System and method for particulate filter regeneration

Abstract

The present application relates to systems and methods for particulate filter regeneration. Methods and systems are provided for selecting a first driving route of a vehicle from a database based on a particulate filter regeneration requirement and an inferred initial driver mental state. In one example, the initial driver mental state is selected based on past driver history, and the driver mental state is updated based on the interaction between the driver and traffic during driving along the first driving route. The route selection is also updated based on the updated driver mental state.

Description

Technical Field

[0001] The present invention generally relates to methods and systems for selecting a driving route for a vehicle for particulate filter regeneration based on driver behavior. Background Art

[0002] Emission control devices such as particulate filters (PF) can reduce the amount of soot emissions from an internal combustion engine by trapping soot particles. Such devices can be passively regenerated during engine operation to reduce the amount of trapped particulate matter. However, during vehicle operation, conditions for continuous full regeneration of the PF may not be available. For example, during urban driving conditions including frequent idle stops and light-load engine operation, frequent premature termination of regeneration may occur. Premature termination of regeneration may occur due to driving behavior (e.g., frequent brake application) of the vehicle driver (also referred to herein as the operator). Premature termination may result in the need for active regeneration, resulting in increased regeneration fuel losses.

[0003] Various methods are provided for regenerating a PF during a vehicle drive cycle. In one example, as shown in US8,424,294, Schumacher et al. disclose a method for controlling a regeneration cycle of an exhaust aftertreatment system (e.g., a particulate filter) based on driver-specific information so that optimal regeneration is achieved. The driver-specific information may include driving habits, driving cycles, and driving routes used by the driver. Such driver-specific information may be used to predict a period during driving when a particulate filter may be regenerated.

[0004] However, the inventors herein have recognized potential drawbacks to the above approach. As an example, driver-specific information may not remain constant during a driving cycle, resulting in significant differences between predicted driver behavior and real-time driver behavior along a driving route. Therefore, without accounting for temporal variations in driver behavior, a route planned based on predicted driver behavior may have a PF regeneration efficiency that is different from the actual PF regeneration efficiency. Furthermore, environmental factors including traffic conditions and weather conditions can significantly affect the probability of completing regeneration within a driving cycle. Furthermore, selecting a driving route based solely on driver preference can result in higher fuel consumption and increased driving duration. Summary of the invention

[0005] In one example, the above problem can be solved by an engine method, the engine method including: at the beginning of a driving cycle, displaying a first driving route in response to each of a particulate filter (PF) load and a past driving history; and during driving along the first driving route, displaying an updated route in response to each of traffic conditions and a comparison of driving history along the first route relative to past driving history in the driving cycle. In this way, by estimating a driver's state of mind in real time and quantitatively, using the driver's state of mind to recommend a route to a vehicle driver, PF regeneration efficiency can be improved.

[0006] As an example, a vehicle controller may develop a route database for vehicle drivers based on a function of frequently used routes together with driving history on each route. Each time a trip is completed, the database may be updated with information about driver characteristics including driving practices such as pedal input, brake use, lane change frequency, vehicle start-stop frequency, etc. At the beginning of a driving cycle, the driver's initial psychological state may be predicted based on the driving history (driver characteristics) retrieved from the database. Thus, based on factors such as traffic conditions and weather conditions, the driver may have multiple psychological states and may change psychological states during the driving cycle. Each psychological state may correspond to a different PF regeneration factor, which may directly affect the probability of obtaining the desired regeneration of the PF on a given route. In response to an indication of a known endpoint (based on driver input) or a predicted endpoint (based on driving history and route prediction algorithm) and further based on the current soot level of the PF and the initial driver psychological state, one or more routes may be selected from the database and displayed to the vehicle driver in a hierarchical manner. For a given endpoint, when the PF load is above a threshold and the driver is in a first psychological state, a first route may provide a higher PF regeneration efficiency, while a second route may have a lower PF regeneration efficiency. However, for the same destination, and the same above threshold PF load but different driver psychological state, the first route may have a lower PF regeneration efficiency, while the second route may have a higher PF regeneration efficiency. Navigation instructions can then be provided based on the driver selection. During the driving cycle, the psychological state can be updated in real time based on driver interactions with traffic and environmental conditions (such as weather). During driving, a non-homogeneous state transition matrix can be used to determine changes in the driver's psychological state. When the driver's psychological state changes, the regeneration efficiency of the route can be recalibrated, and an alternative route that now provides the highest PF regeneration efficiency can be displayed. The ranking of the selected routes can be adjusted in real time based on the current driver psychological state, so that the route at the top of the list can correspond to the highest degree of PF regeneration that can be achieved.

[0007] In this way, by considering the current driver psychological state when selecting and ranking routes for particulate filter regeneration, the probability that the driver will follow the recommended route is increased. By estimating the driver psychological state in real time based on the interaction between the driver and traffic and environmental conditions and updating the ranking of the displayed routes, the probability of achieving the desired level of PF regeneration can be improved. By maintaining a database of frequently traveled routes with information including the actual PF regeneration degree obtained on each route and the driver's history on these routes, it is possible to select one or more routes from the database based on the PF regeneration requirements during future driving cycles. The technical effect of using a non-homogeneous transition matrix to estimate the change in the driver's psychological state during a driving cycle is that the changing traffic situation can be best captured while determining the current driver psychological state and its impact on PF regeneration. By correlating each different driver psychological state with a regeneration factor, the impact of driver behavior on regeneration can be quantified and explained during route planning and passive PF regeneration. In this way, by estimating an appropriate route for PF regeneration while considering the impact of the driver's psychological state, the regeneration of the system can be carried out in a timely manner, thereby reducing the overload of soot in the particulate filter and improving engine performance and particulate filter health.

[0008] It should be understood that the above summary is provided to introduce some concepts in a simplified form, which are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is solely defined by the appended claims. Furthermore, the claimed subject matter is not limited to implementations that address any disadvantages noted above or in any part of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 An exemplary embodiment of an engine system including a particulate filter is shown.

[0010] Figure 2 A flow chart illustrating an example method that may be implemented for selecting a travel route based on a PF regeneration request is shown.

[0011] Figure 3 A flow chart illustrating an example method that may be implemented for determining a driver's current mental state and the impact of the driver's mental state on route selection is shown.

[0012] Figure 4 A flow chart illustrating an example method that may be implemented for updating a database of frequently traveled routes is shown.

[0013] Figure 5A A first example display of a suggested route based on PF regeneration requirements and initial driver psychological state is shown.

[0014] Figure 5B A second example display of a suggested route based on the PF regeneration request and the updated driver psychological state is shown.

[0015] Figure 6 A state machine diagram for a driver's mental state transitions is shown.

[0016] Figure 7 A transition matrix showing the changes in the driver's mental state.

[0017] Figure 8 A table showing the regeneration influencing factor corresponding to each psychological state of the driver is shown.

[0018] Fig. 9 A table showing the scaling factor for each regeneration influence factor corresponding to the weight of the cost function associated with the route.

[0019] Fig.10 An example of prediction and dynamic selection of proposed routes for PF regeneration is shown. DETAILED DESCRIPTION

[0020] The following description relates to a system and method for selecting a route from a database containing details of frequently traveled routes and driver behavior for optimal particulate filter regeneration. Figure 1 An example engine system including a particulate filter is shown in FIG. The engine controller may be configured to execute control routines such as Figure 2 An example routine of ) can be used to select a driving route from a database based on a PF regeneration requirement. A control routine (such as Figure 3 ) to estimate the driver's current psychological state and further determine the impact of the current psychological state on the selection of the driving route that is optimal for PF regeneration. Following each driving cycle, the controller may execute a routine (such as Figure 4 ) to update the database with the learned information during the drive cycle. Figure 5A and Figure 5B An example display of a suggested route based on PF regeneration requirements and the driver's psychological state is shown. Figure 6 As shown in the state machine diagram of FIG, the driver's mental state can change between multiple states during the driving cycle, and as Figure 7 As shown in , this change can be estimated based on the transformation matrix. Different driver psychological states can correspond to Figure 8 The different regeneration influencing factors listed can affect Fig. 9 The weights of the cost function listed for route determination. Fig.10An illustrative example including prediction and selection of a proposed driving route based on PF regeneration requirements is shown at .

[0021] Figure 1 Various aspects of a vehicle system 102 having an example engine system 100 including an engine 10 are schematically shown. In one example, the engine system 100 can be a diesel engine system. In one example, the engine system 100 can be a gasoline engine system. In the depicted embodiment, the engine 10 is a supercharged engine coupled to a turbocharger 113, which includes a compressor 114 driven by a turbine 116. Specifically, fresh air is introduced into the engine 10 via an air filter 112 along an intake passage 42 and flows to the compressor 114. The compressor can be any suitable intake-air compressor, such as a motor-driven or drive shaft-driven supercharger compressor. In the engine system 10, the compressor is a turbocharger compressor mechanically coupled to a turbine 116 via a shaft 19, and the turbine 116 is driven by expanding the engine exhaust.

[0022] like Figure 1 As shown, compressor 114 is coupled to throttle valve 20 via charge-air cooler (CAC) 17. Throttle valve 20 is coupled to engine intake manifold 22. Compressed air charge from the compressor flows through charge-air cooler 17 and throttle valve to the intake manifold. Figure 1 In the illustrated embodiment, the pressure of the air charge within the intake manifold is sensed by a manifold air pressure (MAP) sensor 124 .

[0023] One or more sensors may be coupled to the inlet of the compressor 114. For example, a temperature sensor 65 may be coupled to the inlet for estimating the compressor inlet temperature, and a pressure sensor 66 may be coupled to the inlet for estimating the compressor inlet pressure. As another example, a humidity sensor 67 may be coupled to the inlet for estimating the humidity of the air charge entering the compressor. Other sensors may include, for example, an air-fuel ratio sensor, etc. In other examples, one or more of the compressor inlet conditions (such as humidity, temperature, pressure, etc.) may be inferred based on the engine operating conditions. In addition, when exhaust gas recirculation (EGR) is enabled, the sensor may estimate the temperature, pressure, humidity, and air-fuel ratio of the air charge mixture including fresh air, compressed air, and recirculated exhaust gas residual received at the compressor inlet.

[0024] Wastegate actuator 92 may be actuated open to vent at least some exhaust pressure from upstream of the turbine to a location downstream of the turbine via wastegate 90. By reducing exhaust pressure upstream of the turbine, turbine speed can be reduced, which in turn helps reduce compressor surge.

[0025] Intake manifold 22 is coupled to a series of combustion chambers 30 via a series of intake valves (not shown). The combustion chambers are further coupled to exhaust manifold 36 via a series of exhaust valves (not shown). In the depicted embodiment, a single exhaust manifold 36 is shown. However, in other embodiments, the exhaust manifold may include multiple exhaust manifold sections. A configuration with multiple exhaust manifold sections may enable outflows from different combustion chambers to be directed to different locations in the engine system.

[0026] In one embodiment, each of the exhaust valve and the intake valve can be electronically actuated or controlled. In another embodiment, each of the exhaust valve and the intake valve can be cam actuated or controlled. Whether electronically actuated or cam actuated, the timing of the opening and closing of the exhaust valve and the intake valve can be adjusted as needed for the desired combustion and emission-control performance.

[0027] Combustion chamber 30 may be supplied with one or more fuels such as gasoline, an ethanol fuel blend, diesel, biodiesel, compressed natural gas, etc. via injector 69. Fuel may be supplied to the combustion chamber via direct injection, port injection, throttle body injection, or any combination thereof. Combustion may be initiated in the combustion chamber via spark ignition and / or compression ignition.

[0028] like Figure 1As shown, exhaust gas from one or more exhaust manifold sections is directed to turbine 116 to drive the turbine. The combined flow from the turbine and the wastegate may then flow through exhaust aftertreatment devices 170 and 172. In one example, the first exhaust aftertreatment device 170 may be a light-off catalyst, and the second exhaust aftertreatment device 172 may be a particulate filter, such as a regenerable particulate filter (PF). As an example, the PF may be a diesel particulate filter coupled to the exhaust passage 104 of a diesel engine. In another example, the PF may be a gasoline particulate filter coupled to the exhaust passage 104 of a gasoline engine. The PF may be made of various materials, including cordierite, silicon carbide and other high temperature oxide ceramics. Thus, the PF has a limited capacity for retaining soot. Therefore, the PF may need to be regenerated regularly in order to reduce soot deposits in the filter so that the flow resistance caused by soot accumulation does not reduce engine performance. Passive PF regeneration may be advantageously performed during certain engine operating conditions (such as during higher engine loads) to burn or oxidize trapped particulate matter when the exhaust gas flowing through the PF has a defined composition and is above a threshold temperature. During passive PF regeneration, soot may be burned in a timely manner due to higher exhaust temperatures and also by the desired amount of oxygen present in the exhaust. Filter regeneration may be accomplished by actively heating the filter to a temperature, such as 400 to 600 degrees (such as during active PF regeneration) that will burn soot particles at a faster rate than the deposition of new soot particles by flowing an electric current. During active regeneration, spark timing may be retarded or fuel enrichment may be performed to increase exhaust temperature. Thus, active PF regeneration may increase parasitic losses of fuel consumption and energy (by powering the filter). In contrast, during passive regeneration, it may not be desirable to actively heat the PF using electric current, spark retardation, and / or fuel enrichment. In one example, the PF may be a catalytic particulate filter containing a coating of a precious metal (such as platinum) to reduce soot combustion temperature and also oxidize hydrocarbons and carbon monoxide into carbon dioxide and water.

[0029] In one example, the exhaust aftertreatment device 170 may be configured to capture NOx from the exhaust flow when the exhaust flow is lean and reduce the captured NOx when the exhaust flow is rich. In other examples, the exhaust aftertreatment device 170 may be configured to disproportionate NOx or selectively reduce NOx with the aid of a reductant. In yet another example, the exhaust aftertreatment device 170 may be configured to oxidize residual hydrocarbons and / or carbon monoxide in the exhaust flow. Different exhaust aftertreatment catalysts having any such functions may be arranged separately or together in a washcoat or elsewhere in the exhaust aftertreatment stage. All or a portion of the treated exhaust from the exhaust aftertreatment devices 170 and 172 may be released into the atmosphere via the main exhaust passage 105 after passing through the muffler 174.

[0030] An exhaust gas recirculation (EGR) delivery passage 180 may be coupled to exhaust passage 104 downstream of turbine 116 to provide low pressure EGR (LP-EGR) to the engine intake manifold upstream of compressor 114. EGR valve 62 may be coupled to EGR passage 180 at the junction of EGR passage 180 and intake passage 42. EGR valve 62 may be opened to allow a controlled amount of exhaust gas to enter the compressor inlet for desired combustion and emission control performance. EGR valve 62 may be configured as a continuously variable valve or an on / off valve. In further embodiments, the engine system may include a high pressure EGR flow path in which exhaust gas is extracted upstream of turbine 116 and recirculated to the engine intake manifold downstream of compressor 114.

[0031] One or more sensors may be coupled to EGR passage 180 for providing details about the composition and condition of the EGR. For example, a temperature sensor may be provided for determining the temperature of the EGR, a pressure sensor may be provided for determining the pressure of the EGR, a humidity sensor may be provided for determining the humidity or water content of the EGR, and an air-fuel ratio sensor may be provided for estimating the air-fuel ratio of the EGR. Alternatively, EGR conditions may be inferred from one or more temperature, pressure, humidity, and air-fuel ratio sensors 65-67 coupled to the compressor inlet. In one example, air-fuel ratio sensor 57 is an oxygen sensor.

[0032] A plurality of sensors including an exhaust temperature sensor 128 and an exhaust oxygen sensor and an exhaust pressure sensor 129 may be coupled to the main exhaust passage 105. The oxygen sensor may be a linear oxygen sensor or UEGO (Universal or Wide Range Exhaust Gas Oxygen), a two-state oxygen sensor or EGO, a HEGO (Heated EGO), a NOx sensor, an HC sensor, or a CO sensor.

[0033] The engine system 100 may further include a control system 14. The control system 14 is shown as receiving information from a plurality of sensors 16 (various examples of sensors are described herein) and sending control signals to a plurality of actuators 18 (various examples of actuators are described herein). A navigation system 154, such as a global positioning system (GPS), may be coupled to the control system 14 to determine the location of the vehicle 102 at key-on and at any other instant in time. The navigation system may be connected to an external server and / or a network cloud 160 via wireless communication 150. The navigation system 154 may determine the current location of the vehicle 102 and obtain environmental condition data (such as temperature, pressure, etc.) and road information (such as road grade) from the network cloud 160. The controller 12 may be coupled to a wireless communication device 152 for direct communication between the vehicle 102 and the network cloud 160. At the completion of the driving cycle, the database 13 may be updated with the road segment information (including driver behavior, driver psychological state, achieved particulate filter regeneration level, engine operating conditions, date and time information, and traffic information). Further, trip details including starting and ending points, stops during travel and the duration of each stop, road grade (topography) of the route, fuel consumption, travel duration, driver behavior, etc. may be stored in a database 13 within the controller 12. Information about the route and traffic may be obtained from the navigation system 154 and the external cloud 160 via wireless communication 150. Different routes in the database may be compared and ranked based on fuel efficiency, travel duration, and achievable PF regeneration levels. Details about the driver's driving pattern may be retrieved from the controller's memory and used to rank the routes. Moreover, based on one or more of frequent travel time patterns, habitual probability patterns, route-based statistical profiles, and environmental attribute profiles, the driving pattern of the vehicle driver may be known over a certain number of vehicle driving cycles. Other statistical profiles, different psychological states of the driver, and conditions for transitioning from one psychological state to another may be known and stored in the database 13. Engine operating parameters may be estimated via input from one or more sensors 16, and information may be added to the database 13. About Figure 4 The details of updating database 13 are discussed in detail.

[0034] At the beginning of a drive cycle (at vehicle key-on), based on the PF soot level, the requirement for PF regeneration during the upcoming drive cycle may be assessed and in response to the driver providing an endpoint (such as via input to the onboard navigation system), one or more routes may be selected from the database 13 based on the driver's state of mind to facilitate a higher degree of PF regeneration while optimizing fuel efficiency and travel time. Route selection may be further based on a driver-selected cost function that includes the highest fuel efficiency and the lowest travel time for the drive cycle. The selected one or more routes may then be ranked according to a weighted function of each of the particulate filter regeneration efficiency, the probability of completing a PF regeneration event, fuel efficiency, and travel time for each of the one or more routes. As an example, if based on the current PF soot level, it is inferred that PF regeneration is desired during the upcoming drive cycle, the highest ranked route displayed to the operator may be a route that is capable of providing the highest degree of regeneration while reaching the endpoint and at the same time capable of providing a certain degree of fuel economy. In the event that the driver does not select a route from the one or more routes recommended (displayed) to the driver, the upcoming road segment may be dynamically predicted based on the (driver's) driving history retrieved from the database. Furthermore, the driver may select a route from one or more recommended routes and begin traveling along the route and then deviate from the selected route. During such deviations, the controller may dynamically predict an upcoming road segment based on the driver's driving history retrieved from a database, such as preferred routes traveled during specific times of the day or specific days of the week. One or more routes or road segments may be selected from the database based on the predicted endpoint, ranked according to their particulate filter regeneration efficiency, probability of completing a PF regeneration event, fuel efficiency, and travel time for each of the one or more routes, and displayed to the driver. Figure 2 Details of route selection based on information stored in database 13 are discussed.

[0035] In this manner, in response to a driver destination selection indicated via a display of the vehicle, particulate filter soot loading may be estimated, a current location of the vehicle may be determined, one or more routes from the current location to the destination may be retrieved from a database, the one or more routes may be ranked based on each of particulate filter regeneration efficiency, fuel efficiency, and travel time for each route; and the one or more routes to the selected destination may be displayed to the driver in the order in which they are ranked.

[0036] The selection and ranking of one or more routes may be further based on a driver psychological state. The driver psychological state may represent the real-time driving behavior of the driver, such as the driver may drive more aggressively in one psychological state, while in a different psychological state, the driver may be more relaxed during the driving cycle. At the beginning of the driving cycle, a first driver psychological state may be selected from a plurality of driver psychological states stored in the database 13 based on the driver's past driving history, traffic conditions at the start of the driving cycle, and environmental conditions at the start of the driving cycle including temperature, humidity, precipitation, etc. The driver's past driving history includes routes traveled by the operator according to a function of one or more of the time of day, the day of the week, the start and end of the driving cycle, and driving characteristics, including the frequency of brake application, the average acceleration force used, and the average lane change frequency. The first driver psychological state may correspond to a first particulate filter regeneration factor. Based on the first particulate filter regeneration factor, the selection and ranking of one or more routes may be updated. Updating the ranking of the one or more routes includes ranking each of the one or more routes based on a weighted function of each of the respective regeneration completion efficiency, the probability of completing a particulate filter regeneration event, the fuel efficiency, and the time to reach the end of each of the one or more routes, the weighted function being scaled based on the first regeneration factor. Once the driver selects a route from the ranked one or more routes, navigation instructions for the operator-selected route may be displayed to the driver.

[0037] As the driver travels along the route selected by the driver, real-time interactions between the driver and traffic during the driving cycle may be learned, including one or more of a stop frequency, a lane change frequency, an accelerator pedal input, and a brake input. Based on the learned real-time interactions between the driver and traffic and a comparison of the real-time interactions between the driver and traffic during travel along the route selected by the driver relative to past driving history, the driver's mental state may be updated from a first mental state to a second mental state, the second mental state also selected from the database 13. The updated second driver mental state may correspond to a second particulate filter regeneration factor. In response to the change in the driver's mental state, the ranking of one or more routes may be updated based on a weighted function of each of the respective regeneration completion efficiencies, the probability of completing a particulate filter regeneration event, the fuel efficiency, and the time to reach the end of each of the one or more routes, the weighted function may be scaled based on the second regeneration factor. For example, in a first ranking, the highest ranked route displayed to the operator may be the route that provides the highest degree of PF regeneration, however, once the ranking is updated in response to the change in the driver's mental state, the previously highest ranked route may no longer provide the highest degree of PF regeneration, and a different route that can achieve the highest degree of PF regeneration may now be ranked first. The updated ranking of one or more routes may then be displayed to the driver for further selection. Upon completion of the drive cycle, the degree of PF regeneration achieved during the drive cycle may be learned, and the database 13 may be updated using the learned degree of PF regeneration achieved during the drive cycle, the first driver psychological state, and the updated driver psychological state.

[0038] The control system 14 may include a controller 12. The controller 12 may receive input data from various sensors 18, process the input data, and trigger various actuators 81 in response to the processed input data based on instructions or code programmed therein corresponding to one or more routines. As an example, the sensors 16 may include an exhaust oxygen sensor located upstream of the turbine 116, a pedal position sensor, a MAP sensor 124, an exhaust temperature sensor 128, an exhaust pressure sensor 129, an oxygen sensor, a compressor inlet temperature sensor 65, a compressor inlet pressure sensor 66, and a compressor inlet humidity sensor 67. Other sensors such as additional pressure, temperature, air-fuel ratio, and composition sensors may be coupled to various locations in the engine system 100. The actuators 81 may include, for example, the throttle valve 20, the EGR valve 62, the wastegate 92, and the fuel injector 69. As an example, during key-on, based on the estimated soot level on the PF via the exhaust pressure sensor 129, the controller may select an optimal route for the drive cycle based on information stored in the database and input from the navigation system 154 and the network cloud 160. The controller may then display the optimal route to the driver and, if that route is selected, monitor and learn the level of PF regeneration achieved during the trip.

[0039] In some examples, the vehicle 102 may be a hybrid vehicle with multiple torque sources available for one or more wheels 55. In other examples, the vehicle 102 is a conventional vehicle with only an engine or an electric vehicle with only (one or more) motors. In the example shown, the vehicle 102 includes an engine 10 and a motor 52. The motor 52 may be a motor or a motor / generator (M / G). When one or more clutches 56 are engaged, the crankshaft of the engine 10 and the motor 52 are connected to the wheels 55 via a transmission 54. In the depicted example, the first clutch 56 is disposed between the crankshaft 140 and the motor 52, and the second clutch 56 is disposed between the motor 52 and the transmission 54. The controller 12 may send a signal to the actuator of each clutch 56 to engage or disengage the clutch so as to connect or disconnect the crankshaft with the motor 52 and the components connected to the motor, and / or connect or disconnect the motor 52 with the transmission 54 and the components connected to the transmission. The transmission 54 may be a gearbox, a planetary gear system, or other types of transmissions. The powertrain may be configured in various ways, including parallel, series, or series-parallel hybrid vehicles.

[0040] The electric machine 52 receives power from the traction battery 58 to provide torque to the wheels 55. The electric machine 52 may also be operated as a generator to provide power to the rechargeable battery 58, such as during braking operations.

[0041] In this way, Figure 1The components enable a vehicle system including: a vehicle, a navigation system wirelessly connected to an external network, a display, an engine including an intake system and an exhaust system, the exhaust system including a particulate filter (PF) coupled to an exhaust passage and a pressure sensor coupled to the exhaust passage upstream of the particulate filter, and a controller having computer-readable instructions stored on a non-transitory memory, the instructions being used to: at the beginning of a driving cycle, display a first route based on a PF load and a first driver psychological state, and in response to interaction between the driver and traffic while traveling on the first route, display a plurality of updated routes based on a second driver psychological state, wherein the first driver psychological state is selected from a database based on each of the PF load and the driver history and a change from the first driver psychological state to the second driver psychological state is based on interaction between the driver and traffic while traveling on the first route.

[0042] Figure 2 An example method 200 is shown for selecting a driving route based on a particulate filter (PF) regeneration requirement. The method may be based on instructions stored in a memory of the controller and in conjunction with sensors from the engine system (such as reference Figure 1 Signals received by the sensors described above are used by the controller to execute instructions for performing method 200 and the remaining methods included herein. According to the methods described below, the controller may employ engine actuators of the engine system to adjust engine operation.

[0043] At 202, the routine may include determining whether a vehicle key-on event is detected. For example, it may be determined that the vehicle driver has expressed an intention to start vehicle operation. Thus, by confirming the vehicle key-on event, an upcoming vehicle driving cycle is indicated. Although referred to herein as a vehicle "key-on" event, it should be understood that the driver may indicate an intention to operate the vehicle with or without a key. For example, vehicle operation may be initiated by inserting a key (active key) into the ignition slot and moving the slot to the "on" position. Alternatively, vehicle operation may be initiated when the key (passive key) is within a threshold distance of the vehicle (e.g., in the vehicle). As another example, vehicle operation may be initiated when the driver presses the ignition button to the "on" position. The driver may also use other methods to indicate the intention to operate the vehicle. Thus, the vehicle driver's driving mode may only be known when the vehicle is operating. Therefore, if the vehicle key-on event is not confirmed and therefore the upcoming vehicle driving cycle is not confirmed, the method may end and PF regeneration may not be performed.

[0044] If a key-on event is confirmed, current vehicle and engine operating conditions may be estimated and / or measured at 204. These may include, for example, engine speed, vehicle speed, engine temperature, engine load, ambient conditions (ambient humidity, temperature, and barometric pressure, for example), boost level, exhaust temperature, manifold pressure, manifold air flow, battery state of charge, and the like.

[0045] At 206 , the exhaust pressure sensor may be located upstream of the PF based on the exhaust pressure. Figure 1 The level of soot accumulated in the exhaust PF is estimated based on input from a pressure sensor 129 in the exhaust. As the soot level in the PF increases, the exhaust back pressure may increase pumping losses, thereby affecting engine performance and increasing fuel consumption. Therefore, if the soot level increases above a threshold, the PF may be regenerated by burning at least a portion of the soot deposited thereon. However, during engine operating conditions (such as idling, lower engine loads, and lower engine temperatures), passive regeneration of the PF may be adversely affected. Incomplete or terminated PF regeneration may adversely affect engine efficiency. Therefore, when starting a new trip, the driving route may be selected taking into account the soot level on the PF so that PF regeneration may be performed in a timely manner during the trip.

[0046] At 208, the routine includes determining whether the driver has specified a destination. The driver may specify the destination via input to the onboard navigation system. If it is determined that the destination is known, the routine proceeds to step 210 to retrieve the destination from a database such as Figure 1 The database 13 in the vehicle is used to retrieve one or more routes between the starting point and the end point. The starting point (such as coordinates, geographic location) can be determined from the vehicle navigation system or from the network cloud via a wireless connection. The database is kept updated with information on frequently traveled vehicle routes. Information including the starting point and end point, the route taken, stops during the trip and the duration of each stop, traffic information for each route, date and time of travel, engine operating conditions, fuel consumption, travel duration, possible PF regeneration degree, driver driving characteristics, etc. can be obtained in the database. Reference Figure 4 An example method of updating the database during each trip is described in detail.Given the current vehicle location and the destination (as indicated by the driver), there may be multiple possible routes available in the database.

[0047] At 212, one or more routes may be selected from the database for traveling between the current vehicle location and the destination. Dynamic programming may be performed to estimate the cost associated with each route. As an example, the cost function associated with the route may be estimated by Equation 1.

[0048]

[0049] Among them J A-Bis the total cost function (the sum of the individual cost functions) associated with a particular route from origin A and destination B, E(θ) is the expected PF regeneration level for this route from point A to B, w1 is the weight associated with the expected PF regeneration level for this route, is the cost of heating the PF to a temperature at which regeneration can begin, is the fuel consumption associated with traveling the route from A to B, which includes fuel use due to backpressure-related pumping losses as the soot load on the PF increases, w4t A-B is the duration of a trip from A to B following a specific route, P(A bort |θ<θ * ) is the probability of terminating the PF regeneration process during travel from point A to point B via this particular route, and w5 is the weight associated with the probability of PF regeneration termination.

[0050] The weights w1 and w5 may be adjusted based on the PF soot level. In one example, when the current PF soot level is above a threshold and PF regeneration is expected during an upcoming drive cycle, each of the weights w1 and w5 may be increased and the weights w3 and w4 may be decreased in order to increase the cost function of routes with lower expected PF regeneration levels or with a higher probability of PF regeneration process termination. However, when PF regeneration is not expected during an upcoming drive cycle, the weights w1 and w5 may be decreased and the weights w3 and w4 may be increased in order to prioritize routes with shorter travel times and higher fuel efficiency.

[0051] The weights w1 and w5 may be further adjusted based on the current psychological state of the driver. The psychological state of the driver may affect driving characteristics (such as the application and release frequency of the accelerator pedal, the gear shift frequency, the brake application frequency), which may further affect PF regeneration. The psychological state of the driver may correspond to a regeneration influence factor (RIF), and each RIF may in turn correspond to a scaling factor for each of the weights w1 and w5. In one example, if the driver is in a first psychological state, during which the driver's driving characteristics may be optimal for PF regeneration (such as lower pedal application and release frequency, lower gear shift frequency, and lower brake application frequency), the corresponding RIF may cause the scaling factor of each of the weights w1 to w5 to be equal, so that the effect of the driver's psychological state may not change the total cost function associated with the route. As an example, an equal scaling factor of 0.2 may be assigned to each of the weights w1 to w5. Since the scaling factors of all weights are equal (the sum of the scaling factors is always equal to 1), it can be inferred that the driver's psychological state may not have any adverse effect on the total cost function of any route. In another example, if the driver is in a second psychological state during which the driver's driving characteristics may have an adverse effect on PF regeneration, the RIF corresponding to the second psychological state may result in unequal scaling factors for each of the weights w1 to w5. In order to incorporate the adverse effects of the driver's psychological state into the cost function estimate for a route, the corresponding RIF may result in higher scaling factors for w1 (the weight associated with the expected PF regeneration level for the route) and w5 (the weight associated with the probability of PF regeneration termination) relative to the scaling factors assigned to the other weights (w2, w3, and w4). By assigning higher scaling factors to w1 and w5, the separate cost functions associated with each of the expected PF regeneration level and the probability of PF regeneration termination for the route may be increased relative to separate cost functions associated with other factors (such as travel time and fuel use). In this way, the driver's current psychological state can be quantitatively accounted for in the estimate of the total cost function associated with each route between a start point and a destination. Details regarding the real-time determination of the driver's psychological state and the impact of the driver's psychological state on route selection are described in detail in the accompanying drawings. Figure 3 Described in detail in.

[0052] Thus, traffic information, such as signal phase and timing information (SPaT), as may be obtained from an external server or navigation system, may be considered while estimating the probability of the PF regeneration process being terminated in a given driving cycle and predicting the duration of the trip. For example, a higher number of traffic stops and traffic congestion may generally increase both the probability of the passive PF regeneration event being terminated and the duration of the trip. The controller may determine each of the expected PF regeneration level and the probability of PF regeneration being terminated by determining to directly account for the traffic conditions (such as increasing the expected PF regeneration level and decreasing the probability of PF regeneration being terminated as the number of traffic stops decreases). Alternatively, the controller may determine each of the expected PF regeneration level and the probability of PF regeneration being terminated based on a calculation using a lookup table, the input of which is the current traffic conditions and the output of which is the expected PF regeneration level and the probability of PF regeneration being terminated.

[0053] Once the cost functions of multiple available routes between the start point and the end point are estimated, the routes may be ranked based on the cost functions, with the highest ranked route corresponding to the lowest cost function. In one example, when PF regeneration is expected during an upcoming driving cycle (such as when the PF soot level is above a threshold), the route with the highest expected PF regeneration level and the lowest probability of termination of the regeneration event may be ranked highest. The highest ranked (recommended) route may be a route that is able to reach the end point without any significant delays and providing the highest degree of regeneration and a certain degree of fuel economy. Subsequent routes may provide a relatively lower degree of regeneration while also providing a certain degree of fuel economy, etc. In another example, when PF regeneration is not expected during an upcoming driving cycle (such as when the PF soot level is below a threshold), the displayed selected routes may be ranked based on the time and / or fuel cost taken to reach the end point, and the recommended route may be selected independently of its ability to complete PF regeneration. Therefore, the highest ranked (recommended) route may be a route that is able to reach the end point in the shortest time or use the least amount of fuel.

[0054] Once one or more routes are selected from the database and ranked, the selected routes may be displayed to the driver in the order in which they are ranked 214. The screen and user interface of the in-vehicle navigation system may be used to display the selected routes to the operator.

[0055] At 216, the routine includes determining whether the driver has selected a route from a list of recommended (displayed) routes. If it is determined that the driver has selected one of the recommended routes, the routine proceeds to 218, where navigation instructions for the selected route are provided to the driver.

[0056] At 220, as the driver follows the selected route, in addition to the passive regeneration that occurs, the controller may schedule active regeneration of the PF during travel from the start point to the end point. The scheduling may be based on the soot level and upcoming road conditions and corresponding engine operating conditions. As an example, once the soot level increases above a threshold level and the driving conditions are favorable for PF regeneration (such as when the engine load is above a threshold load and the engine temperature is above a threshold temperature), PF regeneration may be scheduled. During the scheduled PF regeneration, the temperature of the exhaust gas may be increased by transmitting power through the PF to burn the soot accumulated on the PF, thereby reducing the soot load on the PF. When the exhaust temperature is above a threshold and the exhaust has a desired chemical composition that facilitates oxidation of the soot deposited on the PF, the PF may be passively regenerated during the trip.

[0057] At 222, the routine includes determining whether the driver has detoured (deviated) from the intended route. Each of the passive regeneration and active regeneration of the PF regeneration may be affected by unexpected changes in driving conditions and traffic conditions. In one example, the detour may have a greater number of traffic signals than the intended route, and frequent traffic stops may adversely affect PF regeneration. If it is determined that there is no detour, the scheduled PF regeneration may continue and at 226, the routine includes determining whether the intended endpoint has been reached. If it is determined that the intended endpoint has not been reached, then at 224, the scheduled PF regeneration may continue.

[0058] Once the endpoint is confirmed, at 228, the soot load on the PF may be estimated via the exhaust pressure sensor and the data may be updated. By estimating the soot level remaining in the PF, it may be estimated how much soot has been burned during the regeneration process. Based on the soot removal level during the drive cycle, it may be estimated what level of PF regeneration will be achieved during travel along the route, as well as the probability of terminating the PF regeneration process during travel along the route.

[0059] At 230 , the route database may be updated with information including fuel consumption, travel time (duration), traffic information, PF regeneration schedule, PF regeneration level achieved, and driver's mental state (including conditions that trigger mental state changes) during travel along the route. Figure 4 An example method for updating the database after each trip is described in detail.

[0060] Returning to 208, if it is determined that the driver has not provided a destination, the routine proceeds to 232 to predict a possible destination based on the driver's history as stored in a database. As an example, the prediction may be performed taking into account the current vehicle location, routes that are frequently traveled during specific times of the day and specific days of the week, and the driver's psychological state. Traffic conditions (such as traffic jams) and weather conditions (such as rain or snow forecasts) near the current vehicle location may also be taken into account when predicting the destination. In one example, the prediction may be performed in increments during the trip. The vehicle controller may divide the route into sections and predict the expected destination of the initial section. A greedy algorithm may be used by taking the best path segment to predict intermediate points on the way to the destination.

[0061] If it is determined at 222 that a detour has been made and the vehicle is no longer traveling to the expected destination via the expected route (as selected in step 216), the routine may also proceed to step 232, where the final destination or intermediate point may be predicted based on information available in the database. At 234, based on the predicted destination (or upcoming intermediate point), the controller may use stochastic dynamic programming to update the selected one or more routes for reaching the predicted destination. The selection process may follow the algorithm shown in step 212 using Equation 1. Once the total cost function of multiple routes between the vehicle's current position and the predicted destination is estimated, the routes may be ranked based on the cost function, with the highest ranked route corresponding to the lowest total cost function. When PF regeneration is expected during the upcoming travel segment, the route with the highest expected PF regeneration level and the lowest probability of termination of the regeneration event may be the most highly recommended route. Once one or more routes have been updated and ranked, the routine proceeds to step 214, where the selected routes may be displayed to the driver in the order of their ranking. At 216, if the driver does not receive any recommended routes, then at 236, the controller may predict a route based on driver preferences (corresponding to the driver's psychological state), current traffic, and weather conditions at the current location and at the destination. As an example, the driver may prefer to take a certain route during sunny days during weekday mornings. In another example, intermediate road segments may be predicted based on driver history as retrieved from a database. The controller may then schedule a PF regeneration event based on the predicted road segments.

[0062] In this manner, in response to the driver not indicating an end point of the drive cycle, a current location of the vehicle may be determined, a driving history of the driver may be retrieved, an end point may be predicted based on the driving history, a selection of one or more upcoming road segments may be dynamically updated relative to the predicted end point based on the current location of the vehicle, the road segments may be ranked based on each of particulate filter regeneration efficiency, fuel efficiency, and travel time, and the one or more road segments to the predicted end point displayed in the order of their ranking may be displayed to the driver.

[0063] Figure 3 An example method 300 is shown for estimating a driver's psychological state and the impact of the driver's psychological state on route selection for a trip in real time. The method 300 may be part of the method 200 and may be performed at step 212 of the method 200. The route selection may be further based on the soot load accumulated on the particulate filter (PF).

[0064] At 302, the controller may be informed of the date of travel, the time of travel including the time of day when the vehicle is traveling, the day of the week on which the vehicle is traveling, etc. The controller may obtain this information from an onboard navigation system (e.g., a GPS device) or from a network cloud via a wireless connection. At 308, the controller may be informed of the characteristics of the starting point including the geographic location, current weather conditions, and traffic conditions. For example, based on information from the vehicle navigation system or the network cloud, the controller may determine the characteristics of the starting point. In one example, the geographic location of the starting point may include the GPS coordinates of the starting point. Weather conditions may include temperature, humidity, wind speed, and precipitation (such as rain, snow, etc.). Traffic conditions may include the speed limit of the road on which the vehicle is being operated, the speed of overall traffic movement, the average distance between vehicles, traffic congestion, etc. Moreover, the controller may be informed of the geographic location of the end point based on the driver's input to the navigation system.

[0065] At 310, a database (such as Figure 1The database 13 in the example retrieves the driver history including the driver's driving characteristics. In one example, the driver can be identified by a specific key used by the driver to operate the vehicle. In another example, the driver can be identified based on the time, day, date, and geographical location of the starting point of the trip. Thus, a specific driver (such as driver 1) can operate the vehicle at a specific time (or time window) of the day during the weekday, while a different driver (such as driver 2) can operate the vehicle during the weekend and during a specific time window. The driver's characteristics may include the frequency of brake use, the average acceleration force used, the average lane change frequency, etc. The driver's characteristics may vary based on the time, day, date, weather, and geographical location of the starting point of the trip. In one example, during sunny days, the driver may drive more actively (such as frequent acceleration, increased lane changes, higher speeds) compared to the driver's driving style during rainy days. Further preferences of the driver may also be retrieved from the database, including frequently driven routes, stops made during driving, etc. In one example, the driver may drive to a specific end point every weekday morning. If the driver leaves the starting point at a certain time, the driver may usually stop on the way to the end point. However, if the driver leaves the starting point later, the driver may drive more aggressively to the same end point without making any stops. Thus, the driver's time limit may be different for different days of the week, such as the driver may have a higher time limit during the weekdays compared to the weekend. In another example, the driver's preference may depend on weather conditions, such as if it is snowing or predicted to snow, the driver may take a different route from the frequently traveled route (when driving from the starting point to the frequently traveled end point) to avoid road sections with slopes. In yet another example, the driver's preference may depend on traffic conditions, if there is traffic congestion at the starting point, the driver may take a different route from the frequently traveled route. In a further example, the driver may select a route based on fuel level, such as if the fuel level in the fuel tank is below a threshold level, the driver may select a shorter route. In yet a further example, route selection may be based on the vehicle's payload, such as based on the number of passengers in the vehicle or whether it is towed by a trailer. The database is updated using information about the driver's characteristics and preferences.

[0066] At 312, based on the retrieved information including driver history (driver characteristics and driver preferences), date and time of travel, and starting point characteristics (weather conditions and traffic conditions), the controller may assign an initial mental state to the driver. The driver's mental state may directly affect the driver's behavior (driving style) during the trip, which may affect the probability of PF regeneration and regeneration termination. Mental states are situational and probabilistic, and each driver may have multiple mental states (S DK, K = 1, 2, 3, ..., n), and each state may have a different effect on PF regeneration. In one example, the driver may have three different psychological states, namely, the first state S D0 , the second state S D1 and the third state S D2 The first mental state (S D0 ) may correspond to an optimal state for PF regeneration. When the driver operates in this optimal state of mind, driving characteristics (such as frequency of brake application, average acceleration force used, average lane change frequency, etc.) may promote PF regeneration and may not increase the probability of terminating regeneration. For example, in the optimal state, the driver may operate the vehicle at a steady speed for a longer duration while applying the brakes less frequently, the driver may not accelerate or decelerate for a short period of time, and may not change lanes frequently. In this optimal state of mind, passive regeneration of the PF may be performed to a higher level, thereby enabling a more thorough cleaning of the PF. The second driver psychological state (S D1 ) may correspond to a suboptimal state of PF regeneration, wherein the regeneration level achieved is lower than the regeneration level achieved in the first psychological state. When the driver operates in this suboptimal psychological state, driving characteristics (such as frequent acceleration and deceleration, lane changes, making stops) may reduce the PF regeneration level and / or may increase the probability of prematurely terminating regeneration. The third driver psychological state (S D2 ) may correspond to a most undesirable state for PF regeneration, wherein the regeneration level achieved is lower than the regeneration level achieved in each of the first and second psychological states. When the driver operates in this most undesirable psychological state, driving characteristics (such as frequent braking, driving at a lower speed) may further reduce the PF regeneration level and / or may further increase the probability of premature termination of regeneration. In this most undesirable state, passive regeneration of the PF may be frequently interrupted, and regeneration may not be performed to a desired level.

[0067] Since the driver's psychological state affects PF regeneration, a regeneration influence factor may be associated with each driver's psychological state. At 314, an initial regeneration influence factor corresponding to the initial psychological state may be determined. In one example, the controller may use a lookup table to determine the initial regeneration influence factor corresponding to the initial driver state, where the input is the initial driver psychological state and the output is the regeneration influence factor. Figure 8 An example table 800 is shown of the regeneration input factors corresponding to each mental state. The first row 802 shows the regeneration input factors corresponding to the optimal mental state (S D0 The second row 804 shows the first regeneration influence factor f0 corresponding to the suboptimal psychological state (S D1 The third row 806 shows the second regeneration impact factor f1 corresponding to the most undesirable psychological state (S D2)’s third regeneration impact factor f2.

[0068] At 316, a first set of scaling factors corresponding to the initial regeneration impact factors may be determined. Each regeneration impact factor may have a corresponding set of scaling factors that may be applied to the weights used in the cost function calculation for each route between the start point and the end point. Figure 2 As shown in step 212 of , based on the PF regeneration requirement, one or more routes may be selected from a database for traveling between the current vehicle position and the destination. Dynamic programming may be performed to estimate the total cost associated with each of the one or more selected routes. As an example, the total cost function associated with the route may be estimated by Equation 1. The total cost function associated with a particular route between a starting point and a destination may be the sum of individual cost functions corresponding to each of the expected PF regeneration level from the starting point to the destination, the probability that the PF regeneration process is terminated during the driving cycle, the cost of heating the PF to a temperature at which regeneration may be initiated, the fuel consumption, and the duration of the trip between the starting point and the destination. The weights corresponding to the individual cost functions may be adjusted based on scaling factors corresponding to the regeneration influencing factors. In one example, the scaling factor may be multiplied by the corresponding weight of the cost function to determine the scaled weight.

[0069] Fig. 9 Table 900 of scaling factors corresponding to regeneration influencing factors and cost function weights is shown. A first column 902 of table 900 lists weights associated with individual cost functions for a route between a start point and a destination. A second column 904 lists individual cost functions for the total cost function. A first weight w1 may be associated with an expected PF regeneration level for the route, a second weight w2 may be associated with the cost of heating the PF to a temperature at which regeneration may begin, a third weight w3 may be associated with fuel consumption, a fourth weight w4 may be associated with duration, and a fifth weight w5 may be associated with the probability that the PF regeneration process is terminated during travel in the route.

[0070] The third column 906 in the table shows a first set of scaling factors corresponding to the first regeneration impact factor (f0) for each weight. For the first regeneration impact factor (f0), an equal scaling factor of 0.2 may be assigned to each of the weights w1 to w5. Since the scaling factors for all weights are equal and the sum of the scaling factors remains 1, it can be inferred that the first regeneration impact factor (f0) may not have any significant impact on the individual cost function and the total cost function of any route. In other words, for the first regeneration impact factor (f0), the operator may not force any particular trip criteria to be critical, and therefore the costs associated with all trip parameters may be equally weighted.

[0071] The fourth column 908 in the table shows a second set of scaling factors corresponding to the second regeneration influence factor (f1) for each weight. For the second regeneration influence factor (f1), the scaling factors may be unequally distributed, while the sum of the scaling factors remains 1. The scaling factor assigned to each of w1 (PF regeneration level), w2 (cost of regeneration), and w5 (probability of regeneration termination) may be 0.25, while the scaling factor assigned to each of w3 (fuel consumption) and w4 (trip duration) may be 0.125. By assigning a higher scaling factor to each of w1, w2, and w5, the individual cost associated with the PF regeneration efficiency of the route may be increased relative to the individual cost functions related to other factors such as travel time and fuel use. Based on the increase in the cost function associated with the PF regeneration efficiency of the route, it can be inferred that the second regeneration influence factor (f1) may have an adverse effect on the PF regeneration efficiency of the route if regeneration is attempted.

[0072] The fifth column 910 in the table shows a third set of scaling factors corresponding to the third regeneration influence factor (f2) for each weight. For the third regeneration influence factor (f2), the scaling factors may be unequally distributed, while the sum of the scaling factors remains 1. The scaling factor assigned to w1 (PF regeneration level) may be 0.35, the scaling factor associated with w2 (cost of regeneration) may be 0.15, and the scaling factor associated with w5 (probability of regeneration termination) may be 0.5, while the scaling factor assigned to each of w3 (fuel consumption) and w4 (trip duration) may be 0. By assigning zero scaling factors to trip duration and fuel consumption and increasing the scaling factors of w1, w2, and w5, the separate cost function associated with the PF regeneration efficiency of the route may be further increased, while the separate cost function related to travel time and fuel use may not be considered in the total cost function estimation. The third regeneration influence factor (f2) may significantly affect the PF regeneration efficiency of the route because the probability of regeneration termination is high, and the total cost function of the route is calculated entirely based on the PF regeneration efficiency, thereby attempting to increase the trip cost of regeneration under these conditions.

[0073] At 318, the weights scaled by the first set of scaling factors corresponding to the initial regeneration influence factors may be used to estimate a total cost function associated with each route between the start point and the end point as selected from the database. Estimation of the total cost function based on individual cost functions associated with the expected PF regeneration level for the route, the probability that the PF regeneration process is terminated during the drive cycle, the cost of heating the PF to the regeneration temperature, the fuel consumption, and the trip duration may be performed using Equation 1. A plurality of routes between the start point and the end point may be selected from the database, and a total cost function may be estimated for each of the plurality of routes. Details of the total cost function estimation are described in Figure 2 The description is made in step 212 of FIG.

[0074] Once the total cost function of multiple available routes between the starting point and the end point is estimated, the routes can be ranked based on the total cost function, with the highest ranked route corresponding to the lowest total cost function. In one example, when PF regeneration is expected during the upcoming driving cycle (such as when the PF soot level is above a threshold), the route with the highest expected PF regeneration level and the lowest probability of termination of the regeneration event can be ranked highest. The highest ranked (recommended) route can be a route that can reach the end point without any significant delay while providing the highest degree of regeneration and a certain degree of fuel economy. Subsequent routes can provide a relatively lower degree of regeneration while still providing a certain degree of fuel economy, etc. Once one or more routes have been selected from the database and ranked, the selected routes can be displayed to the driver in the order of their ranking. The screen and user interface of the on-board navigation system can be used to display the selected route to the operator.

[0075] Simply go to Figure 5A , which shows a screenshot 500 of an example in-vehicle navigation system displaying ranked route options. In this example, PF regeneration is expected during the current driving cycle, and therefore the route with the highest expected PF regeneration level and the lowest probability of regeneration event termination can be ranked first. Further, in this example, the driver psychological state can be the best first state (S D0 ), and thus may have no significant (adverse) influence on the driver's psychological state in the route ranking. Four routes have been selected from the database and ranked in order of their expected PF regeneration levels in rows 502, 504, 506, and 508, respectively. In each column, a first box 501 indicates a suggested route between a start point and a destination, a second box 503 shows a PF regeneration percentage that can be achieved during driving, and a third box 505 shows the time required to reach the destination from the start point. The driver may select one of the four routes via a fourth box included in each row. The driver may select the fourth box of any one of the four routes via a user interface (such as a touch function on a screen).

[0076] The first route shown in row 502 corresponds to the highest expected PF regeneration level, but the longest trip duration. The fourth route shown in row 508 corresponds to the lowest expected PF regeneration level, but the shortest trip duration. Each of the second route shown in row 504 and the third route shown in row 506 corresponds to an intermediate level of expected PF regeneration level and trip duration. In this example, the driver selects the first route (row 502) corresponding to the highest expected PF regeneration. Based on the selection made by the driver, it can be inferred that the driver has no time constraints during this driving cycle and PF regeneration can be scheduled during the driving cycle. If the driver starts on the selected route but does not follow the selected route and takes a detour (as discussed below), the controller can assume that the current driver preference selects a route that can achieve a higher degree of PF regeneration, while predicting the endpoint (based on driver history) and ranking the routes.

[0077] Return to Figure 3 , during the driving cycle, in order to determine the current state of mind of the driver, at 320, the real-time interaction between the driver and the traffic can be determined. Based on the interaction between the driver and the traffic and further based on environmental factors (such as weather) and behavioral factors (such as reactions to certain situations), the driver's state of mind can change from one state of mind to another. The controller can determine the number and frequency of stops (start-stop frequency) and the duration of each stop. In one example, the driver can travel through a busy street and frequent traffic stops, where the vehicle stops for multiple shorter durations. In another example, the driver can reduce the number of stops, but the duration of the stops is longer. As the number of stops increases, the probability of terminating regeneration increases. The frequency and force of each of the accelerator pedal and the brake pedal applied by the driver can also be determined. In one example, the driver's feet can be guided and can frequently accelerate and brake. In another example, the driver can maintain a steady vehicle speed for a period of time without hard acceleration and deceleration. Moreover, the overall traffic speed (the average speed of other vehicles on the road) and the distance between two consecutive vehicles traveling on the lane can be determined. Thus, during lower traffic speeds, if the distance between successive vehicles is smaller, the probability that the driver needs to apply the brakes more frequently is higher. Conversely, during higher traffic speeds, and when the distance between successive vehicles is larger, the vehicle can travel at a steady speed for a longer duration.

[0078] At 322, the controller may determine the driver's mental state (eg, from an initial state S to a state S) based on the learned interaction between the driver and traffic and further based on environmental factors and behavioral factors. D0 To state S DK). In one example, the driver's mental state may not change during the entire driving cycle, while in another example, the driver's mental state may frequently change from one state to another. A heterogeneous transition model may be used to determine the probability of transitioning from one mental state to another. In one example, a simplified homogeneous transition matrix (T) may be used to predict the possible transitions from one mental state to another.

[0079] Figure 7 An example of a homogeneous transition matrix (T) is shown in 700. In a homogeneous transition matrix (T), finite probabilities are assigned to each transition from one state to another. The probabilities of mental state transitions when operating on the same route may be based on the driver's history (such as retrieved from a database). The finite probabilities in the homogeneous transition matrix may not change based on driver behavior (interaction between the driver and traffic) or conditions (such as weather in the current driving cycle).

[0080] As can be seen from the matrix 700, the first row 702 represents the current mental state S D0 Transition to state S D1 and S D2 If the current state is assigned S D0 , the driver will maintain S during the entire driving cycle D0 The probability of being in state S is 70%, and the driver can switch to state S at any point during the driving cycle. D1 The probability of S is 20%, and at any point during the driving cycle the driver can go directly from state S D0 Transition to state S D2 The second row 704 indicates that the probability of D1 Transition to state S D0 and S D2 If the current state is assigned S D1 , the driver will maintain S during the entire driving cycle D1 The probability of being in state S is 50%, and the driver can switch to state S at any point during the driving cycle. D0 The probability of S is 25%, and the driver can transition to state S at any point during the driving cycle. D2 The third row 706 indicates that the probability of D2 Transition to state S D0 and S D1 If the current state is assigned S D2 , the driver will maintain S during the entire driving cycle D2The probability of being in state S is 50%, and the driver can directly switch to state S at any point during the driving cycle. D0 The probability of S is 25%, and the driver can transition to state S at any point during the driving cycle. D1 The probability is 25%.

[0081] The transition from one driver mental state to another can be estimated based on the dynamic equation. For example, if the driver is in the initial mental state S D0 , then equation 2 can be used to estimate the state S Dk conversion.

[0082] S Dk (t+n)=S Dk *T n (2)

[0083] Where S Dk is the driver's mental state, t is the time, n is the number of possible driver mental states, and T is the homogeneous transition matrix.

[0084] In another example, a heterogeneous transition matrix (Tn) can be used to predict possible transitions from one mental state to another. The heterogeneous matrix can take into account the real-time interaction between the driver and traffic and other evolving conditions (such as weather) while determining the state transition from the initial driver mental state to the updated driver mental state. The heterogeneous transition model can be experimentally identified and validated via tools including machine learning and exploratory data analysis. Such tools can be used to determine the probability of transitioning from one state to another. The transition probabilities of the heterogeneous transition matrix (Tn) may not be fixed numbers and may change in real time. In one example, as shown in Equation 3, from the initial state (S Dj ) to the updated state (S Dk ) can be a function of driver interactions with traffic, weather conditions, driver trip constraints, and time of day.

[0085] P(S Dk |S Dj )=f(driver interactions with traffic, weather, tripconstraints, time of day) (3)

[0086] Figure 6A state machine diagram 600 is shown for the transition of a driver's mental state from one state to another. In the diagram 600, three possible driver mental states S are shown. D0 , S D1 and S D2 In one example, if the initial driver mental state is determined (based on driver history) to be the first state S D0 , the driver can continue to operate in the first psychological state throughout the driving cycle. D0 is optimal for obtaining increased PF regeneration efficiency. Thus, in the first state, the driver can maintain a steady vehicle speed for a period of time without hard acceleration and deceleration. If there are no significant changes in weather conditions and / or traffic conditions (which can impose constraints and increase the duration of the trip), the driver can continue to operate in the first state. The probability that the driver will remain in the first psychological state is given by P(S D0 |S D0 ) is given.

[0087] In response to the interaction between the driver and the traffic and / or the change of weather, the psychological state can change from the first state S to the D0 Transition to the second state S D1 The second state S D1 may be a suboptimal psychological state and may have an adverse effect on PF regeneration efficiency. In one example, due to changes in weather, such as if it begins to rain heavily during driving, the interaction between the driver and traffic may change. As a result, the driver may accelerate for a short period of time and then frequently apply the brakes to control speed. The frequent application of the brakes may be detrimental to PF regeneration efficiency. In another example, an expected traffic jam may cause the driver to stop for longer than expected, and after passing the traffic jam, the driver may drive aggressively to reach the end point without further delay. The probability of transitioning from the first state to the second state is given by P(S D1 |S D0 ). Once in the second state, the driver may continue to operate in the second state for the remainder of the driving cycle. The probability that the driver will remain in the second psychological state is given by P(S D1 |S D1 After operating in the second state for a period of time, the driver may switch to the first state S in response to the interaction between the driver and the traffic and / or the change of the weather. D0 In one example, due to changes in traffic conditions (such as fewer cars on the road), the driver may begin driving less aggressively and may change the psychological state accordingly.

[0088] The driver's mental state may also change from the second state S to the second state S in response to the interaction between the driver and the traffic and / or the change of weather conditions.D1 Transition to the third state S D2 The third state S D2 can be the least desirable psychological state and can adversely affect PF regeneration efficiency. In one example, due to further changes in weather (such as if snow begins to fall during driving), the driver may make frequent stops without significant acceleration. In another example, a trigger such as a phone call may cause the driver to start driving aggressively with increased braking frequency. The probability of transitioning from the second state to the third state is given by P(S D2 |S D1 ). Once in the third state, the driver may continue to operate in the third state for the remainder of the driving cycle. The probability that the driver will remain in the third psychological state is given by P(S D2 |S D2 After operating in the third state for a period of time, the driver may switch to the second state S in response to the interaction between the driver and the traffic and / or the change of the weather. D1 In one example, weather conditions that affect the interaction between the driver and traffic may change, and the driver may make fewer stops in the driving cycle. The probability of transitioning from the third state to the second state is given by P(S D1 |S D2 ). The psychological state may also be directly transitioned from the third state to the first state to facilitate improved PF regeneration. In one example, road congestion may be relieved, allowing the driver to optimally accelerate without frequent braking during the remainder of the driving cycle. The probability of transitioning from the third state to the first state is given by P(S D0 |S D2 ). Thus, an unexpected change in traffic conditions (such as a road closure) may result in a transition from the first state to the third state. Due to the unexpected change in traffic, the driver may take an alternative route and the driving motivation may also increase. The state change from the first state to the third state may result in the termination of the ongoing PF regeneration. The probability of transitioning from the first state to the third state is given by P(S D2 |S D1 ) is given. In this way, during a driving cycle, the interaction between the driver and the traffic and the environmental conditions can lead to changes in the driver's psychological state.

[0089] Based on the learned interaction between the driver and the traffic, during the driving cycle, the controller can determine an updated driver mental state. The controller can use a non-homogeneous transition model to determine an updated (current) driver mental state. Thus, during the driving cycle, there can be multiple changes in the driver mental state (there can be additional mental states, which are not depicted here), and the controller can continue to update the current driver mental state as described above based on the changes in operating conditions.

[0090] Returning to 324, the controller may determine an updated regeneration influence factor corresponding to the updated driver psychological state. In one example, the controller may determine an updated regeneration influence factor corresponding to the updated driver psychological state using a lookup table, wherein the input is the updated driver psychological state and the output is the updated regeneration influence factor. A second set of scaling factors may be associated with the updated regeneration influence factor. The total cost function of each route ranked and displayed before the driver psychological state change may be re-estimated based on the weight change caused by the scaling of the second set of scaling factors. Moreover, in response to the updated driver psychological state, new routes may be retrieved from the database and ranked together with previously displayed routes. Alternatively, previously displayed routes may be re-ranked and displayed in a different order. The weights scaled by the second set of scaling factors corresponding to the updated regeneration influence factor may be used to estimate the total cost function associated with each route. Estimation of the total cost function based on individual cost functions associated with the expected PF regeneration level of the route, the probability of the PF regeneration process being terminated during the driving cycle, the cost of heating the PF to the regeneration temperature, fuel consumption, and the trip duration may be performed using Equation 1. Details of the total cost function estimation are described in Figure 2 The description is made in step 212 of FIG.

[0091] Once the total cost function of the plurality of available routes between the starting point and the end point is estimated, the routes may be re-ranked based on the total cost function, with the highest ranked route corresponding to the lowest total cost function. In one example, the ranking of the routes may be changed after the driver's mental state is updated. In another example, the ranking of the routes may not be changed after the driver's mental state is updated. At 325, the updated route ranking estimated based on the updated total cost function may be displayed to the driver.

[0092] Figure 5B Screen shot 550 of an example in-vehicle navigation system is shown, where updated route options are displayed after the driver's mental state changes. At the beginning of a driving cycle, based on the initial driver mental state, such as Figure 5A As shown, the selected routes between the starting point and the end point are ranked and displayed. After selecting the first route corresponding to the highest degree of achievable PF regeneration (from the displayed routes), the driver may proceed along the selected route. However, after operating for a period of time in the selected first route, the previously highest ranked route may no longer remain the route corresponding to the highest degree of achievable PF regeneration due to changes in traffic and / or environmental conditions that may change the driver's psychological state and based on the updated driver's psychological state. In one example, the change in the driver's psychological state may be associated with the driver starting to drive more aggressively due to the presence of more traffic. Therefore, the updated route may include an alternative route with fewer vehicles that is more suitable for steady-state operation and achieving the desired level of PF regeneration.

[0093] As seen in the screenshot 550, in each row, the first box 501 indicates a suggested route between a start point and a destination, the second box 503 shows the PF regeneration percentage that can be achieved during driving, and the third box 505 shows the time required to reach the destination from the start point. The driver can select one of the four routes via the fourth box included in each row.

[0094] Thus, a previously displayed route may be displayed again, but the ranking of the route may change. Figure 5A The second ranked route is now available in Figure 5B is shown as the highest ranked route in Figure 5A The routes shown as the highest ranked routes in Figure 5B The route is shown as the second-ranked route in Figure 5A A route that was displayed as the third ranked route in may no longer be displayed as a route option. Figure 5A The route shown as the fourth ranked route in Figure 5B In the example, the driver may select a first route that corresponds to the highest degree of PF regeneration that can be achieved, while displaying a third ranked route in the list. At the same time, a new route between the current geographic location and the destination may be retrieved from the database and included as a new (fourth ranked) option. In this example, the driver may select a first route that corresponds to the highest degree of PF regeneration that can be achieved. Since the first route in the updated list is different from the previously selected route, updated navigation instructions may be provided to divert the driver from the current route. The driver may continue to drive along the newly selected route to the destination. In this way, the ranking of routes may be updated in real time based on the current driver psychological state, and the updated ranking may be displayed to the driver. Similar updates in the recommended route list may occur multiple times during the driving cycle as the driver psychological state changes based on real-time conditions.

[0095] In this way, an updated driver psychological state and corresponding regeneration factor can be selected from a database by applying a heterogeneous transition model based on the probability of transitioning from a first driver psychological state (and a corresponding first regeneration factor) to an updated driver psychological state (and a corresponding updated second regeneration factor), wherein the probability is based on the real-time interaction between the driver and traffic.

[0096] Return to Figure 3At 326, the routine includes determining whether there is a key-off event indicating that vehicle operation has ceased. If a key-off event is not indicated, then at 328, the controller may continue to learn the current driver mental state in real time, and based on the updated driver mental state, the route suggestions may be updated in real time. The updates may include updating the ranking of previously displayed routes or displaying new routes (retrieved from a database) to the driver to increase the PF regeneration level achievable during a given drive cycle.

[0097] At 330 , the database may be updated with information learned during the current drive cycle, including interactions between the driver and traffic, the route displayed, the road segments traveled, different driver mental states, actual particulate filter regenerations obtained, etc. Further, the circumstances leading to each change in the driver's mental state and the probability of transitioning (from one mental state to another) may be included in the update. Figure 4 The details of updating the database with information learned during the current drive cycle are discussed in detail in.

[0098] In this manner, at the beginning of a drive cycle, a first particulate filter regeneration factor may be selected based on the operator's past driving history, and one or more driving routes selected from a database may be displayed to the operator, the one or more routes ranked based on each of the drive cycle start and end points, respective regeneration completion efficiencies, and the first regeneration factor. Navigation instructions for an operator-selected route from the displayed one or more driving routes may be displayed to the operator. During the drive cycle, a second particulate filter regeneration factor may be selected based on real-time interaction between the driver and traffic while traveling along the operator-selected route, and the route and navigation instructions may be updated accordingly.

[0099] Figure 4 An example method 400 for updating a database of frequently traveled routes is shown. At 402, the routine may include determining whether a vehicle key-on event exists. For example, it may be determined whether a vehicle driver has expressed an intent to begin vehicle operation. Thus, by confirming a vehicle key-on event, an upcoming vehicle drive cycle is indicated. If a vehicle key-on event is not detected and therefore an upcoming vehicle drive cycle is not confirmed, the method may end and the database may not be updated.

[0100] If a vehicle key-on event is confirmed, then at 404, the controller may learn the origin characteristics including the time and geographic location of the key-on event. For example, based on information from a vehicle navigation system (e.g., a GPS device), the controller may determine the origin characteristics. In this way, the controller may determine the amount of time the vehicle has been stopped at a certain location (e.g., a point of origin) before starting the trip. Furthermore, the duration that has passed since the immediately preceding key-off event may be determined. That is, the duration of the vehicle's stop at the current location may be estimated.

[0101] At 406, the controller may be informed of details about the vehicle's route including the road segments traveled. This may include terrain information (such as road grade, inclination, terrain) for each road segment in the actual route of travel. The details may be obtained based on information from the vehicle navigation system and / or from an external server via wireless communication. At 408, the controller may be informed of details about intermediate stops on the way to the destination. The stop may be due to a traffic signal, traffic congestion, or the stop may be intentionally made by the driver. The geographic location and duration of each stop may also be known. At 410, route traffic information including the number of traffic stop sites may be obtained via the navigation system. The controller may also determine the speed limit for each road segment and the actual speed of the vehicle travel.

[0102] At 412, the controller may learn the driving time, which includes the time of day when the vehicle is driving, the driving date, the day of the week when the vehicle is driving, etc. At 414, the controller may learn the data from multiple engine sensors (such as Figure 1 The controller can also obtain engine operating conditions of the sensor 16 in the vehicle, such as engine speed, engine load, engine temperature, etc. Moreover, the controller can be aware of the soot level accumulated on the PF during vehicle driving.

[0103] At 416, the controller may learn the PF regeneration level achieved during the current driving period. In one example, the PF regeneration level may be determined based on the change in PF soot level (the change between the soot level measured at the beginning of the drive and at the end of each road segment) as estimated via the exhaust pressure sensor. In another example, the PF regeneration level may be determined based on the duration of the PF regeneration. Furthermore, it may be known whether the PF regeneration process has been prematurely terminated due to adverse operating conditions during the driving cycle. The reason for the termination of the PF regeneration and the engine operating conditions and road conditions where the PF regeneration event was terminated may also be recorded.

[0104] At 418, the controller may learn the driver's driving characteristics. These may include, for example, the frequency of brake and accelerator pedal applications, the frequency of brake and accelerator pedal releases, the frequency of transmission shifts, the duration of operation of electric mode versus engine mode, etc. The controller may also learn the different psychological states of the driver during the driving cycle and the duration of each psychological state based on the driving characteristics. Further, the conditions (traffic, environment, behavior, etc.) that trigger the driver's psychological state changes may also be learned. Further, in each driver psychological state, the controller may learn the probability of switching from the current psychological state to another psychological state based on a function of the current psychological state and the conditions that trigger the driver's psychological state change.

[0105] At 420, the routine includes determining whether there is a key-off event indicating that vehicle operation has stopped. If a key-off event is not indicated, then at 422, the controller may continue to collect data about various aspects of vehicle operation while the vehicle is traveling. If the vehicle is confirmed to be stopped, then at 424, the method includes learning the endpoint characteristics including the geographic location of the endpoint.

[0106] At 425, the controller may learn the fuel consumed during the trip from the starting point to the end point. As an example, the fuel consumption may be estimated based on the initial and final fuel levels in the fuel tank. In another example, the fuel consumption may be estimated based on the engine operating conditions. Also, the duration of the trip and the time taken to reach the end point from the starting point may be learned.

[0107] At 426, the database may be updated with all of the aforementioned data (as collected in steps 404 to 424) including information about the current driving route, engine operating conditions, PF regeneration information, and driver driving characteristics. At 428, the current route for traveling between the starting point and the end point may be compared with one or more routes (between the starting point and the end point) previously stored in the database. The cost function estimated according to Equation 1 may be estimated for the current route and compared with the cost function of each route previously stored in the database. At 430, based on the cost function comparison, different routes may be ranked according to the highest fuel efficiency, the shortest driving duration, the highest level of PF regeneration achieved, and any other operator-selected cost function. Furthermore, a Markovechain based route ranking algorithm may be used. As an example, a route that may be a target level of PF regeneration may not be the most fuel-efficient route.

[0108] In this way, while the vehicle is keyed off, the database may be updated with route information, origin characteristics, destination characteristics, driver behavior, PF regeneration levels achieved, engine operating conditions, date and time information, and traffic information, among others.

[0109] Fig.10 A prophetic example of prediction and dynamic selection of a proposed route suitable for optimal particulate filter regeneration is shown. A route can be selected from an existing route database based on the engine's PF regeneration requirements, and the current driver state of mind and the selected route can be displayed to the driver. The horizontal line (x-axis) represents time, and the vertical markers t1 to t6 identify important times in the operation of the vehicle system.

[0110] At time t1, the driver starts the vehicle (such as a key-on event) and the current geographic location of the vehicle as determined via the onboard navigation system is represented by A. The driver initially indicates the geographic location of the end point as represented by B via input to the navigation system. The controller retrieves the (driver's) driving history from the database including the driver's characteristics and preferences (such as the frequency of brake use, the average acceleration force used, the average lane change frequency, etc.). The controller can determine the current driving date and time, the starting point characteristics (weather conditions and traffic conditions), and based on the retrieved data (the above information), the controller assigns an initial psychological state to the driver. The controller then selects one or more routes from the database using dynamic programming based on PF soot level, fuel consumption, driving time, and traffic conditions. Line 1020 shows the change in the soot level deposited on the PF. Dashed line 1021 represents a threshold value, above which PF regeneration is expected. Based on the current PF soot level (at time t1), the controller infers that the PF soot level may increase to a threshold soot level during the journey from point A to point B, and PF regeneration needs to be performed. The controller then ranks the one or more selected routes based on a weighted function of each of the particulate filter regeneration efficiency, the probability of completing a PF regeneration event, the fuel efficiency, and the travel time. In ranking the one or more selected routes, the weighted function is adjusted based on a regeneration efficiency factor corresponding to the initial psychological state of the driver. In this example, the initial driver psychological state is the optimal psychological state (S D0), where the particulate filter regeneration efficiency is the highest and also increases the probability of completing the PF regeneration event. The particulate filter regeneration efficiency is the amount of particulate filter regeneration achieved in a driving cycle via a combination of passive regeneration and active regeneration. In passive regeneration, soot is burned due to higher exhaust temperatures during higher load conditions, and during active regeneration, the temperature of the PF may be increased by flowing current therethrough. In response to an upcoming PF regeneration request, during ranking of one or more routes, a higher weight is assigned to each of the particulate filter regeneration efficiency and the probability of completing the PF regeneration event, and a lower weight is assigned to fuel efficiency and driving time. In this manner, the controller displays the routes with the highest particulate filter regeneration efficiency and the probability of completing the PF regeneration event at the highest ranking, and displays the routes with the lowest particulate filter regeneration efficiency and the probability of completing the PF regeneration event at the lowest ranking. The list is then displayed to the driver so that they can select a route based on the ranking.

[0111] At time t1, the driver selects route 402, which is the highest ranked route in the list of suggested routes between starting point A and end point B provided to the driver. However, at time t2, the driver is observed to deviate from the initially selected route and take a new route. In response to the change in route, the controller predicts the upcoming road segment between the current location and end point B based on the driver's driving history and current traffic conditions retrieved from the database. Thus, while predicting the upcoming road segment using stochastic dynamic programming, the routes that the driver frequently travels during the time of the week and / or during the day are taken into account. Based on the driver history and initial psychological state, the controller predicts that the driver may take route 404 to the first intermediate stop site C and from there take route 406 to end point B. The controller then schedules PF regeneration based on the predicted route.

[0112] The driver follows the predicted route and continues to drive via route 404, however, it is observed that after the first intermediate station C, at time t3, the driver deviates from route 406. Also, at time t3, based on the interaction between the driver and the traffic between times t1 and t3, the controller updates the driver's mental state from the best mental state to a second best mental state (S D1). As a result of the transition to the second optimal state of mind, it is known that the driver may be operating the vehicle more aggressively, and driving characteristics (such as increased frequency of brake application) may reduce the efficiency of particulate filter regeneration and may reduce the probability of completing PF regeneration in a given driving cycle. In response to the change in route, based on the driver history and updated state of mind, the controller once again predicts that the driver may take route 408 to the second intermediate stop site D and from there take route 410 to the third intermediate stop site E and then take route 412 to the end point B. However, it is observed that the driver does not take the predicted segments 408, 410, and 412, but instead continues to the end point via new routes 414 and 416. The driver stops at intermediate point F on the way to the end point B and finally reaches the end point at time t4.

[0113] It is observed that the soot level in the PF reaches a threshold level 421 at time t3, and passive regeneration of the soot is performed. However, because the driver does not follow the recommended route that is optimal for PF regeneration and due to the driver's suboptimal psychological state, the PF regeneration level achieved after reaching the end point B at time t4 is lower than the PF regeneration level that would be achieved if the driver took the route 1002 from the starting point A to the end point B. Moreover, since the driver deviates from the recommended route and the predicted route, the arrangement of passive regeneration of the PF (between time t3 and t4) may not be possible. Dashed line 1022 shows the change in the soot level in the PF that may occur if route 1002 is taken and assuming that the driver's psychological state does not change from the first to the second. As observed from graphs 1020 and 1022, if the recommended route 1002 is taken, the amount of soot burned between time t3 and t4 will be higher relative to the amount of soot burned during travel via routes 1014 and 1016.

[0114] At time t4, upon reaching the end point B (after the vehicle key is turned off), the database may be updated with route information, starting point characteristics (such as geographic location), end point characteristics, location of each stop taken, driver interaction with traffic (such as frequency of gear changes, pedal application and release frequency, brake application frequency, etc.), PF regeneration level achieved, engine operating conditions (such as engine speed, engine load, engine temperature, etc.), date and time information, driver psychological state, and traffic information. Furthermore, road grade, terrain, inclination information for each segment of the route may be included in the database. The data stored in the database may be utilized for future route selection and / or prediction.

[0115] In this way, PF regeneration can be efficiently scheduled and performed by dynamically selecting a driving route from a plurality of routes available in a database based on the particulate filter regeneration requirements during a driving cycle. By considering the current driver's mental state, which represents the real-time driver's driving behavior, the likelihood that the driver will select a recommended route (where the PF regeneration efficiency is high) increases. By estimating the driver's mental state in real time based on the driver's interaction with traffic and environmental conditions, the ranking of navigation routes corresponding to the probability of achieving a desired PF regeneration level can be updated as the driver's behavior changes. By predicting the end point or section of the upcoming route based on the driving history and driving statistics stored in the route database, it is even possible to schedule PF regeneration during a trip where the driver has not yet specified a final end point or when the driver deviates from the selected route. The technical effect of maintaining a database of driver mental states and frequently driven routes with information including possible PF regeneration levels that can be achieved on each route is that at vehicle key-on, an initial mental state can be selected from the database based on the driver's history, and a route can be selected from the database based on the PF soot level and the driver's selected cost function (including the highest fuel efficiency and the lowest travel time in the driving cycle). In this way, during periods of PF soot loading above a threshold, by selecting a favorable route for timely regeneration of the PF, overloading of the PF with soot can be reduced, thereby improving engine performance. By allowing the PF to be regenerated timely, using passive regeneration, the need for active regeneration is reduced, thereby providing additional fuel efficiency benefits.

[0116] An example engine method includes: learning a particulate filter regeneration efficiency after each driving cycle based on one or more characteristics of the driving route and a function of operator behavior on the driving route, updating a database based on the learning, and displaying one or more routes selected from the database to the operator at the beginning of the driving cycle, the selection based on the particulate filter soot load at the beginning of the driving cycle. In any of the foregoing examples, additionally or alternatively, the selection is further based on an operator-indicated end point of the driving cycle relative to the start point of the driving cycle. In any or all of the foregoing examples, additionally or alternatively, the selection is further based on an operator-selected cost function that includes one or more of the highest fuel efficiency and the lowest driving time in the driving cycle. Any or all of the foregoing examples further include, additionally or alternatively, ranking the one or more routes based on a weighted function of each of the particulate filter regeneration efficiency, the probability of completing a particulate filter regeneration event, the fuel efficiency, and the driving time for each of the one or more routes. In any or all of the foregoing examples, additionally or alternatively, the particulate filter regeneration efficiency includes a degree of particulate filter regeneration predicted for the driving cycle, and wherein the one or more characteristics of the driving route include a number of stops, a road grade, and traffic conditions. In any or all of the foregoing examples, additionally or alternatively, when the particulate filter soot load is above a threshold, each of the particulate filter regeneration efficiency and the probability of completing a particulate filter regeneration event is assigned a higher weight. In any or all of the foregoing examples, additionally or alternatively, when the particulate filter soot load is below a threshold, each of the fuel efficiency and the travel time is assigned a higher weight. Any or all of the foregoing examples further include, additionally or alternatively, in response to the operator not selecting a route from one or more routes displayed to the operator, dynamically predicting an upcoming road segment based on the operator's driving history retrieved from a database. In any or all of the foregoing examples, additionally or alternatively, the operator's driving history includes routes previously traveled as a function of one or more of time of day, day of the week, and traffic conditions. Any or all of the foregoing examples further include, additionally or alternatively, in response to the operator selecting a route from one or more routes displayed to the operator, beginning to travel along the route, and then deviating from the selected route, dynamically predicting an upcoming road segment based on the operator's driving history retrieved from a database. Any or all of the foregoing examples further include, additionally or alternatively, scheduling an active particulate filter regeneration event during the drive cycle based on an operator selected route from one or more displayed routes, wherein during the active particulate filter regeneration event, a temperature of the particulate filter is increased by flowing current through the particulate filter.Any or all of the foregoing examples further include, additionally or alternatively, when an operator does not specify an endpoint for the drive cycle, predicting the endpoint based on the operator's driving history as retrieved from the database, and displaying one or more routes selected from the database to the operator based on the predicted endpoint. In any or all of the foregoing examples, additionally or alternatively, the start of the drive cycle includes a vehicle key-on event, and wherein updating the database based on the learning includes updating the database with route information, starting point characteristics, endpoint characteristics, operator behavior, achieved particulate filter regeneration level, engine operating conditions, date and time information, and traffic information at the vehicle key-off event.

[0117] Another example engine method includes: in response to an operator destination selection indicated via a display of a vehicle, estimating an exhaust particulate filter soot load, determining a current location of the vehicle, retrieving one or more routes from the current location to the destination from a database, ranking the one or more routes based on each of a particulate filter regeneration efficiency, a fuel efficiency, and a travel time of each route, and displaying the one or more routes to the selected destination in the order of their ranking to the operator. Any of the foregoing examples further include, additionally or alternatively, in response to a particulate filter soot load above a threshold, ranking the one or more routes by assigning a higher weight to the particulate filter regeneration efficiency and a lower weight to each of the fuel efficiency and the travel time. Any or all of the foregoing examples further include, additionally or alternatively, in response to a particulate filter soot load below a threshold, ranking the one or more routes by assigning a higher weight to each of the fuel efficiency and the travel time and a lower weight to the particulate filter regeneration efficiency. Any or all of the foregoing examples further include, additionally or alternatively, predicting one or more road segments from a current location to a destination based on an operator driving history retrieved from a database in response to an operator not selecting a route from one or more displayed routes or the operator deviating from the selected route, and scheduling passive regeneration of the particulate filter based on the one or more predicted road segments.

[0118] In yet another example, a vehicle method includes: in response to an operator not indicating an end point of a drive cycle, determining a current location of the vehicle, retrieving a driving history of the operator from a database, predicting an end point based on the driving history, dynamically updating a selection of one or more upcoming road segments based on the current location of the vehicle relative to the predicted end point, ranking the one or more upcoming road segments based on each of corresponding particulate filter regeneration efficiency, fuel efficiency, and travel time, and hierarchically displaying the one or more upcoming road segments to the predicted end point to the operator in their ranked order. Any of the foregoing examples further include, additionally or alternatively, in response to an operator not selecting a route from the one or more displayed upcoming road segments, dynamically updating the one or more upcoming road segments based on the driving history, and scheduling active regeneration of the particulate filter based on the updated one or more road segments. Any or all of the foregoing examples further include, additionally or alternatively, upon completion of the drive cycle, updating the database with road segment information including operator behavior, a particulate filter regeneration level achieved, engine operating conditions during the completed drive cycle, date and time information of the travel, and traffic information within the drive cycle.

[0119] In a further example, an engine method includes: at the beginning of a driving cycle, displaying a first driving route in response to each of a particulate filter (PF) load and a past driving history; and during driving along the first driving route, displaying an updated route in response to each of traffic conditions and a comparison of real-time driving history along the first route in the driving cycle relative to past driving history. In any of the foregoing examples, additionally or alternatively, displaying the first driving route includes selecting the first driving route from a database including a plurality of driving routes based on a first inferred driver psychological state, the first inferred driver psychological state being based on past driving history, and wherein displaying the updated route includes selecting the updated route from the database based on the updated driver psychological state. In any or all of the foregoing examples, additionally or alternatively, selecting an updated driver psychological state from a plurality of inferred driver psychological states stored in the database, selecting the updated driver psychological state based on a comparison of real-time driving history along the first route in the driving cycle relative to past driving history, wherein each of the plurality of inferred driver psychological states has an associated PF regeneration factor. In any or all of the foregoing examples, additionally or alternatively, displaying the first route further includes, in response to the operator indicating an end point of the drive cycle, displaying one or more routes retrieved from a plurality of driving routes included in the database, the one or more routes ranked according to a first function of a corresponding PF regeneration efficiency, a probability of completing a PF regeneration event during the drive cycle, and a first PF regeneration factor associated with a first driver psychological state, wherein the corresponding PF regeneration efficiency of each of the one or more routes is determined according to a function of a corresponding PF regeneration degree predicted for the drive cycle and the first PF regeneration factor. In any or all of the foregoing examples, additionally or alternatively, displaying the updated route includes, in response to the comparison, displaying one or more routes ranked according to a second function of an updated PF regeneration efficiency, a probability of completing a PF regeneration event during the drive cycle, and a second PF regeneration factor associated with an updated driver psychological state, wherein the corresponding PF regeneration efficiency of each of the one or more routes is determined according to a function of a corresponding PF regeneration degree predicted for the drive cycle and the second PF regeneration factor.In any or all of the foregoing examples, additionally or alternatively, ranking the one or more routes according to a function of the first PF regeneration factor includes ranking the one or more routes based on an assigned weight to each of a PF regeneration efficiency and a probability of completing a PF regeneration event during a drive cycle, the assigned weight being scaled by a first function based on the first PF regeneration factor, and wherein ranking the one or more routes according to a function of the updated PF regeneration factor includes ranking based on an assigned weight to each of a PF regeneration efficiency and a probability of completing a PF regeneration event during a drive cycle, the assigned weight being scaled by a second set of factors corresponding to a second PF regeneration factor, the second set of factors being different from the first set. In any or all of the foregoing examples, additionally or alternatively, the real-time driving history includes real-time interactions between the driver and traffic including real-time accelerator pedal usage and real-time brake usage during a drive cycle while driving along the first driving route, and wherein the past driving history includes a frequency of brake usage, an average acceleration force used, and an average lane change frequency while driving along the first route in one or more drive cycles prior to the drive cycle. Any or all of the foregoing examples further include, additionally or alternatively, learning a degree of PF regeneration obtained during the driving cycle upon completion of the driving cycle, and then updating the database with the learned degree of PF regeneration obtained in the driving cycle, the first inferred driver mental state, and the updated driver mental state.

[0120] In yet a further example, a method includes: selecting a first particulate filter regeneration factor at the beginning of a drive cycle based on an operator's past driving history; displaying one or more driving routes selected from a database to the operator, the one or more routes ranked based on a drive cycle start and end point, respective regeneration completion efficiencies, and each of the first regeneration factors; displaying navigation instructions to the operator for an operator-selected route from the displayed one or more driving routes; and selecting a second particulate filter regeneration factor during the drive cycle based on real-time interaction between a driver and traffic while traveling along the operator-selected route. In any of the foregoing examples, additionally or alternatively, each of the first regeneration factor and the second regeneration factor is selected from a plurality of regeneration factors stored in a database, each of the plurality of regeneration factors corresponding to a different driver psychological state. In any or all of the foregoing examples, additionally or alternatively, selecting the second regeneration factor includes applying a heterogeneous transition model to select the second regeneration factor from the plurality of regeneration factors based on a probability of transitioning from the first regeneration factor to the second regeneration factor, the probability being based on real-time interaction between the driver and traffic. In any or all of the foregoing examples, additionally or alternatively, the driver's past driving history includes routes traveled by the operator as a function of one or more of the time of day, the day of the week, the start and end of the drive cycle, and driving characteristics, including frequency of brake application, average acceleration force applied, and average lane change frequency. In any or all of the foregoing examples, additionally or alternatively, the real-time interaction between the driver and traffic includes one or more of the frequency of stops during the drive cycle, the frequency of lane changes, accelerator pedal inputs, and brake inputs. In any or all of the foregoing examples, additionally or alternatively, selecting the first regeneration factor is further based on traffic conditions at the start of the drive cycle and environmental conditions at the start of the drive cycle, the environmental conditions including ambient temperature, ambient humidity, and precipitation. In any or all of the foregoing examples, additionally or alternatively, ranking the one or more routes further includes ranking each of the one or more routes based on a weighted function of each of the respective regeneration completion efficiencies, the probability of completing a particulate filter regeneration event, the fuel efficiency, and the time to reach each of the one or more routes, the weighted function being scaled based on the first regeneration factor. Any or all of the foregoing examples further include, additionally or alternatively, in response to selecting a second particulate filter regeneration factor, updating a weighting function scaled based on the second regeneration factor and then updating a ranking of the one or more routes.

[0121] In another further example, a vehicle system includes: a vehicle, a navigation system wirelessly connected to an external network, a display, an engine including an intake system and an exhaust system, the exhaust system including a particulate filter (PF) coupled to an exhaust passage and a pressure sensor coupled to the exhaust passage upstream of the particulate filter, and a controller having computer-readable instructions stored on a non-transitory memory, the instructions for: at the beginning of a drive cycle, displaying a first route based on a PF load and a first driver psychological state, and in response to interactions between the driver and traffic while traveling on the first route, displaying a plurality of updated routes based on a second driver psychological state, wherein the first driver psychological state is selected from a database based on each of the PF load and a driver history and changes from the first driver psychological state to the second driver psychological state based on interactions between the driver and traffic while traveling on the first route. In any of the foregoing examples, additionally or alternatively, the first route is selected based on a first weighted PF regeneration efficiency, the first weighted PF regeneration efficiency being based on a first PF regeneration factor corresponding to the first driver psychological state. In any or all of the foregoing examples, additionally or alternatively, the plurality of updated routes are selected based on a second weighted PF regeneration efficiency, the second weighted PF regeneration efficiency being based on a second PF regeneration factor corresponding to a second driver psychological state, and wherein the display of the plurality of updated routes includes ranking each of the plurality of updated routes based on the second weighted PF regeneration efficiency. In any or all of the foregoing examples, additionally or alternatively, the controller includes further instructions for: learning driver interaction with traffic, the displayed route, the road segment traveled, the driver psychological state, and the obtained particulate filter regeneration during the driving cycle, and updating the database based on the learning after completing the driving cycle.

[0122] In further representations, the vehicle is a hybrid vehicle system. In any of the foregoing examples, additionally or alternatively, an example method for a hybrid vehicle includes: selecting, at the beginning of a drive cycle, a first value indicative of a driver's state of mind based on a driving history, displaying to the driver one or more routes selected from a database, a regeneration factor for each of the one or more routes based on the first value, and selecting, in real time during the drive cycle, a second value indicative of an updated driver's state of mind based on real-time interaction between the driver and traffic.

[0123] Note that the example control and estimation routines included herein can be used with various engine and / or vehicle system configurations. The control methods and routines disclosed herein can be stored in non-temporary memory as executable instructions and can be executed by a control system including a controller combined with various sensors, actuators and other engine hardware. The specific routines described herein can represent one or more of any number of processing strategies, such as event-driven, interrupt-driven, multi-tasking, multi-threading, etc. Therefore, the various actions, operations and / or functions shown can be executed in the order shown, executed in parallel, or omitted in some cases. Similarly, the processing sequence is not required to implement the features and advantages of the example embodiments described herein, but is provided for ease of illustration and description. Depending on the specific strategy used, one or more of the actions, operations and / or functions shown can be repeatedly executed. In addition, the described actions, operations and / or functions can graphically represent the code of the non-temporary memory of the computer-readable storage medium programmed into the engine control system, wherein the described actions are implemented by cooperating with the electronic controller to execute instructions in the system including various engine hardware components.

[0124] It should be appreciated that the configurations and routines disclosed herein are exemplary in nature, and that these specific embodiments are not to be considered limiting, as many variations are possible. For example, the above techniques can be applied to V-6, I-4, I-6, V-12, opposed 4, and other engine types. The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various systems and configurations and other features, functions, and / or properties disclosed herein.

[0125] The accompanying claims specifically point out certain combinations and subcombinations regarded as novel and non-obvious. The claims may refer to "an" element or a "first" element or the equivalent thereof. The claims should be understood to include incorporation of one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and subcombinations of the disclosed features, functions, elements, and / or properties may be claimed by amendment of the present claims or by presentation of new claims in this or a related application. Such claims, whether broader, narrower, the same or different in scope to the original claims, are deemed to be included within the subject matter of the present disclosure.

Claims

1. A method for an engine, comprising: At the beginning of a driving cycle, displaying a first driving route in response to each of a particulate filter (PF) load and a past driving history; as well as During travel along the first driving route, an updated route is displayed in response to each of traffic conditions and a comparison of real-time driving history along the first driving route in the drive cycle relative to the past driving history.

2. The method of claim 1 , wherein displaying the first driving route comprises selecting the first driving route from a database comprising a plurality of driving routes based on a first inferred driver psychological state, the first inferred driver psychological state being based on the past driving history, and wherein displaying the updated route comprises selecting the updated route from the database based on the updated driver psychological state.

3. The method according to claim 2, wherein the updated driver psychological state is selected from a plurality of inferred driver psychological states stored in the database, the updated driver psychological state being selected based on a comparison of the real-time driving history along the first driving route in the driving cycle relative to the past driving history, wherein each of the plurality of inferred driver psychological states has a PF regeneration factor corresponding thereto.

4. The method of claim 3, wherein displaying the first driving route further comprises: In response to an operator indication of an end point of the drive cycle, displaying one or more routes retrieved from the plurality of driving routes included in the database, the one or more routes ranked according to a first function of a corresponding PF regeneration efficiency, a probability of completing a PF regeneration event during the drive cycle, and a first PF regeneration factor associated with the first inferred driver mental state, wherein the corresponding PF regeneration efficiency for each of the one or more routes is determined according to a function of a corresponding PF regeneration degree predicted for the drive cycle and the first PF regeneration factor.

5. The method of claim 4, wherein displaying the updated route comprises: In response to the comparing, the one or more routes are displayed, the one or more routes ranked according to the updated PF regeneration efficiency, the probability of completing the PF regeneration event during the driving cycle, and a second function of a second PF regeneration factor associated with the updated driver psychological state, wherein the corresponding PF regeneration efficiency of each of the one or more routes is determined according to the function of the corresponding PF regeneration degree predicted for the driving cycle and the second PF regeneration factor.

6. The method of claim 5, wherein ranking the one or more routes as a function of the first PF regeneration factor comprises: The one or more routes are ranked based on assigned weights to each of the PF regeneration efficiency and the probability of completing the PF regeneration event during the drive cycle, the assigned weights being scaled by a first function based on the first PF regeneration factor, and wherein ranking the one or more routes according to the function of the updated PF regeneration factor includes ranking based on assigned weights to each of the PF regeneration efficiency and the probability of completing the PF regeneration event during the drive cycle, the assigned weights being scaled by a second set of factors corresponding to the second PF regeneration factor, the second set of factors being different from a first set of factors corresponding to the first PF regeneration factor.

7. The method of claim 1 , wherein the real-time driving history comprises real-time interactions between a driver and traffic, the real-time interactions between the driver and traffic comprising real-time accelerator pedal usage and real-time brake usage during the driving cycle while driving along the first driving route, and wherein the past driving history comprises a frequency of brake usage, an average acceleration force used, and an average lane change frequency while driving along the first driving route in one or more driving cycles prior to the driving cycle.

8. The method according to claim 2, further comprising: A PF regeneration degree obtained during the driving cycle is learned upon completion of the driving cycle, and the database is then updated using the learned PF regeneration degree obtained during the driving cycle, the first inferred driver mental state, and the updated driver mental state.

9. A vehicle system comprising: vehicle; a navigation system that is wirelessly connected to an external network; monitor; an engine including an intake system and an exhaust system, the exhaust system including a particulate filter (PF) coupled to an exhaust passage and a pressure sensor coupled to the exhaust passage upstream of the particulate filter; and A controller having computer readable instructions stored on a non-transitory memory for: At the beginning of a driving cycle, selecting a first particulate filter regeneration factor corresponding to a driver psychological state based on the operator's past driving history; displaying to the operator one or more driving routes selected from a database, the one or more routes ranked based on each of a drive cycle start and end point, a respective regeneration completion efficiency, and the first particulate filter regeneration factor; displaying to the operator navigation instructions for an operator-selected route from the displayed one or more driving routes; as well as During the drive cycle, while traveling along the operator-selected route, a second particulate filter regeneration factor corresponding to another driver psychological state is selected based on real-time interaction between the driver and traffic.

10. The system of claim 9, wherein each of the first particulate filter regeneration factor and the second particulate filter regeneration factor is selected from a plurality of regeneration factors stored in the database, each of the plurality of regeneration factors corresponding to a different driver psychological state.

11. The system of claim 10 , wherein selecting the second particulate filter regeneration factor comprises applying a heterogeneous transition model to select the second particulate filter regeneration factor from the plurality of regeneration factors based on a probability of transitioning from the first particulate filter regeneration factor to the second particulate filter regeneration factor, the probability being based on real-time interaction between the driver and traffic.

12. The system of claim 9, wherein the past driving history of the driver comprises: the route traveled by the operator as a function of one or more of time of day, day of week, start and end points of the drive cycle, and driving characteristics including frequency of brake application, average acceleration force applied, and average lane change frequency, and wherein the real-time interaction between the driver and traffic includes one or more of: frequency of stops, frequency of lane changes, accelerator pedal inputs, and brake inputs during the drive cycle.

13. The system of claim 9, wherein selecting the first particulate filter regeneration factor is further based on traffic conditions at the start of the drive cycle and environmental conditions at the start of the drive cycle, the environmental conditions including ambient temperature, ambient humidity, and precipitation.

14. The system of claim 9, wherein ranking the one or more routes further comprises: Each of the one or more routes is ranked based on a weighted function of each of the respective regeneration completion efficiency, probability of completing a particulate filter regeneration event, fuel efficiency, and time to reach an endpoint of each of the one or more routes, the weighted function being scaled based on the first particulate filter regeneration factor.

15. The system of claim 14, wherein the controller includes further instructions for: in response to selecting the second particulate filter regeneration factor, updating the weighting function scaled based on the second particulate filter regeneration factor and then updating the ranking of the one or more routes.

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