corrected using the KF correction factors.This value is thus 2.5 mg / km for oxygen concentration monitoring unit 28, multiplied by the correction factor 0.5, 0.2 mg / km for fuel system monitoring unit 30, and 4 mg / km for misfire detection monitoring unit 22, multiplied by the correction factor 0.4. As a result, air-fuel ratio monitoring unit 32 is moved from second to first place in the second prioritization list 50, misfire detection monitoring unit 22 is moved from first to second place, and fuel system and oxygen concentration monitoring units 28 and 30 remain unchanged in the third and fourth places.

[0032] This procedure can thus shift the priorities for replacing monitoring units by taking into account the mutual influence of the various monitoring units in the workshop in addition to the existing identified damage conditions. This is done depending on the available driving data and, if applicable, also on existing experience with occurring errors when additional statistics are used for the correction factor or elsewhere.

[0033] It should also be clear that, in addition to the monitoring units described, all other monitoring units and sensors can be considered. Furthermore, in addition to the impact of an increase in the nitrogen oxide content, an increase in any other emission component can also be calculated and considered. Of particular note here is the ammonia content, which is also suitable for this method. In addition, the costs incurred by a replacement can also be considered in the final prioritization.

Claims

[1] Procedure for identifying and prioritizing monitoring units (22, 28, 30, 32) to be exchanged in the presence of interactions between the monitoring units (22, 28, 30, 32) comprising the following steps: - Recording and storing driving parameters over a defined number of driving cycles (N) of a vehicle (10), - Acquisition and storage of diagnostic parameters of each monitoring unit (22, 28, 30, 32) from a diagnostic statistic (33) of the monitoring units (22, 28, 30, 32) over the defined number of driving cycles (N) of the vehicle (10) to determine a damage state (SOD) of the respective monitoring unit (22, 28, 30, 32), - Determining and storing an expected increase in the emitted mass as a function of a distance traveled of at least one defined emission component based on the driving parameters, the damage state (SOD) and an aging factor (AF) of the monitoring units (22, 28, 30, 32) of the vehicle (10), - Establishing an initial prioritization list (45) depending on the respective damage status (SOD) of the monitoring units (22, 28, 30, 32), - Storing an interaction matrix (46) which specifies whether a damage state (SOD) of one of the monitoring units (22, 28, 30, 32) affects a damage state (SOD) of another monitoring unit (22, 28, 30, 32), - Storing a correction factor (CF) with which, in the event of such an interaction and exceeding a threshold value of the damage state (SOD) of the influencing monitoring unit (22, 28, 30, 32), the determined expected increase in the emitted mass of the influencing monitoring unit (22, 28, 30, 32) is corrected as a function of the distance traveled, - Creating a second prioritization list (50) depending on the size of the expected increases in the mass emitted for the monitoring units (22, 28, 30, 32) corrected by the correction factor (CF) depending on the distance traveled, depending on which an exchange of the monitoring units (22, 28, 30, 32) takes place. [2] Method for identifying and prioritizing monitoring units to be replaced according to claim 1, characterized by, that in addition to the second prioritization list (50) in a repair logic (36) the costs for replacing the monitoring units (22, 28, 30, 32) are taken into account. [3] Method for identifying and prioritizing monitoring units to be replaced according to claim 1 or 2, characterized by , that the driving parameters from which the emission load is calculated are the load state (14) of the vehicle (10), the speed (16) and the distance traveled (18). [4] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by , that a driving data acquisition matrix (24) is created from the driving parameters and the integrated air mass, which is used as an input value to calculate the expected increase in the emitted mass as a function of a distance traveled of at least one defined emission component. [5] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by , that a misfire matrix (26) is created from the engine speed (21), the load condition (14) and the number of misfires, which is used as an input value to calculate the expected increase in the emitted mass as a function of a distance traveled by at least one defined emission component. [6] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by, that for each monitoring unit (22, 28, 30, 32) an emission influence map (38, 40, 42, 44) is stored for the emission load, in which the load to be expected at the respective monitoring unit (22, 28, 30, 32) with the respective emission component is stored assuming a complete damage state (SOD) and a complete aging of the respective monitoring unit (22, 28, 30, 32) as a function of the load state (14) and the engine speed (21), whose data are corrected by the determined damage states (SOD) and aging factors (AF), and subsequently the increases in the emission load with the respective exhaust component at the respective monitoring unit (22, 28, 30, 32) are calculated by matrix multiplication with the driving data acquisition matrix (24). [7] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by, that the monitoring units (22, 28, 30, 32) include at least one monitoring unit (28) for measuring the oxygen concentration, one monitoring unit (30) of the fuel system, one monitoring unit (22) for detecting misfires or ignitions, and one monitoring unit (32) for determining the air-fuel ratio per cylinder. [8] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by , that the repair logic (36) is connected via a network to a repair statistics system in which error frequencies of the monitoring units (22, 28, 30, 32) are collected. [9] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by, that a plurality of driving cycles are stored in a storage unit (34), wherein the stored data includes the driving data acquisition matrix (24) and the damage status data (SOD) from the on-board diagnostics. [10] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by , that a defined number of driving cycles are stored in which at least one of the emission measurement values ​​is exceeded, whereby if the defined number of driving cycles is exceeded, the oldest driving cycle is overwritten by the newest driving cycle. [11] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by that the aging factor (AF) is determined as a function of the oxygen storage capacity of a catalyst. [12] Method for identifying and prioritizing monitoring units to be replaced according to any of the preceding claims, characterized by , that the driving data acquisition matrix (24), the emission influence maps (38, 40, 42, 44), the interaction matrix (46) and a correction factor table (48) with the correction factors (KF) are stored on a repair logic (36) of a computer or web-based.

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