Patent Publication Number: US-2015088399-A1

Title: Exhaust system and method of estimating diesel particulate filter soot loading for same

Description:
TECHNICAL FIELD 
     The present teachings generally include a method of estimating soot loading in a diesel particulate filter and an exhaust system implementing the method. 
     BACKGROUND 
     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. 
     SUMMARY 
     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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of a vehicle exhaust system including a diesel particulate filter and a controller. 
         FIG. 2  is a schematic diagram of the controller of  FIG. 1 , including a processor with an engine-out soot model, a DPF soot loading model based partly on the engine-out soot model, a DPF soot loading pressure-based model, and a learning algorithm for the engine-out soot model. 
         FIG. 3  is a schematic three-dimensional plot of engine-out soot rate, showing engine-out soot rate at various engine operating points according to engine speed and quantity of fuel injected, and associated current and updated engine-out soot rate values at predetermined engine operating points. 
         FIG. 4  is a schematic illustration of a soot rate table showing engine-out soot rate as a function of engine speed and injected fuel quantity rate, and showing updated engine-out soot rate values for various engine operating conditions. 
         FIG. 5  is a schematic three-dimensional plot of operation time at various engine operating points according to engine speed and injected fuel quantity rate, and the distribution of operation at one engine operating point to predetermined engine operating points 
         FIG. 6  is a schematic illustration of a time table showing an engine operating point and the distribution of operation time at predetermined engine operating points having various engine speeds and at different injected fuel quantity rates. 
         FIG. 7  is a schematic flow diagram of a method of estimating soot loading carried out by the controller of  FIG. 1  via the models and learning algorithm of  FIG. 2 . 
     
    
    
     DETAILED DESCRIPTION 
     Referring to the drawings, wherein like reference numbers refer to like components throughout the several views,  FIG. 1  shows a vehicle  10  that includes an engine  11  with a representative exhaust system  12  that includes a diesel particulate filter (DPF)  14 . A monitoring system  16  for the DPF  14  is operable to monitor the amount of soot mass in the DPF  14  in order to ensure filter performance, enhance overall fuel economy and reduction of emissions, and provide for timely regeneration of the DPF  14 . 
     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  FIG. 2  to estimate the amount of soot in the DPF  14  and output a control signal  38  that causes engine operation at conditions (such as increased fuel amount) that initiate regeneration of the DPF  14 . If the DPF  14  is a type that is actively regenerated by changing operating parameters to increase exhaust flow temperature to burn the soot, the signal  38  may affect engine parameters to cause the increase in temperature of the exhaust flow  20 . 
     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 (mm 3 /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  FIG. 2 , the processor  30  is shown in more detail to represent the algorithms executed by and the empirical data accessed by the processor  30 . The processor  30  includes a first stored algorithm, also referred to as a DPF soot loading pressure-based model  50 , that provides an inferred DPF soot loading estimate based in part on the differential pressure  46  provided by the pressure measurement device  44 . The engine operating conditions  36 ,  47  are also provided to the pressure-based model  50 . The pressure-based model  50  represents the dynamics of engine-out soot and DPF soot loading inferred from the pressure differential across the DPF  14 . The pressure-based model  50  can include stored data based on prior testing, including offline weighings of the DPF  14  that are coordinated with measured pressure differentials and engine operating conditions. 
     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  FIG. 7 . In a first step  102 , the controller  28  monitors engine operating conditions, including engine speed  36  and fuel quantity rate  47 . That is, the controller  28  tracks actual engine operating points within the range of engine operating conditions by periodically analyzing the engine speed  36  and fuel quantity rate  47  provided. The controller  28  also has a timer that measures the time of operation at each monitored engine operating point in step  104 . The controller  28  also periodically monitors the pressure differential  46  provided by the pressure differential measurement device  44  in step  106 . Steps  102 ,  104 ,  106  are repeated periodically throughout the method  100 . 
     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  FIG. 3 , a lookup table  57  included in the engine-out soot model  54  stores engine-out soot rate  53  (in grams per second) according to engine speed  36  (in revolutions per minute) and fuel quantity rate  47  (mm 3 /cycle). Various current soot rate values  60  are indicated with open circles (i.e., the soot rate values at each engine operating point as stored in a lookup table  57  of the engine-out soot model  54  prior to updating). Only some of the current soot rate values  60  are labeled. The initial current soot rate values  60  are based on initial soot rate values determined during offline testing for a vehicle having the engine  11  and exhaust system  12 , and are then updated during vehicle use according to the method  100  carried out by the processor  30  as described herein. Incremental soot rate values  62 A,  62 B,  62 C as determined by the pressure-based model  50  at a series of actual engine operating points as periodically determined in step  102  are indicated in  FIG. 3 . The current stored soot rate values  60  in the lookup table of the engine-out soot model  54  are updated using each of the soot rate values  62 A,  62 B,  62 C determined by the pressure-based model  50  as described herein. 
     The soot rate value  62 A determined at an actual engine operating point P x,y  is used to provide updated engine-out soot rate values  64 A,  64 B,  64 C,  64 D, 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 P x,y  that corresponds with soot rate value  62 A). 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  FIG. 4 . Current soot rate values  60  within a predetermined distance of the actual engine operating points corresponding with the engine-out soot rate values  62 B,  62 C would be updated in like manner. The updated values in table  57  will be used in the engine-out soot model  54  to calculate the estimated engine soot rate  53 . 
     Referring again to  FIG. 7 , the main steps in the enable mode include step  110 , calculating the inferred DPF soot loading {circumflex over (M)} Δp (t) from the differential pressure  46  (ΔP) measurement via the pressure-based model  50 . 
     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  62 A) 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: 
     
       
         
           
             
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       FIG. 4  shows a soot rate table as a two-dimensional plot of fuel quantity rate  47  on the Y-axis versus engine speed  36  on the X-axis. As illustrated in  FIG. 4 , current soot rate values  60  in the engine-out soot table  57  of  FIG. 3  are updated by distributing the estimated soot rate error {circumflex over (Z)} to the soot rate values  60  at the four adjacent junction points of the engine-out soot table  57 , based on respective distances between the current engine operation point (P x,y ) (corresponding to the operating point having the soot rate  62 A) and the four adjacent junction points P1, P2, P3, P4. If the distance from the engine operating point P x,y  to its four adjacent junction points P1, P2, P3, P4 are d i,j , d i,j+1 , d i+1,j , and d i+1,j+1  respectively, then these distances can be calculated in step  118  by the geometric distance formula for determining the distance between two points in a plane, e.g., for d i,j : 
         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 P x,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 P i,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  64 A,  64 B,  64 C,  64 D at time t by: 
     
       
         
           
             
               
                 
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     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  FIG. 2  via soot rate error values that are calculated in proportion to the proximity of the engine operating points of the stored values (i.e., the current soot rate values  60 ) to the engine operating point at which the difference {circumflex over (M)}(t) is calculated. The method  100  then returns to step  108 . 
     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  FIG. 5  (named “Operation Time Table”) is constructed under the method  100  to record the engine operation time  70 A,  70 B,  70 C at different engine operating points such as engine operating point P x,y  (shown in  FIG. 6 ). Engine operation time  69  as determined in step  104  is stored according to engine speed  36  and fuel quantity rate  47 . For example, at engine operating point P x,y  (e. g., corresponding with the engine operating point at which time  70 A is spent), let the engine operation time be T x,y , and then T x,y  will be distributed and recorded at the four adjacent junction points PA, PB, PC, PD surrounding P x,y  as described below. 
     The four adjacent junction points in the Operation Time Table  68  are PA, PB, PC, PD (referred to as T i,j , T i,j+1 , T i+1,j , and T i+1,j+1 .) The distance from the engine operating point P x,y  to its four adjacent junction points P1, P2, P3, P4 is d i,j , d i,j+1 , d i+1,j , and d i+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 d i,j  from point P x,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 P x,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  70 A at the engine operating point P x,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 P x,y  at which the time  70 A was measured. Then, corresponding to the engine operating point P x,y , the engine operation time distributed in step  130  at each adjacent point (i, j) in the Operation Time Table  68  is as follows: 
     
       
         
           
             
               
                 
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     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  FIG. 5  (only one of which is labeled  75 ). The updated accumulated time  77 A,  77 B,  77 C,  77 D is shown at each point. 
     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)}(t e ) (also referred to as a soot loading increment difference) 
     during the disable mode is calculated as follows: 
       Δ{circumflex over ( M )}( t   e )=[ {circumflex over (M)}   Δp ( t   e )− {circumflex over (M)}   Δp ( t   d )]−[ {circumflex over (M)}   1dk ( t   e )− {circumflex over (M)}   1dk ( t   d )];
 
     where, referring to  FIG. 2 , {circumflex over (M)} 1dk (t e ) is the output of the DPF soot loading model  52 , {circumflex over (M)} Δp (t e ) is the output of the pressure-based model  50 ; and t d  and t e  are the time of entering the disable mode (i.e., time at the first recorded engine operating point in the second set of engine operating conditions as determined in step  108  after steps  110 - 112 ), and the time of entering the enable mode (i.e., time at the first recorded engine operating point in the first set of engine operating conditions after operation in the second set of engine operating conditions as determined in step  126 ), respectively. 
     Next, in step  134 , the average total soot rate error  M  (also referred to as the average total soot rate difference) during the disable mode is calculated as follows: 
     
       
         
           
             
               Δ 
                
               
                 M 
                 _ 
               
             
             = 
             
               
                 
                   Δ 
                    
                   
                     
                       M 
                       ^ 
                     
                      
                     
                       ( 
                       
                         t 
                         e 
                       
                       ) 
                     
                   
                 
                 
                   
                     t 
                     e 
                   
                   - 
                   
                     t 
                     d 
                   
                 
               
               . 
             
           
         
       
     
     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  M  to each junction point, where the accumulated time is recorded during the disable mode in the Operation Time Table  68 , proportionally to the recorded accumulated time as an average soot rate error Z i,j (t): 
         Z   i,j ( t )= Z   i,j ( t− 1)+[ T   i,j   Δ  M ].    
     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.