The present teachings generally include a method of estimating soot loading in a diesel particulate filter and an exhaust system implementing the method.
Diesel particulate filters (DPFs) are designed to remove soot from the exhaust flow of a diesel engine. When the accumulated soot reaches a predetermined amount, the filter is “regenerated” by burning off the accumulated soot. There is no mechanism available to directly measure the amount of soot in the exhaust flow from the engine, or to directly measure the amount of soot in the DPF when the vehicle is in use. Accordingly, mathematical and empirical soot models have been used to estimate the amount of soot present in the filter so that timely disposal or regeneration of the filter can be assured. Modeling the exhaust flow and resultant DPF loading is dependent on complex chemical reactions and physical flow dynamics. One mathematical soot model is dependent on engine operating conditions and an engine-out soot rate resulting from the engine operating conditions. Another soot model estimates the amount of soot in the filter based on the pressure drop in exhaust flow through the filter (i.e., a differential pressure across the filter). This soot model is thus based partly on a measured parameter (pressure differential). Accuracy of the soot model used is important, as the DPF functions optimally when the amount of soot present is below a predetermined amount. An accurate soot model ensures that the DPF is not regenerated unnecessarily at relatively low soot concentrations (grams of soot per volume of filter), thus enhancing fuel economy.
A DPF soot loading estimate using a mathematical model implemented by an onboard computer as an algorithm can be less expensive than measurement-based models that require numerous and/or expensive sensing devices, and can be used under a greater range of operating conditions than a measurement-based system. The accuracy of such a mathematical model can be improved if the model is updated by comparison of a model-based result with a measurement-based result, such as the pressure-based model. However, accurate DPF soot loading has been determined from offboard testing, in which the DPF is periodically removed from the exhaust system and weighed,—since the pressure-based model is only an accurate predictor of soot loading under certain engine operation conditions, such as high speed steady driving.
A method of estimating soot loading is presented that enables reliance on a mathematical soot loading model, referred to herein as a DPF soot loading model, by updating an engine-out soot rate used in the mathematical model based on a differential pressure-based model under all engine operating conditions. A method of estimating soot loading in a DPF in a vehicle exhaust system includes determining engine operating conditions of an engine in exhaust flow communication with the diesel particulate filter, and monitoring a pressure differential of the exhaust flow across the diesel particulate filter. The method includes estimating soot loading in the diesel particulate filter according to a pressure-based model using the monitored pressure differential when the engine operating conditions are within a predetermined first set of engine operating conditions (defining an enable mode), and estimating soot loading in the diesel particulate filter according to an engine-out soot model and a DPF soot loading model when the engine operating conditions are within a predetermined second set of operating conditions (defining a disable mode). In both cases, the estimating is via an electronic controller. The engine-out soot model and the DPF soot loading model are stored on the electronic controller. The engine-out soot model is based on the engine operating conditions, and the DPF soot loading model is based at least partially on the engine-out soot model.
The method includes updating the engine-out soot model based in part on a difference in estimated soot loading between the pressure-based model and the DPF soot loading model. Updating the engine-out soot model is done in real time during the enable mode. As used herein, updating in “real time” means updating the engine-out soot model based on the difference without first requiring the occurrence of a subsequent event or condition. Updating the engine-out soot model is done after a return to engine operating conditions within the enable mode after operation in the disable mode, and is based in part on a saved estimated soot rate loading value from an engine operating point in the enable mode prior to the operation in the disable mode. That is, updating is not in real time during the disable mode, and instead occurs only after a return to the enable mode, when a pressure-differential measurement is again considered to be sufficiently indicative of soot loading.
The above features and advantages and other features and advantages of the present teachings are readily apparent from the following detailed description of the best modes for carrying out the present teachings when taken in connection with the accompanying drawings.
Referring to the drawings, wherein like reference numbers refer to like components throughout the several views,
The exhaust system 12 includes a diesel oxidation catalyst 18 that oxidizes and burns hydrocarbons in the exhaust flow 20 exiting the engine 11. Exhaust then flows through a selective catalytic reduction catalyst 22, which converts at least some of the nitrogen oxides in the exhaust flow 20 into water and nitrogen. Exhaust then flows from an inlet 24 of the DPF 14 to an outlet 26 of the filter 14, and then exits the exhaust system 12. The exhaust system 12 may instead be arranged with the selective catalytic reduction catalyst 22 downstream of the DPF 14 without affecting the function of the monitoring system 16.
The monitoring system 16 includes a controller 28 that has a processor 30 that executes stored algorithms from a tangible, non-transitory memory, as described further with respect to
Data reflecting real-time operating parameters in the exhaust system 12 is input into the controller 28 and used by various ones of the stored algorithms as described herein. For example, the monitoring system 16 may include an engine speed sensor 32 positioned in operative communication with the engine crankshaft 34 and operable to monitor engine speed 36 (also referred to as a first engine operating condition) such as in revolutions per minute (rpm) and provide a signal representing engine speed to the processor 30. Additionally, the monitoring system 16 includes a sensor 37 that measures air fuel ratio in the engine 11 and provides an air fuel ratio 42 via a signal to the processor 30. The monitoring system 16 also includes a sensor 39 that measures air flow into the engine 11 and provides an air flow measurement 43 via a signal to the controller 28. A fuel flow measuring device 49 measures an injected fuel quantity rate 47 (also referred to as a second engine operating condition) such as the fuel flow in cubic millimeters per engine stroke (mm3/cycle) into a fuel injection system for the engine 11. The fuel quantity rate 47 is provided as a signal to the processor 30. Fuel quantity rate 47 is proportional to engine load (e.g., torque at the crankshaft 34). Additional engine operating parameters and exhaust system 12 operating parameters can also be provided to the controller 28 and used by the stored algorithms on the processor 30 to estimate the amount of soot loading in the DPF 14. For example, exhaust temperature and other parameters can be monitored.
The monitoring system 16 also includes a differential pressure measurement device 44 that is operable to measure a third operating parameter, which is a pressure differential between exhaust flow at the inlet 24 and exhaust flow at the outlet 26 of the DPF 14. The differential pressure measurement device 44 is in fluid communication with the exhaust flow 20 at the inlet 24 and at the outlet 26 and emits a signal representative of a differential pressure 46 (also referred to as a pressure drop). The differential pressure 46 is utilized by the processor 30 as further described below.
Referring to
The processor 30 includes a second stored algorithm, also referred to as a DPF soot loading model 52 that provides an estimated DPF soot loading based on a mathematical model of the DPF kinetic process. The mathematical model is dependent on the engine operating conditions 36, 47, as well as an estimated engine-out soot rate 53 provided as a signal from an engine-out soot model 54. The engine-out soot model 54 is an input to the DPF soot loading model 52, as it provides an estimated engine-out soot rate 53 used by the DPF soot loading model 52. The engine-out soot model 54 is a group of stored lookup tables of engine-out soot rate values correlated with the selected engine operating points. An engine operating point is represented by an engine speed and by an injected fuel quantity rate in grams.
Finally, a learning algorithm 56 is utilized that provides an output 59 that is an adaptation of the engine-out soot model 54 to update the engine-out soot model 54 under all engine operating conditions using a comparison of the estimated soot loading by the pressure-based model 50 and the estimated soot loading by the DPF soot loading model 52. By updating the engine-out soot model 54 under all engine operating conditions based on this comparison, the DPF soot loading model 52 can provide a more accurate estimated DPF soot loading estimate adapting to different engine operation conditions. The pressure-based model 50 more accurately reflects actual DPF soot loading than does the DPF soot loading model 52 under a first set of engine operating conditions (the enable mode), and can thus be used as a check to update the DPF soot loading model 52. However, the pressure-based model 50 is less accurate under other engine operating conditions (a second set of engine operating conditions called the disable mode). For example, at low engine speeds, or non-steady (transient) driving, the differential pressure 46 is less correlated with DPF soot loading than at high-speed, steady driving.
The learning algorithm 56 enables the engine-out soot model 54 to be updated to reflect engine operation in the disable mode as well as in the enable mode, as described herein. In other words, the learning algorithm 56 extends updating of the engine-out soot model 54 and the DPF soot loading model 52 to an entire engine operating range (which is defined as the total of the first set of engine operating conditions and the second set of engine operation conditions). The learning algorithm 56 continuously adapts the engine-out soot model 54 and the DPF soot loading model 52 to the pressure-based model 50.
The learning algorithm 56 thus operates in one of two different operating modes: the disable mode or the enable mode, dependent on the engine operating conditions. In the disable mode, measurement of the pressure differential 46 is relatively inaccurate. The disable mode is defined as the engine operating conditions 36, 47 being within the second set of engine operating conditions. In the disable mode, there is no real-time learning for (i.e., updating of) the engine-out soot model 54. The second set of engine operating conditions reflects low speed driving and/or start-stop driving. In the enable mode, the measured differential pressure 46 is relatively accurate, and the learning algorithm 56 provides real-time learning of the engine-out soot model 54 as described herein. The learning algorithm 56 determines and saves certain operating parameters during the disable mode, and then updates the engine-out soot model 54 based on the saved operating parameters when the engine operating conditions return to the enable mode. Accordingly, the learning algorithm 56 is effective to update the engine-out soot model 54 for all engine operating conditions, either in real time or at a later time, as described herein.
The learning algorithm 56 accomplishes different tasks depending on whether it is in the enable mode, the disable mode, or transitioning from the disable mode to the enable mode. These tasks are described in detail herein, and are included in the method of estimating DPF soot loading 100 carried out by the controller 28 and the processor 30 thereon, as schematically illustrated in
Based on the engine operating conditions determined in step 102, the processor 28 determines in step 108 whether the current engine operating conditions (i.e., the most recent monitored engine operating conditions) are within the first set of engine operating conditions. If the engine operating conditions are within the first set of engine operating conditions, then the learning algorithm 56 is in the enable mode, and the processor 30 accomplishes steps 110-120 as described herein.
In the enable mode, the differential pressure measurement 46 can be relied upon to accurately reflect the amount of accumulated soot in the DPF 14, and the pressure-based model 50 can thus be used to update the engine-out soot model 54 directly. Referring to
The soot rate value 62A determined at an actual engine operating point Px,y is used to provide updated engine-out soot rate values 64A, 64B, 64C, 64D, as shown above four corresponding current soot rate values 60 (i.e., the soot rate values for four engine operating conditions P1, P2, P3, P4 within a predetermined distance of the actual engine operating point Px,y that corresponds with soot rate value 62A). The predetermined distance is the increment between adjacent stored engine speed 36 values and between adjacent stored fuel quantity rate 47 values in the lookup table 57, as further described with respect to
Referring again to
In step 112, estimated DPF soot loading {circumflex over (M)}1dk(t) is then calculated from the DPF soot loading model 52. A soot loading error {circumflex over (M)}(t) (also referred to as a soot loading difference) is then calculated in step 114 by subtracting the DPF soot loading model {circumflex over (M)}1dk(t) from the inferred DPF soot loading {circumflex over (M)}Δp(t):
Δ{circumflex over (M)}(t)={circumflex over (M)}Δp(t)−{circumflex over (M)}1dk(t)
Using the accumulated time T at the engine operating point (e.g., the point having the soot rate value 62A) as determined in step 104, the estimated soot rate error {circumflex over (Z)} (also referred to as a soot rate difference) is determined in step 116 by dividing the soot loading error {circumflex over (M)}(t) by the accumulated time T:
d
i,j=√{square root over ((x−i)2+(y−j)2)}{square root over ((x−i)2+(y−j)2)}.
The total distance d from the engine operating point Px,y to these four adjacent points is:
d=d
i,j
+d
i,j+1
+d
i+1,j
+d
i+1,j+1.
Corresponding to the engine operating point Pi,j, the current soot rates values 60 at each adjacent junction point in the engine-out soot rate table 57 (i.e., soot rate values 60 at time t−1) are updated to soot rate values 64A, 64B, 64C, 64D at time t by:
where 0≦k≦1 is a distribution gain determined by experiment to keep the learning process (i.e., the updating process) stable. Accordingly, the soot rate values 60 in the lookup table 57 are updated in step 120 via the output 59 by distributing the estimated engine out soot-rate error {circumflex over (Z)} in the lookup table 57 of
After steps 110 to 120, if it is then determined in step 108 that the engine operating conditions are in the disable mode, then at this transition from the enable mode to the disable mode, the learning algorithm 54 accomplishes steps 126-138 of the method 100. First, the method 100 moves to step 126 in which the last soot loading estimate based on the pressure-based model 50 during engine operation in the enable mode is saved. The last soot loading estimate based on the DPF soot loading model 52 during engine operation in the enable mode is saved in step 127.
Once in the disable mode, a lookup table 68 shown in
The four adjacent junction points in the Operation Time Table 68 are PA, PB, PC, PD (referred to as Ti,j, Ti,j+1, Ti+1,j, and Ti+1,j+1.) The distance from the engine operating point Px,y to its four adjacent junction points P1, P2, P3, P4 is di,j, di,j+1, di+1,j, and di+1,j+1 respectively, and these distances can be calculated in step 128 by using the geometric distance formula for determining the distance between two points in a plane. For example, the distance di,j from point Px,y to point P1 is:
d
i,j=√{square root over ((x−i)2+(y−j)2)}{square root over ((x−i)2+(y−j)2)}.
The total distance d from the engine operating point Px,y to these four adjacent points is:
d=d
i,j
+d
i,j+1
+d
i+1,j
+d
i+1,j+1.
In step 130, the engine operation time 70A at the engine operating point Px,y is distributed to the four adjacent engine operating points PA, PB, PC, PD according to the proximity of each of the four points to the engine operation point Px,y at which the time 70A was measured. Then, corresponding to the engine operating point Px,y, the engine operation time distributed in step 130 at each adjacent point (i, j) in the Operation Time Table 68 is as follows:
where 0≦k≦1 is a distribution gain determined by experiment to keep the learning process (i.e., the updating) stable. The prior accumulated time 75 (if any) for operation during the second set of engine operating conditions at each of these points is shown with open circles in
In step 131, it is then determined whether the engine operating conditions have returned to the enable mode. If they have not, then the method 100 returns to step 128 and continues to distribute time accumulated at a subsequent periodic engine operating point into the Operation Time Table 68 as described. When monitoring under step 131 indicates that engine operating conditions have returned to the enable mode, reliance on the pressure differential measurement 46 is resumed. The pressure-based model 50 is used to calculate the DPF soot accumulated during the time when the DPF ΔP measurement is disabled (i.e., during the disable mode). Soot loading determined to have occurred during the disable mode is distributed into each engine operating point during the disable mode according to the time spent thereon. In order to transition from the disable mode to the enable mode, in step 132, the soot loading increment error {circumflex over (M)}(te) (also referred to as a soot loading increment difference)
during the disable mode is calculated as follows:
Δ{circumflex over (M)}(te)=[{circumflex over (M)}Δp(te)−{circumflex over (M)}Δp(td)]−[{circumflex over (M)}1dk(te)−{circumflex over (M)}1dk(td)];
where, referring to
Next, in step 134, the average total soot rate error
In step 136, the lookup table 57 of the engine-out soot model 54 is updated via the output 50 by soot rate error values that are calculated by distributing the average total soot rate error
Z
i,j(t)=Zi,j(t−1)+[Ti,jΔ
Finally, in step 138, the operation time table 68 is cleared so that it is ready for use during a subsequent occurrence of operating in the disable mode following operation in the enable mode. The method 100 then returns to step 108, with steps 102, 104, and 106 continuing periodically.
While the best modes for carrying out the many aspects of the present teachings have been described in detail, those familiar with the art to which these teachings relate will recognize various alternative aspects for practicing the present teachings that are within the scope of the appended claims.