System and method for particulate filter regeneration

Abstract

This application relates to systems and methods for particulate filter regeneration. A method and system are provided for maintaining a database with details of frequently driven routes and selecting vehicle routes from the database based on particulate filter regeneration requirements. In one example, the method may include selecting one or more routes from the database based on current particulate filter soot levels, fuel efficiency, driving time, operator behavior, etc., and ranking the routes based on the particulate filter regeneration efficiency of each of the one or more routes. When the vehicle key is turned off, the database can be updated using information about the driven routes, including the particulate filter regeneration level achieved during the driving cycle.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to a method and system for selecting a driving route for a vehicle from a database based on particulate filter regeneration requirements. BACKGROUND

[0002] Emission control devices, such as particulate filters (PFs), can reduce the amount of soot emissions from an internal combustion engine by trapping soot particulates. Such devices can be regenerated passively during engine operation to reduce the amount of trapped particulate matter. Regeneration is typically achieved by raising the temperature of the PF to a predetermined level for a period of time, while flowing exhaust gas of a defined composition through the PF so as to burn or oxidise the trapped particulate matter. However, during vehicle operation, conditions can not be available for the PF to be regenerated fully. For example, during urban driving conditions involving frequent idle stops and light load engine operation, frequent premature termination of regeneration can occur. Premature termination can result in the need for active regeneration, causing an increase in regeneration fuel losses.

[0003] Various methods are provided for regenerating a PF during a vehicle drive cycle. In one example, as shown in US20160075333, Sujan et al. disclose a method of calculating a recommended route for a vehicle to drive, taking into account the regeneration requirements of an exhaust aftertreatment device. Factors taken into account during route calculation include load information for the vehicle, traffic information and cost policy. A simulation is performed to select a preferred route which is then provided to the operator of the vehicle.

[0004] However, the inventors herein have recognised potential drawbacks of the above methods. As one example, in the method of Sujan et al., it can be difficult to suggest a driving route which enables full regeneration when the driver does not provide an end point. Also, during conditions when the operator starts on the recommended route but takes a detour, the resulting route can have lower regeneration efficiency than intended. Further, the operator’s driving history, driving characteristics and driving preferences can significantly influence the operator’s decision to accept a route recommended by the vehicle controller. As a result, the PF can remain under-regenerated. SUMMARY

[0005] In one example, the above problems can be addressed by an engine method comprising, after each drive cycle, learning particulate filter regeneration efficiency as a function of one or more characteristics of a driven route and operator behaviour on the driven route, updating a database based on the learning, and at the start of a drive cycle, displaying to an operator one or more routes selected from the database, the selection being based on particulate filter soot load at the start of the drive cycle. In this way, by providing route recommendations to the vehicle operator based on operator driving characteristics and history to enable full exhaust aftertreatment device regeneration, PF regeneration can be better arranged.

[0006] As one example, the vehicle controller can develop a route database for the vehicle operator as a function of frequently used routes along with operator driving characteristics on each route. Each time a trip is completed, the database can be updated with driving information including origin and destination details, route details such as route topography, grade, day of the week and time of day that the trip occurred, vehicle stops that occurred during the route and duration of each stop, traffic information for the route as a function of trip time, engine operating conditions on the route, fuel consumption, duration of the trip, possible or actual PF regeneration levels achieved on the route of the trip, operator driving characteristics such as aggressiveness and pedal actuation frequency, etc. Different routes can be stored in the database and ranked according to one or more parameters such as fuel efficiency, duration of the trip, and PF regeneration levels that can be achieved. In each driving cycle, the controller can receive input from the operator regarding the final destination, such as via a navigation system. In response to the operator input and further based on the current soot level of the PF, one or more routes can be selected from the database and hierarchically displayed to the vehicle operator.

[0007] For example, when the PF soot level is below the threshold and PF regeneration is not expected during the upcoming drive cycle, the displayed selection of routes can be ranked based on the time taken to reach the final destination and / or the fuel cost, and the recommended route can be selected independent of its ability to complete PF regeneration. Thus, the recommended route at the top of the list can be the route that is able to reach the final destination in the shortest amount of time or use the least amount of fuel. As another example, when the PF soot level is above the threshold and PF regeneration is expected during the upcoming drive cycle, the displayed selection of routes can be ranked first based on their ability to complete PF regeneration. The routes can then be further weighted based on their fuel economy. Thus, the recommended route at the top of the list can be the route that is able to reach the final destination while providing the highest degree of regeneration and some degree of fuel economy. Subsequent routes can provide a relatively lower degree of regeneration while also providing some degree of fuel economy, and so on. Navigation instructions can then be provided based on the operator selection. However, if the operator does not provide a final destination, the expected destination can be predicted based on the operator's driving history (e.g., based on the destinations that typically occur frequently on a given day of the week and a given time of day) and route selection can be provided based on the expected destination. The vehicle controller can divide the route into road segments and predict the expected destination of the initial road segment and provide a recommended route that is able to perform PF regeneration on the initial road segment. Then, as the operator begins driving, the expected destination of one or more subsequent road segments and the recommended route for each road segment can be dynamically updated using stochastic dynamic programming. Similarly, when the operator begins on the recommended route and takes a detour, the upcoming road segments can be predicted based on the driving history in the database and one or more routes can be recommended from the database using stochastic dynamic programming.

[0008] In this way, by maintaining a database of frequently traveled routes with information including the actual degree of PF regeneration obtained on each route, one or more routes can be selected from the database during future drive cycles based on PF regeneration requirements. By dynamically selecting and suggesting a travel route that is able to perform PF regeneration while taking into account the operator's driving history and the operator's driving preferences, the likelihood that the operator will follow the recommended route is increased. Thus, a higher degree of PF regeneration can be facilitated without substantially increasing fuel consumption and trip duration. The technical effect of using stochastic dynamic programming to predict the road segments of an upcoming route based on the driving history and driving statistics stored in the route database is that PF regeneration can be scheduled even during a trip when the operator has not specified a final destination or when the operator takes a detour from a selected route. In this way, by regenerating PF in a timely manner, the overload of soot in the PF can be reduced, thereby improving engine performance and PF health.

[0009] It is to be understood that the above general description is intended to be illustrative, and not restrictive, of the scope of the claimed subject matter. The above description is intended to be illustrative, and not restrictive, of the scope of the claimed subject matter. Without further limitation, the scope of the claimed subject matter is defined by the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 An exemplary embodiment of an engine system including a particulate filter is shown.

[0011] Figure 2 A flowchart illustrating an example method that can be implemented for selecting a driving route based on PF regeneration requirements is shown.

[0012] Figure 3 A flowchart illustrating an example method that can be implemented for determining a current mental state of a driver and the influence of the driver mental state on route selection is shown.

[0013] Figure 4 A flowchart illustrating an example method that can be implemented for updating a database of frequently driven routes is shown.

[0014] Figure 5A A first example display of a suggested route based on PF regeneration requirements and an initial driver mental state is shown.

[0015] Figure 5B A second example display of a suggested route based on PF regeneration requirements and an updated driver mental state is shown.

[0016] Figure 6 A state machine graph for mental state transitions of a driver is shown.

[0017] Figure 7 A transition matrix of changes in driver mental state is shown.

[0018] Figure 8 A table of regeneration impact factors corresponding to each mental state of a driver is shown.

[0019] Figure 9 A table of scaling factors for each regeneration impact factor corresponding to weights of a cost function associated with a route is shown.

[0020] Figure 10 An example of the prediction and dynamic selection of a proposed route for PF regeneration is shown. DETAILED DESCRIPTION

[0021] The following description relates to systems and methods for selecting routes from a database containing details of frequently driven routes and driver behavior for optimal particulate filter regeneration. Figure 1 An example engine system including a particulate filter is shown. The engine controller can be configured to execute control routines (such as...) Figure 2 Example routines) select driving routes from the database based on PF regeneration requirements. Executable control routines (such as...) Figure 3 (Example) to estimate the driver's current psychological state and further determine the impact of the current psychological state on the selection of the optimal driving route for PF regeneration. In each of the following driving cycles, the controller may execute routines (such as...) Figure 4 (Example routine) to update the database using the learned information during a driving cycle. Figure 5A and Figure 5B An example of a route suggested based on PF regeneration requirements and the driver's psychological state is shown. Figure 6 As shown in the state machine diagram, the driver's mental state can change between multiple states during the driving cycle, and as... Figure 7 As shown, this change can be estimated based on the transformation matrix. Different driver psychological states can correspond to, for example, Figure 8 The list includes different regeneration influencing factors, and these factors can affect, for example... Figure 9 The weights of the cost function used for route determination are listed below. Figure 10 The diagram shows a predictive example of the prediction and selection of driving routes proposed based on PF regeneration requirements.

[0022] Figure 1 Aspects of a vehicle system 102 having an example engine system 100 including an engine 10 are schematically illustrated. In one example, engine system 100 may be a diesel engine system. In another example, engine system 100 may be a gasoline engine system. In the depicted embodiment, engine 10 is a turbocharged engine coupled to a turbocharger 113, which includes a compressor 114 driven by a turbine 116. Specifically, fresh air is introduced into engine 10 via an air filter 112 along intake passage 42 and flows to compressor 114. The compressor may be any suitable intake-air compressor, such as a motor-driven or drive shaft-driven supercharger compressor. In engine system 10, the compressor is a turbocharger compressor mechanically coupled to turbine 116 via shaft 19, turbine 116 being driven by expanding engine exhaust.

[0023] like Figure 1As shown, compressor 114 is connected to throttle valve 20 via supercharger-air cooler (CAC) 17. Throttle valve 20 is connected to engine intake manifold 22. Compressed air from the compressor flows through supercharger-air cooler 17 and throttle valve to the intake manifold. Figure 1 In the embodiment shown, the pressure of the air intake manifold is sensed by the manifold air pressure (MAP) sensor 124.

[0024] One or more sensors may be coupled to the inlet of compressor 114. For example, temperature sensor 65 may be coupled to the inlet to estimate compressor inlet temperature, and pressure sensor 66 may be coupled to the inlet to estimate compressor inlet pressure. As another example, humidity sensor 67 may be coupled to the inlet to estimate the humidity of the air entering the compressor. Other sensors may include, for example, an air-fuel ratio sensor. In other examples, one or more of the compressor inlet conditions (such as humidity, temperature, pressure, etc.) may be inferred based on engine operating conditions. Additionally, when exhaust gas recirculation (EGR) is enabled, the sensors may estimate the temperature, pressure, humidity, and air-fuel ratio of the air-fuel mixture comprising fresh air, compressed air, and recirculated exhaust gas residue received at the compressor inlet.

[0025] The wastegate actuator 92 can be actuated to open to release at least some of the exhaust pressure from upstream of the turbine to downstream of the turbine via the wastegate 90. By reducing the exhaust pressure upstream of the turbine, the turbine speed can be reduced, which in turn helps to reduce compressor surge.

[0026] Intake manifold 22 is connected to a series of combustion chambers 30 via a series of intake valves (not shown). The combustion chambers are further connected 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 allows effluent from different combustion chambers to be directed to different locations within the engine system.

[0027] 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 or cam-actuated, the opening and closing timing of the exhaust valve and the intake valve can be adjusted as needed for desired combustion and emissions control performance.

[0028] Combustion chamber 30 may be supplied with one or more fuels, such as gasoline, ethanol fuel mixtures, 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.

[0029] like Figure 1 As 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 exhaust valve can then flow through exhaust aftertreatment units 170 and 172. In one example, the first exhaust aftertreatment unit 170 may be an ignition catalyst, and the second exhaust aftertreatment unit 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 can be made of various materials, including cordierite, silicon carbide, and other high-temperature oxide ceramics. Therefore, the PF has a limited capacity for retaining soot. Consequently, the PF may need to be regenerated periodically to reduce soot deposits in the filter so that the flow resistance caused by soot buildup does not degrade engine performance. Passive PF regeneration can be advantageously performed during certain engine operating conditions (such as during periods of high engine load) when the exhaust gas flowing through the filter has a defined composition and is above a threshold temperature, in order to burn or oxidize the captured particulate matter. During passive PF regeneration, soot can be burned in a timely manner due to the higher exhaust temperature and also by the desired amount of oxygen present in the exhaust. Filter regeneration can be accomplished by actively heating the filter to a temperature, for example, 400 to 600 degrees Celsius, by flowing an electric current, at which soot particles will burn at a rate faster than the deposition of new soot particles (as during active PF regeneration). During active regeneration, spark timing can be delayed or fuel enrichment can be performed to increase the exhaust temperature. Thus, active PF regeneration can increase fuel consumption and parasitic energy losses (by powering the filter). In contrast, during passive regeneration, it is not desirable to actively heat the PF using an electric current, spark delay, and / or fuel enrichment. In one example, the PF can be a catalytic particulate filter coated with a precious metal (such as platinum) to reduce the soot combustion temperature and also oxidize hydrocarbons and carbon monoxide into carbon dioxide and water.

[0030] In one example, exhaust aftertreatment device 170 may be configured to capture NOx from the exhaust stream when the exhaust stream is lean and to reduce the captured NOx when the exhaust stream is rich. In other examples, exhaust aftertreatment device 170 may be configured to disproportionate NOx or selectively reduce NOx by means of a reducing agent. In yet another example, exhaust aftertreatment device 170 may be configured to oxidize residual hydrocarbons and / or carbon monoxide in the exhaust stream. Different exhaust aftertreatment catalysts having any of these functions may be arranged separately or together in the coating or elsewhere in the exhaust aftertreatment stage. All or part of the treated exhaust from exhaust aftertreatment devices 170 and 172 may be released into the atmosphere via main exhaust passage 105 after passing through muffler 174.

[0031] 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 a further embodiment, the engine system may include a high-pressure EGR flow path in which exhaust gas is drawn upstream of turbine 116 and recirculated to the engine intake manifold downstream of compressor 114.

[0032] One or more sensors may be coupled to EGR channel 180 to provide details about the composition and condition of the EGR. For example, a temperature sensor may be provided to determine the temperature of the EGR, a pressure sensor to determine the pressure of the EGR, a humidity sensor to determine the humidity or water content of the EGR, and an air-fuel ratio sensor to estimate the air-fuel ratio of the EGR. Alternatively, the EGR condition may be inferred from one or more temperature, pressure, humidity, and air-fuel ratio sensors 65 to 67 coupled to the compressor inlet. In one example, air-fuel ratio sensor 57 is an oxygen sensor.

[0033] Multiple sensors, including an exhaust temperature sensor 128, an exhaust oxygen sensor, and an exhaust pressure sensor 129, can be connected to the main exhaust passage 105. The oxygen sensor can be a linear oxygen sensor or UEGO (universal or wide-range exhaust oxygen), a dual-state oxygen sensor or EGO, HEGO (heated EGO), a NOx sensor, an HC sensor, or a CO sensor.

[0034] Engine system 100 may further include control system 14. Control system 14 is configured to receive information from multiple sensors 16 (various examples of sensors are described herein) and send control signals to multiple actuators 18 (various examples of actuators are described herein). Navigation system 154, such as Global Positioning System (GPS), may be coupled to control system 14 to determine the location of vehicle 102 at key-on and at any other instant. Navigation system may be connected to an external server and / or cloud 160 via wireless communication 150. Navigation system 154 may determine the current location of vehicle 102 and obtain environmental condition data (such as temperature, pressure, etc.) and road information (such as road gradient) from cloud 160. Controller 12 may be coupled to wireless communication device 152 for direct communication between vehicle 102 and cloud 160. Upon completion of a driving cycle, database 13 may be updated using road segment information (including driver behavior, driver mental state, achieved particulate filter regeneration level, engine condition, date and time information, and traffic information). Furthermore, trip details, including start and end points, stops during travel and the duration of each stop, road gradient (terrain), fuel consumption, travel duration, driver behavior, etc., can be stored in database 13 within controller 12. Information about routes and traffic can be obtained from navigation system 154 and external cloud 160 via wireless communication 150. Different routes in the database can be compared and ranked based on fuel efficiency, travel duration, and achievable power factor (PF) regeneration levels. Details about the driver's driving patterns can be retrieved from the controller's memory and used to rank routes. Moreover, the driver's driving patterns can be determined over a certain number of vehicle driving cycles based on one or more of frequent trip time patterns, habit probability patterns, route-based statistical profiles, and environmental attribute profiles. Other statistical profiles, different driver mental states, and transitions from one mental state to another can be obtained and stored in database 13. Engine operating parameters can be estimated via input from one or more sensors 16, and information can be added to database 13. Figure 4 The details of updating database 13 were discussed in detail.

[0035] At the start of a driving cycle (when the vehicle key is turned on), based on the particulate filter (PF) soot level, the demand for PF regeneration during the upcoming driving cycle can be assessed, and in response to the driver providing a destination (such as via input to the in-vehicle navigation system), one or more routes can be selected from database 13 based on the driver's psychological state to promote a higher degree of PF regeneration while optimizing fuel efficiency and travel time. Route selection can be further based on a cost function selected by the driver, which includes the highest fuel efficiency and lowest travel time for the driving cycle. The selected one or more routes can then be ranked according to a weighted function of each of the one or more routes: particulate filter regeneration efficiency, the probability of completing a PF regeneration event, fuel efficiency, and travel time. As an example, if based on the current PF soot level, it is inferred that PF regeneration is desired during the upcoming driving cycle, the highest-ranked route displayed to the operator could be one that provides the highest degree of regeneration while also offering a degree of fuel economy upon reaching the destination. If the driver does not select a route from the one or more routes recommended (displayed) to the driver, the upcoming route can be dynamically predicted based on the driver's driving history retrieved from the database. Furthermore, the driver can choose a route from one or more recommended routes, begin driving along that route, and then deviate from the selected route. During such deviations, the controller can dynamically predict upcoming road segments based on the driver's driving history retrieved from a database (such as preferred routes driven at specific times of day or on specific days of week). One or more routes or road segments can be selected from the database based on predicted destinations, ranked according to their particulate filter regeneration efficiency, probability of PF regeneration event completion, fuel efficiency, and driving time for each of the one or more routes, and displayed to the driver. About Figure 2 The details of route selection based on information stored in database 13 are discussed.

[0036] In this way, in response to the driver's destination selection indicated via the vehicle's display, the particulate filter soot load can be estimated, the vehicle's current location can be determined, and one or more routes from the current location to the destination can be retrieved from a database. The one or more routes can be ranked based on each of the particulate filter regeneration efficiency, fuel efficiency, and travel time for each route; and the one or more routes to the selected destination can be displayed to the driver in the order of their ranking.

[0037] The selection and ranking of one or more routes can be further based on the driver's psychological state. The driver's psychological state can represent the driver's real-time driving behavior, such as a driver being more aggressive in one psychological state and more relaxed during a driving cycle in different psychological states. At the start of a driving cycle, a first driver psychological state can be selected from multiple driver psychological states stored in 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 driven by the operator based on one or more of the following: time of day, day of week, start and end points of the driving cycle, and driving characteristics, including the frequency of brake use, average acceleration applied, and average lane change frequency. The first driver psychological state can 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 can be updated. Updating the ranking of one or more routes involves ranking each of the one or more routes based on a weighted function of each of the following: regeneration completion efficiency, probability of particulate filter regeneration completion, fuel efficiency, and time to reach the end point of each of the one or more routes, the weighting function being scaled based on the first regeneration factor. Once the driver selects a route from one or more ranked routes, navigation instructions for the operator's chosen route can be displayed to the driver.

[0038] As the driver travels along a driver-selected route, real-time interactions between the driver and traffic during the driving cycle are known, including one or more of the following: stop frequency, lane change frequency, accelerator pedal input, and brake input. Based on the known real-time interactions between the driver and traffic and a comparison of these interactions with past driving history during travel along the driver-selected route, the driver's mental state can be updated from a first mental state to a second mental state, also selected from database 13. The updated second driver mental state may correspond to a second particulate filter regeneration factor. In response to the change in driver mental state, the ranking of one or more routes can be updated according to a weighted function of each of the following: regeneration completion efficiency, probability of particulate filter regeneration completion event, fuel efficiency, and time to reach the end of each of the one or more routes, the weighting function being scaled based on the second regeneration factor. For example, in the first ranking, the highest-ranked route displayed to the operator might be the route providing the highest level of PF regeneration; however, once the ranking is updated in response to a change in driver mental state, the previously highest-ranked route may no longer provide the highest level of PF regeneration, and different routes capable of achieving the highest level of PF regeneration may now be ranked first. The updated rankings of one or more routes can then be displayed to the driver for further selection. Upon completion of a driving cycle, the PF regeneration level obtained during the driving cycle can be determined, and the database 13 can be updated using the known PF regeneration level obtained within the driving cycle, the first driver's mental state, and the updated driver's mental state.

[0039] 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 codes corresponding to one or more routines programmed therein. As an example, sensors 16 may include an exhaust oxygen sensor, 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 located upstream of the turbine 116. Other sensors, such as additional pressure, temperature, air-fuel ratio, and component sensors, may be coupled to various locations within the engine system 100. Actuators 81 may include, for example, a throttle valve 20, an EGR valve 62, an exhaust valve 92, and a fuel injector 69. As an example, during key ignition, based on the soot level on the power filter (PF) estimated via the exhaust pressure sensor 129, the controller may select the optimal route for the driving cycle based on information stored in a database and inputs from the navigation system 154 and the network cloud 160. The controller can then display the optimal route to the driver, and if the route is selected, it can monitor and know the level of PF regeneration achieved during the trip.

[0040] In some examples, vehicle 102 may be a hybrid vehicle having multiple torque sources available for one or more wheels 55. In other examples, vehicle 102 may be a conventional vehicle with only an engine or an electric vehicle with only one or more electric motors. In the example shown, vehicle 102 includes an engine 10 and an electric motor 52. The electric motor 52 may be a motor or a motor / generator (M / G). When one or more clutches 56 are engaged, the crankshaft of engine 10 and electric motor 52 are connected to the wheels 55 via a transmission 54. In the depicted example, a first clutch 56 is disposed between crankshaft 140 and electric motor 52, and a second clutch 56 is disposed between electric motor 52 and transmission 54. Controller 12 may send signals to the actuator of each clutch 56 to engage or disengage the clutch to connect or disconnect the crankshaft from electric motor 52 and components connected to the motor, and / or connect or disconnect the electric motor 52 from transmission 54 and components connected to the transmission. Transmission 54 may be a gearbox, planetary gear system, or other type of transmission. The powertrain may be configured in various ways, including parallel, series, or series-parallel hybrid vehicles.

[0041] Motor 52 receives power from traction battery 58 to provide torque to wheel 55. Motor 52 can also be operated as a generator to provide power to recharge battery 58, for example, during braking operations.

[0042] In this way, Figure 1The components enable the vehicle system to include: 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 an exhaust passage upstream of the particulate filter, and a controller having computer-readable instructions stored in a non-transitory memory for: displaying a first route based on PF load and a first driver psychological state at the start of a driving cycle, and displaying multiple updated routes based on a second driver psychological state in response to driver-traffic interactions while driving on the first route, wherein the first driver psychological state is selected from a database based on PF load and each in the driver's history, and the change from the first driver psychological state to the second driver psychological state is based on driver-traffic interactions while driving on the first route.

[0043] Figure 2 An example method 200 for selecting a driving route based on particulate filter (PF) regeneration requirements is shown. This can be based on instructions stored in the controller's memory and in conjunction with sensors from the engine system (such as reference sensors). Figure 1 The 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 can employ the engine actuators of the engine system to regulate engine operation.

[0044] At point 202, the routine may include determining whether a vehicle key unlocking event has been detected. For example, it may be determined that the vehicle driver has expressed an intention to begin vehicle operation. Thus, by confirming the vehicle key unlocking event, an upcoming vehicle driving cycle is indicated. Although referred to herein as a vehicle “key unlocking” 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 the 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., inside 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 an intention to operate the vehicle. Thus, the vehicle driver’s driving mode may only be known while the vehicle is being operated. Therefore, if the vehicle key unlocking event is not confirmed and therefore the upcoming vehicle driving cycle is not confirmed, the method may terminate and PF regeneration may not be performed.

[0045] If the key-on event is confirmed, at point 204, the current vehicle and engine operating conditions can be estimated and / or measured. These may include, for example, engine speed, vehicle speed, engine temperature, engine load, ambient conditions (ambient humidity, temperature, and atmospheric pressure), boost level, exhaust temperature, manifold pressure, manifold airflow, battery state of charge, etc.

[0046] At position 206, pressure can be determined based on an exhaust pressure sensor located upstream of the PF (such as...). Figure 1 The input of pressure sensor 129 in the engine is used to estimate the level of soot deposited in the exhaust gas pressure filter (PF). As the soot level in the PF increases, the exhaust back pressure can increase pumping losses, thereby affecting engine performance and increasing fuel consumption. Therefore, if the soot level increases above a threshold, the PF can be regenerated by burning at least some of the soot deposited thereon. However, passive regeneration of the PF can be adversely affected during engine operating conditions such as idling, low engine load, and low engine temperature. Incomplete or interrupted PF regeneration can adversely affect engine efficiency. Therefore, when starting a new journey, the soot level on the PF can be taken into account to select the driving route so that PF regeneration can be performed in a timely manner during the journey.

[0047] At 208, the routine includes determining whether the driver has specified a destination. The driver can specify a destination to the in-vehicle navigation system via input. If the destination is determined to be known, the routine proceeds to step 210 to retrieve data from a database (such as...). Figure 1 The database 13) retrieves one or more routes between a starting point and a destination. The starting point (such as coordinates, geographical location) can be determined wirelessly from an in-vehicle navigation system or from a cloud network. The database is kept updated using information from frequently traveled vehicle routes. Information including starting and ending points, routes taken, stops during the trip and the duration of each stop, traffic information for each route, date and time of travel, engine status, fuel consumption, travel duration, possible PF regeneration level, and driver characteristics are available in the database. (Reference) Figure 4 A sample method for updating the database during each trip is described in detail. Given the current vehicle location and destination (such as indicated by the driver), multiple possible routes may exist in the database.

[0048] At point 212, one or more routes can be selected from the database for traveling between the current vehicle location and the destination. Dynamic programming can be performed to estimate the cost associated with each route. As an example, the cost function associated with a route can be estimated using Equation 1.

[0049]

[0050] J A-BIt is the total cost function (the sum of individual cost functions) associated with a specific route from origin A to destination B, where E(θ) is the expected power regeneration level of the route from point A to B, and w1 is the weight associated with the expected power regeneration level of the route. It is the cost of heating the PF to a temperature at which regeneration can begin. This refers to fuel consumption associated with the route from A to B, including fuel use due to back pressure-related pumping losses as soot load increases on the PF. A-B It is the duration of a journey from A to B following a specific route, P(A bort |θ<θ * ) is the probability that the PF regeneration process will terminate during travel from point A to point B via that specific route, and w5 is the weight associated with the probability of PF regeneration termination.

[0051] Weights w1 and w5 can be adjusted based on PF soot levels. In one example, when the current PF soot level is above a threshold and PF regeneration is expected during the upcoming driving cycle, each of weights w1 and w5 can be increased, and weights w3 and w4 can be decreased, to increase the cost function of routes with lower expected PF regeneration levels or a higher probability of PF regeneration process termination. However, when PF regeneration is not expected during the upcoming driving cycle, weights w1 and w5 can be decreased, while weights w3 and w4 can be increased, to prioritize routes with shorter driving times and higher fuel efficiency.

[0052] Weights w1 and w5 can be further adjusted based on the driver's current psychological state. The driver's psychological state can influence driving characteristics (such as the frequency of accelerator pedal application and release, shifting frequency, and brake application frequency), which can further affect PF regeneration. The driver's psychological state can correspond to a regeneration influence factor (RIF), and each RIF can further correspond to a scaling factor for each of 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 shifting frequency, and lower brake application frequency), the corresponding RIF can result in equal scaling factors for each of weights w1 to w5, such that the effect of the driver's psychological state does not change the total cost function associated with that route. As an example, an equal scaling factor of 0.2 can be assigned to each of weights w1 to w5. Since the scaling factors for 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 has no adverse effect on the total cost function for any route. In another example, if the driver is in a second psychological state, during which the driver's driving characteristics can adversely affect PF regeneration, the RIF corresponding to the second psychological state can result in unequal scaling factors for each of the weights w1 to w5. To incorporate the adverse effects of the driver's psychological state into the cost function estimation of the route, the corresponding RIF can result in scaling factors for w1 (the weight associated with the expected PF regeneration level of the route) and w5 (the weight associated with the probability of PF regeneration termination) being higher than those assigned to the other weights (w2, w3, and w4). By assigning higher scaling factors to w1 and w5, the individual cost functions associated with each of the expected PF regeneration level and the probability of PF regeneration termination of the route can be increased relative to the individual cost functions associated with other factors such as travel time and fuel consumption. In this way, the driver's current psychological state can be quantitatively factored into the estimation of the total cost function associated with each route between the origin and 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 discussed in [the following section]. Figure 3 Detailed description is provided.

[0053] Therefore, traffic information such as signal phase and timing information (SPaT) available from external servers or navigation systems can be considered, while estimating the probability of the PF regeneration process terminating in a given driving cycle and predicting the duration of the journey. For example, a higher number of traffic stops and congestion generally increases both the probability of the passive PF regeneration event terminating and the duration of the journey. The controller can determine each of the expected PF regeneration level and the probability of PF regeneration termination by directly considering traffic conditions (e.g., increasing the expected PF regeneration level and decreasing the probability of PF regeneration termination as the number of traffic stops decreases). Alternatively, the controller can determine each of the expected PF regeneration level and the probability of PF regeneration termination based on calculations using a lookup table, where the input to the lookup table is the current traffic conditions and the output is the expected PF regeneration level and the probability of PF regeneration termination.

[0054] Once the cost function of multiple available routes between the start and end points has been estimated, routes can be ranked based on this cost function, with the highest-ranked route corresponding to the lowest cost function. In one example, when PF regeneration is expected during an upcoming driving cycle (e.g., when PF soot levels are above a threshold), the route with the highest expected PF regeneration level and the lowest probability of regeneration event termination can be ranked highest. The highest-ranked (recommended) route could be the one that provides the highest level of regeneration and some degree of fuel economy to the end point without any significant delay. Subsequent routes could provide relatively lower levels of regeneration while also offering some degree of fuel economy, and so on. In another example, when PF regeneration is not expected during an upcoming driving cycle (e.g., when PF soot levels are below a threshold), the displayed routes can be ranked based on the time and / or fuel cost to reach the end point, and the recommended route can be selected independently of its ability to complete PF regeneration. Therefore, the highest-ranked (recommended) route could be the one that reaches the end point in the shortest time or uses the least amount of fuel.

[0055] Once one or more routes are selected and ranked from the database, at point 214, the selected routes can be displayed to the driver in their ranked order. The in-vehicle navigation system's screen and user interface can be used to display the selected routes to the operator.

[0056] At 216, the routine includes determining whether the driver has selected a route from the 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.

[0057] At point 220, as the driver follows the selected route, in addition to passive regeneration, the controller can also schedule active regeneration of the power PF during the journey from start to finish. This scheduling can be based on soot levels, upcoming road conditions, and corresponding engine operating conditions. For example, PF regeneration can be scheduled once the soot level increases above a threshold level and driving conditions are favorable for PF regeneration (e.g., when engine load is above a threshold load and engine temperature is above a threshold temperature). During scheduled PF regeneration, the exhaust temperature can be increased by transmitting electricity through the PF to burn off the soot accumulated on the PF, thereby reducing the soot load on the PF. Passive regeneration of the PF can occur during the journey when the exhaust temperature is above a threshold and the exhaust has a desired chemical composition that facilitates the oxidation of soot deposited on the PF.

[0058] At 222, the routine includes determining whether the driver has deviated from the intended route. Each of the passive and active regeneration of PF regeneration can be affected by unexpected changes in driving and traffic conditions. In one example, a detour may involve more traffic signals compared to the intended route, and frequent traffic stops can adversely affect PF regeneration. If it is determined that there is no detour, the scheduled PF regeneration can continue, and at 226, the routine includes determining whether the intended destination has been reached. If it is determined that the intended destination has not been reached, then at 224, the scheduled PF regeneration can continue.

[0059] Once the endpoint is confirmed, at point 228, the soot load on the power filter (PF) can be estimated via the exhaust pressure sensor, and the data can be updated. By estimating the remaining soot level in the PF, the amount of soot burned during the regeneration process can be estimated. Based on the soot removal level during the driving cycle, the PF regeneration level reached during driving along the route, and the probability of terminating the PF regeneration process during driving along the route, can be estimated.

[0060] At point 230, the route database can be updated using information including fuel consumption during travel along the route, travel time (duration), traffic information, power PF regeneration schedule, achieved power PF regeneration level, and driver psychological state (including conditions that trigger changes in psychological state). Reference Figure 4 Provide a detailed example method for updating the database after each trip.

[0061] 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 driver history, such as that stored in a database. As an example, predictions can be performed considering the current vehicle location, routes frequently traveled during a specific time of day and a specific day of week, and the driver's psychological state. Traffic conditions near the current vehicle location (such as traffic congestion) and weather conditions (such as rain or snow forecasts) can also be taken into account when predicting the destination. In one example, predictions can be performed incrementally during the trip. The vehicle controller can divide the route into segments and predict the expected destination for the initial segment. A greedy algorithm can be used to predict intermediate points along the way to the destination by employing the optimal path segment.

[0062] If at step 222 it is determined that a detour has been taken and the vehicle will no longer travel to the expected destination via the route selected in step 216, the routine can proceed to step 232, where the final destination or intermediate point can be predicted based on information available in the database. At step 234, based on the predicted destination (or the upcoming intermediate point), the controller can use stochastic dynamic programming to update one or more selected routes for reaching the predicted destination. The selection process can 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 has been estimated, the routes can 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 can be the most recommended route. Once one or more routes have been updated and ranked, the routine proceeds to step 214, where the selected routes can be displayed to the driver in their ranking order. At point 216, if the driver does not receive any recommended route, then at point 236, the controller can predict a route based on driver preferences (corresponding to the driver's mental state), current traffic, and weather conditions at the current location and destination. As an example, the driver might prefer to take a certain route during a sunny weekday morning. In another example, intermediate road segments could be predicted based on driver history retrieved from a database. The controller can then schedule PF regeneration events based on the predicted road segments.

[0063] In this way, in response to the driver not indicating the end of the driving cycle, the vehicle's current position can be determined, the driver's driving history can be retrieved, the end point can be predicted based on the driving history, the selection of one or more upcoming road segments can be dynamically updated based on the vehicle's current position relative to the predicted end point, the road segments can be ranked based on each of particulate filter regeneration efficiency, fuel efficiency, and travel time, and the driver can be shown one or more road segments leading to the predicted end point in their ranking order.

[0064] Figure 3 An example method 300 is shown for real-time estimation of the driver's psychological state and the impact of the driver's psychological state on route selection. Method 300 may be part of method 200 and may be performed at step 212 of method 200. Route selection may be further based on the soot load accumulated on the particulate filter (PF).

[0065] At point 302, the controller can obtain the travel date, travel time (including the time of day during which the vehicle travels), and the day of the week in which the vehicle travels. The controller can obtain this information wirelessly from an in-vehicle navigation system (e.g., a GPS device) or from a cloud network. At point 308, the controller can obtain origin characteristics including geographic location, current weather conditions, and traffic conditions. For example, based on information from the vehicle navigation system or the cloud network, the controller can determine origin characteristics. In one example, the geographic location of the origin may include the GPS coordinates of the origin. Weather conditions may include temperature, humidity, wind speed, and precipitation (such as rain, snow, etc.). Traffic conditions may include speed limits on the road the vehicle is operating on, overall traffic movement speed, average distance between vehicles, traffic congestion, etc. Furthermore, the controller can obtain the geographic location of the destination based on input from the driver to the navigation system.

[0066] At 310, it can be obtained from a database (such as...) Figure 1Database 13) retrieves driver history, including driving characteristics of the driver. In one example, a driver can be identified by the specific key used to operate the vehicle. In another example, a driver can be identified based on the time, day, date of travel, and the geographical location of the starting point. Thus, a particular driver (such as Driver 1) may operate the vehicle during a specific time (or time window) on a weekday, while a different driver (such as Driver 2) may operate the vehicle during weekends and within a specific time window. Driver characteristics may include the frequency of brake use, average acceleration applied, average lane change frequency, etc. Driver characteristics may vary based on the time, day, date, weather, and geographical location of the starting point. In one example, a driver may drive more aggressively (e.g., more frequent acceleration, increased lane changes, higher speeds) on a sunny day compared to a driver's driving style on a rainy day. Further driver preferences can also be retrieved from the database, including frequently traveled routes, stops made during travel, etc. In one example, a driver may travel to a specific destination on each weekday morning. If a driver leaves the starting point at a certain time, the driver may typically make stops en route to the destination. However, if the driver leaves the starting point later, they can drive more aggressively to the same destination without making any stops. Therefore, the driver's time constraints can vary depending on the day of the week; for example, a driver might have higher time constraints on weekdays compared to weekends. In another example, the driver's preference might depend on weather conditions; for example, if it's snowing or snow is predicted, the driver might take a different route (from the starting point to a frequently traveled destination) to avoid uphill sections. In yet another example, the driver's preference might depend on traffic conditions; if there is traffic congestion at the starting point, the driver might take a different route than a frequently traveled route. In a further example, the driver might choose a route based on fuel levels; for example, if the fuel level in the tank is below a threshold, the driver might choose a shorter route. In yet another example, route selection might be based on the vehicle's payload, such as the number of passengers in the vehicle or whether it is being towed. The database is updated using information about the driver's characteristics and preferences.

[0067] At point 312, based on retrieved information including driver history (driver characteristics and preferences), travel date and time, and origin characteristics (weather and traffic conditions), the controller can assign an initial mental state to the driver. The driver's mental state directly influences their behavior during the trip (driving style), affecting the probability of PF regeneration and regeneration termination. Mental states are situational and probabilistic, and each driver can have multiple mental states (S...). DK(K = 1, 2, 3, ..., n), and each state can have a different effect on PF regeneration. In one example, the driver can have three different mental states, namely the first state S. D0 Second state S D1 and the third state S D2 First mental state (S) D0 This corresponds to the optimal state for PF regeneration. When the driver operates in this optimal mental state, driving characteristics (such as the frequency of brake use, average acceleration applied, average lane change frequency, etc.) can promote PF regeneration without increasing the probability of termination of regeneration. For example, in the optimal state, the driver can operate the vehicle at a stable speed for a longer period of time while applying the brakes less frequently; the driver can maintain a steady pace for short periods without accelerating or decelerating; and the driver can change lanes less frequently. In this optimal mental state, passive PF regeneration can be performed at a higher level, thus enabling more thorough PF cleaning. The second driver mental state (S) D1 This corresponds to a suboptimal state of PF regeneration, where the regeneration level achieved is lower than that achieved in the first psychological state. When a driver operates in this suboptimal psychological state, driving characteristics (such as frequent acceleration and deceleration, lane changes, and stopping) can reduce the PF regeneration level and / or increase the probability of premature termination of regeneration. The third driver psychological state (S) D2 This corresponds to the least ideal state of PF regeneration, where the achieved regeneration level is lower than that achieved in each of the first and second psychological states. When the driver operates in this least ideal psychological state, driving characteristics (such as frequent braking, driving at low speeds) can further reduce the PF regeneration level and / or further increase the probability of premature termination of regeneration. In this least ideal state, passive PF regeneration can be frequently interrupted, and regeneration may not be performed to the desired level.

[0068] Since the driver's psychological state affects PF regeneration, the regeneration influence factor can be associated with each driver's psychological state. At point 314, an initial regeneration influence factor corresponding to the initial psychological state can be determined. In one example, the controller can 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 Example table 800 shows the regenerative input factors corresponding to each mental state. The first row 802 shows the input factors corresponding to the optimal mental state (S). D0 The first regeneration influence factor f0 is shown in the second row (804), corresponding to the suboptimal psychological state (S). D1 The second regeneration factor f1 is shown in the third row (806), corresponding to the least desirable psychological state (S). D2The third regeneration impact factor f2.

[0069] At position 316, the first set of scaling factors corresponding to the initial regeneration impact factors can be determined. Each regeneration impact factor can have a corresponding set of scaling factors, which can be applied to the weights used in the cost function calculation for each route between the origin and the end point. Figure 2 As shown in step 212, based on the PF regeneration requirement, one or more routes can be selected from the database for travel between the current vehicle location and the destination. Dynamic programming can 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 a route can be estimated using Equation 1. The total cost function associated with a particular route between the origin and the destination can be the sum of individual cost functions corresponding to each of the expected PF regeneration level from the origin to the destination, the probability that the PF regeneration process will be terminated during the driving cycle, the cost of heating the PF to a temperature at which regeneration can begin, fuel consumption, and the duration of the journey between the origin and the destination. The weights corresponding to the individual cost functions can be adjusted based on a scaling factor corresponding to the regeneration impact factor. In one example, the scaling factor can be multiplied by the corresponding weight of the cost function to determine the scaling weight.

[0070] Figure 9 Table 900 shows scaling factors corresponding to the regeneration impact factor and cost function weights. The first column 902 of Table 900 lists the weights associated with the individual cost functions of the total cost function of the route between the start and end points. The second column 904 lists the individual cost functions. A first weight w1 may be associated with the expected level of PF regeneration along the route; a second weight w2 may be associated with the cost of heating the PF to a temperature at which regeneration can 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 along the route.

[0071] Column 3, 906 of the table shows the 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 can 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) does 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 does not need to force any particular trip criterion to be critical, and therefore the costs associated with all trip parameters can be weighted equally. Column 4, 908 of the table shows the second set of scaling factors corresponding to the second regeneration impact factor (f1) for each weight. For the second regeneration impact 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) can be 0.25, while the scaling factor assigned to each of w3 (fuel consumption) and w4 (trip duration) can be 0.125. By assigning higher scaling factors to each of w1, w2, and w5, the individual cost associated with the route's PF regeneration efficiency can be increased relative to the individual cost function associated with other factors such as travel time and fuel consumption. Based on the increase in the cost function associated with the route's PF regeneration efficiency, it can be inferred that a second regeneration impact factor (f1) could adversely affect the route's PF regeneration efficiency if regeneration is attempted.

[0072] Column 5, 910 of the table shows the third set of scaling factors corresponding to the third regeneration impact factor (f2) for each weight. For the third regeneration impact 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 factors 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 individual cost function associated with the PF regeneration efficiency of the route can be further increased, while the individual cost functions related to travel time and fuel use may not be considered in the total cost function estimate. The third regeneration impact factor (f2) can significantly affect the PF regeneration efficiency of a 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, thus attempting to increase the trip cost of regeneration in these conditions.

[0073] At point 318, weights scaled by the first set of scaling factors corresponding to the initial regeneration impact factor can be used to estimate the total cost function associated with each route between the origin and destination, such as those selected from the database. The estimation of the total cost function, based on individual cost functions associated with the expected PF regeneration level of the route, the probability that the PF regeneration process will be terminated during the driving cycle, the cost of heating the PF to the regeneration temperature, fuel consumption, and trip duration, can be performed using Equation 1. Multiple routes between the origin and destination can be selected from the database, and the total cost function for each of the multiple routes can be estimated. Details of the total cost function estimation are in... Figure 2 The steps are described in step 212.

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

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

[0076] The first route, as shown in line 502, corresponds to the highest expected PF regeneration level but has the longest travel duration. The fourth route, as shown in line 508, corresponds to the lowest expected PF regeneration level but has the shortest travel duration. Each of the second route, as shown in line 504, and the third route, as shown in line 506, corresponds to an intermediate level of expected PF regeneration level and travel duration. In this example, the driver selects the first route (line 502) corresponding to the highest expected PF regeneration. Based on the driver's choice, it can be inferred that the driver has no time constraints during this driving cycle and can schedule PF regeneration during the driving cycle. If the driver starts on the selected route but does not follow it and takes a detour (discussed below), the controller can assume that the current driver prefers to select a route that achieves a higher level of PF regeneration, while predicting the destination and ranking the routes (based on driver history).

[0077] Return to Figure 3 During the driving cycle, to determine the driver's current mental state, at point 320, the real-time interaction between the driver and traffic can be determined. Based on the interaction between the driver and traffic, and further based on environmental factors (such as weather) and behavioral factors (such as reactions to certain situations), the driver's mental state can change from one mental state 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 may drive through a busy street with frequent traffic stops, where the vehicle stops for multiple short durations. In another example, the driver may reduce the number of stops, but the duration of the stops is longer. As the number of stops increases, the probability of termination of regeneration increases. The frequency and force with which the driver applies each of the accelerator and brake pedals can also be determined. In one example, the driver's foot can be guided and will accelerate and brake frequently. In another example, the driver may 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 traveling on the road) and the distance between two consecutive vehicles traveling in the lane can be determined. Therefore, at lower traffic speeds, if the distance between consecutive vehicles is small, the driver is more likely to need to apply the brakes more frequently. Conversely, at higher traffic speeds, and when the distance between consecutive vehicles is larger, vehicles can travel at a stable speed for a longer period of time.

[0078] At point 322, the controller can determine the driver's psychological state (e.g., from the initial state S) based on the known interaction between the driver and traffic, and further based on environmental and behavioral factors. D0 to state S DKThe possible transitions between mental states can be considered. In one example, the driver's mental state may remain unchanged throughout the driving cycle, while in another example, the driver's mental state may frequently change from one state to another. A non-homogeneous transition model can be used to determine the probability of transitioning from one mental state to another. In one example, a simplified homogeneous transition matrix (T) can be used to predict the possible transitions from one mental state to another.

[0079] Figure 7 Example 700 of a homogeneous transition matrix (T) is shown. In the 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 driver history (such as that retrieved from a database). The finite probabilities in the homogeneous transition matrix may not change based on driver behavior (interactions between the driver and traffic) or conditions (such as weather in the current driving cycle).

[0080] As can be seen from matrix 700, the first row 702 represents the current mental state S. D0 Transition to state S D1 and S D2 The probability of S. If the current state is assigned as S. D0 Then the driver will remain in S throughout the entire driving cycle. D0 The probability of being in state S is 70%, and the driver can transition to state S at any point during the driving cycle. D1 The probability is 20%, and the driver can directly access state S at any point during the driving cycle. D0 Transition to state S D2 The probability is 10%. The second line, 704, indicates the probability from the current mental state S. D1 Transition to state S D0 and S D2 The probability of S. If the current state is assigned as S. D1 Then the driver will remain in S throughout the entire driving cycle. D1 The probability of being in state S is 50%, and the driver can transition to state S at any point during the driving cycle. D0 The probability is 25%, and the driver can switch to state S at any point during the driving cycle. D2 The probability is 25%. The third line, 706, indicates the probability from the current psychological state S. D2 Transition to state S D0 and S D1 The probability of S. If the current state is assigned as S. D2 Then the driver will remain in S throughout the entire driving cycle. D2The probability of being in state S is 50%, and the driver can directly transition to state S at any point during the driving cycle. D0 The probability is 25%, and the driver can switch to state S at any point during the driving cycle. D1 The probability is 25%.

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

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

[0083] Where S Dk Let t be the driver's psychological state, n be the time, n be the number of possible driver psychological states, and T be 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 takes into account real-time interactions between the driver and traffic, as well as other evolutionary conditions (such as weather), while determining the state transitions from the initial driver mental state to the updated driver mental state. Heterogeneous transition models can be experimentally identified and validated using 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 (SD... j The probability of transitioning to the updated state (SDk) 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, tine 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. Diagram 600 illustrates three possible driver mental states S. D0 S D1 and S D2 In one example, if (based on driver history) the initial driver mental state is determined to be the first state S... D0 Then the driver can continue operating in the first mental state throughout the entire driving cycle. First driver mental state S D0 The optimal approach is to achieve increased PF regeneration efficiency. Therefore, in the first state, the driver can maintain a stable vehicle speed for a period of time without hard acceleration and deceleration. If weather and / or traffic conditions (which can impose constraints and increase the duration of the journey) do not change significantly, the driver can continue operating in the first state. The probability that the driver will remain in the first mental state is given by P(S... D0 |S D0 (This is given.)

[0087] In response to driver-traffic interactions and / or changes in weather, the psychological state can shift from the first state S. D0 Transition to the second state S D1 Second state S D1 A suboptimal mental state can negatively impact PF regeneration efficiency. In one example, the interaction between the driver and traffic can change due to weather variations, such as if heavy rain begins during driving. Consequently, the driver may accelerate briefly and then frequently apply the brakes to control speed. Frequent braking can negatively affect PF regeneration efficiency. In another example, anticipated traffic congestion may cause the driver to stop for longer than expected, and after overcoming the congestion, the driver can drive aggressively to reach the destination without further delay. The probability of transitioning from the first state to the second state is given by P(S... D1 |S D0 The probability that the driver will remain in the second mental state is given by P(S). Once in the second state, the driver can continue to operate in the second state for the remainder of the driving cycle. D1 |S D1 (This is given.) After operating in the second state for a period of time, in response to changes in driver-traffic interaction and / or weather, the driver may switch to the first state S. D0 In one example, due to changes in traffic conditions (such as fewer cars on the road), a driver may begin to drive less aggressively and may subsequently change their mental state.

[0088] In response to changes in driver-traffic interaction and / or weather conditions, the driver's psychological state can also shift from the second state S.D1 Transition to the third state S D2 Third state S D2 This can be the least desirable psychological state and can negatively impact PF regeneration efficiency. In one example, due to further changes in weather (such as if it starts snowing 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 aggressively begin driving with increased braking frequency. The probability of transitioning from the second state to the third state is given by P(S... D2 |S D1 The probability that the driver will remain in the third mental state is given by P(S). Once in the third state, the driver can continue to operate in the third state for the remainder of the driving cycle. D2 |S D2 (This is given.) After operating in the third state for a period of time, in response to changes in driver-traffic interaction and / or weather, the driver may switch to the second state S. D1 In one example, weather conditions affecting the interaction between the driver and traffic can change, and the driver can make fewer stops during the driving cycle. The probability of transitioning from the third state to the second state is given by P(S). D1 |S D2 The mental state can also transition directly from the third state to the first state to facilitate improved PF regeneration. In one example, road congestion can be relieved, allowing the driver to accelerate optimally without frequent braking for 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 The probability of transitioning from state 1 to state 3 is given by P(S). Therefore, unexpected changes in traffic conditions (such as road closures) can lead to a transition from state 1 to state 3. Due to these unexpected changes, drivers may take alternative routes, and their driving motivation may increase. The state change from state 1 to state 3 can lead to the termination of the ongoing PF regeneration. The probability of transitioning from state 1 to state 3 is given by P(S). D2 |S D1 This is given. In this way, during the driving cycle, the interaction between the driver and traffic, as well as environmental conditions, can lead to changes in the driver's psychological state.

[0089] Based on the known interactions between the driver and traffic, the controller can determine the updated driver mental state during a driving cycle. The controller can use a non-homogeneous transition model to determine the updated (current) driver mental state. Therefore, the driver's mental state can undergo multiple changes during a driving cycle (additional mental states may exist, not described here), and the controller can continue to update the current driver mental state based on changes in operating conditions as described above.

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

[0091] Once the total cost function of multiple available routes between the origin and destination has been estimated, the routes can be re-ranked based on this total cost function, with the highest-ranked route corresponding to the lowest total cost function. In one example, the route ranking can be changed after the driver's mental state is updated. In another example, the route ranking can remain unchanged after the driver's mental state is updated. At point 325, the updated route ranking estimated based on the updated total cost function can be displayed to the driver.

[0092] Figure 5B Screenshot 550 of an example in-vehicle navigation system is shown, where updated route options are displayed after a change in the driver's mental state. At the start of a driving cycle, based on the initial driver's mental state, such as... Figure 5A As shown, the selected routes between the start and end points are ranked and displayed. After selecting the first route (from the displayed routes) corresponding to the highest achievable level of power PF regeneration, the driver can proceed along the selected route. However, after operating on the selected first route for a period of time, changes in traffic and / or environmental conditions can alter the driver's psychological state, and based on the updated driver psychological state, the previously ranked highest route may no longer remain the route corresponding to the highest achievable level of power PF regeneration. In one example, the change in driver 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, which is more suitable for steady-state operation and achieving the desired level of power PF regeneration.

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

[0094] Therefore, previously displayed routes can be shown again, but the route ranking can be changed. In one example, with Figure 5A The second-ranked route is now available. Figure 5B The route shown in the middle is the highest ranked route, while... Figure 5A The routes displayed as the highest ranking are available in [the following]. Figure 5B The route shown in the middle is ranked second. Figure 5A Routes that are displayed as the third ranked route will no longer be displayed as route options. Figure 5A The route shown as the fourth ranked route is available in [the following text is missing from the original] Figure 5B The route is displayed as the third ranked route, while a new route between the current geographic location and the destination can be retrieved from the database and included as a new (fourth ranked) option. In this example, the driver can select the first route corresponding to the highest achievable level of PF regeneration. Since the first route in the updated list is different from the previously selected route, updated navigation instructions are provided to allow the driver to change course from the current route. The driver can then continue driving along the newly selected route to the destination. In this way, the ranking of routes can be updated in real time based on the current driver's mental state, and the updated ranking can be displayed to the driver. As the driver's mental state changes based on real-time conditions, similar updates to the list of recommended routes will appear multiple times during the driving cycle.

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

[0096] Return to Figure 3At 326, the routine includes determining whether a key-off event indicating that vehicle operation has ceased is present. If no key-off event is indicated, at 328, the controller can continue to be aware of the current driver's mental state in real time and, based on the updated driver's mental state, can update route suggestions in real time. Updates may include updating the ranking of previously displayed routes or showing the driver (retrieved from a database) new routes to increase the achievable PF regeneration level during a given driving cycle.

[0097] At point 330, the database can be updated using information acquired during the current driving cycle, including driver-traffic interactions, displayed routes, traveled road segments, different driver psychological states, and actual particulate filter regeneration. Furthermore, the probability of each change in the environment leading to a shift in driver psychological state (from one psychological state to another) can be included in the update. Figure 4 The details of updating the database using information obtained during the current driving cycle are discussed in detail.

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

[0099] Figure 4 An example method 400 for updating a database of frequently driven routes is shown. At 402, the routine may include determining whether a vehicle key unlocking event exists. For example, it may be determined whether the vehicle driver has expressed an intention to start vehicle operation. Thus, by confirming a vehicle key unlocking event, an upcoming vehicle driving cycle is indicated. If no vehicle key unlocking event is detected and therefore no upcoming vehicle driving cycle is confirmed, the method may terminate and the database may not be updated.

[0100] If a vehicle key unlocking event is confirmed, at step 404, the controller can learn the starting point characteristics, including the time and geographical location of the key unlocking event. For example, based on information from the vehicle's navigation system (e.g., a GPS device), the controller can determine the starting point characteristics. In this way, the controller can determine the amount of time the vehicle has been stopped at a certain location (e.g., the starting point) before the start of the journey. Furthermore, the duration elapsed since the immediately preceding key ignition event can be determined. That is, the duration the vehicle has been stopped at its current location can be estimated.

[0101] At point 406, the controller can obtain details about the vehicle's route, including the road segments it is traveling on. This may include terrain information (such as road slope, inclination, and topography) for each road segment in the actual route. The details may be obtained based on information from the vehicle's navigation system and / or from an external server via wireless communication. At point 408, the controller can obtain details about intermediate stops en route to the destination. Stops may be due to traffic signals, traffic congestion, or may be intentionally caused by the driver. The geographical location and duration of each stop are also known. At point 410, route traffic information, including the number of traffic stop stations, is obtained via the navigation system. The controller can also determine the speed limits for each road segment and the actual speed of the vehicle.

[0102] At point 412, the controller obtains the travel time, including the time of day, date, and day of the week during which the vehicle traveled. At point 414, the controller obtains data from multiple engine sensors (such as...) during travel on each road segment. Figure 1 The sensor 16 in the controller monitors engine operating conditions such as engine speed, engine load, and engine temperature. Furthermore, the controller can determine the level of carbon soot accumulated on the PF during vehicle operation.

[0103] At point 416, the controller can determine the PF regeneration level achieved during the current driving period. In one example, the PF regeneration level can be determined based on changes in PF soot levels, such as those estimated via exhaust pressure sensors (the change between soot levels measured at the start of driving and at the end of each road segment). In another example, the PF regeneration level can be determined based on the duration of PF regeneration. Furthermore, it can be determined whether the PF regeneration process was prematurely terminated due to adverse operating conditions during the driving cycle. The reason for PF regeneration termination, engine operating conditions, and road conditions under which the PF regeneration event was terminated can also be recorded.

[0104] At point 418, the controller can learn the driver's driving characteristics. These may include, for example, the frequency of brake and accelerator pedal application, the frequency of brake and accelerator pedal release, the transmission shift frequency, and the duration of operation from electric mode to engine mode. The controller can also learn, based on these driving characteristics, the driver's different psychological states during the driving cycle and the duration of each psychological state. Furthermore, it can learn the conditions (traffic, environment, behavior, etc.) that trigger changes in the driver's psychological state. Furthermore, in each driver psychological state, the controller can learn the probability of transitioning from the current psychological state to another psychological state as a function of the current psychological state and the conditions that trigger the change in the driver's psychological state.

[0105] At 420, the routine includes determining whether a key-off event indicating that vehicle operation has stopped exists. If no key-off event is indicated, at 422, the controller may continue to collect data on various aspects of vehicle operation during vehicle travel. If the vehicle is confirmed to have stopped, at 424, the method includes obtaining destination characteristics, including the geographic location of the destination.

[0106] At point 425, the controller can determine the fuel consumed during the journey from the starting point to the destination. As an example, fuel consumption can be estimated based on the initial and final fuel levels in the fuel tank. In another example, fuel consumption can be estimated based on engine operating conditions. Furthermore, the duration of the journey and the time taken to travel from the starting point to the destination can be determined.

[0107] At 426, the database can be updated using all the aforementioned data (collected in steps 404 to 424), including information about the current driving route, engine condition, PF regeneration information, and driver driving characteristics. At 428, the current route used for driving between the origin and destination can be compared with one or more routes previously stored in the database (between the origin and destination). The cost function estimated according to Equation 1 can 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 can be ranked according to the highest fuel efficiency, shortest driving duration, highest level of PF regeneration achieved, and any other cost function chosen by the operator. Moreover, a Markov chain-based route ranking algorithm can be used. As an example, a route with a target level of PF regeneration may not be the most fuel-efficient route.

[0108] In this way, when the vehicle key is turned off, the database can be updated using route information, origin characteristics, destination characteristics, driver behavior, achieved PF regeneration level, engine condition, date and time information, and traffic information.

[0109] Figure 10 A predictive example of the forecasting 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 driver's current mental state and the selected route can be displayed. The horizontal line (x-axis) represents time, and the vertical markers t1 to t6 identify critical times in the operation of the vehicle system.

[0110] At time t1, the driver starts the vehicle (e.g., a key-opening event) and the vehicle's current geographical location, as determined by the in-vehicle navigation system, is represented by A. The driver initially indicates the geographical location of the destination, as represented by B, via input to the navigation system. The controller retrieves the driver's driving history from the database, including driver characteristics and preferences (such as the frequency of brake use, average acceleration used, average vehicle change frequency, etc.). The controller can determine the current travel date and time, starting point characteristics (weather conditions and traffic conditions), and based on the retrieved data (the above information), the controller assigns an initial mental state to the driver. The controller then uses dynamic programming to select one or more routes from the database based on PF soot levels, fuel consumption, travel time, and traffic conditions. Line 1020 shows the change in soot levels deposited on the PF. Dashed line 1021 represents a threshold 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 the threshold soot level during the journey from point A to point B, and PF regeneration needs to be performed. The controller then ranks one or more selected routes based on a weighted function of each of the particulate filter regeneration efficiency, the probability of a PF regeneration event being completed, fuel efficiency, and travel time. When ranking the selected routes, the weighting function is adjusted based on a regeneration efficiency factor corresponding to the driver's initial mental state. In this example, the initial driver mental state is the optimal mental state (S0). D0The particulate filter (PF) regeneration efficiency is the highest among all routes, and it also increases the probability of completing a PF regeneration event. Particulate filter regeneration efficiency is the amount of particulate filter regeneration achieved during a driving cycle via a combination of passive and active regeneration. In passive regeneration, soot is burned due to higher exhaust temperatures during higher load conditions, and in active regeneration, the temperature of the PF can be increased by allowing current to flow through it. In response to an upcoming PF regeneration request, during the ranking of one or more routes, higher weights are assigned to each of particulate filter regeneration efficiency and the probability of completing a PF regeneration event, and lower weights are assigned to fuel efficiency and driving time. In this way, the controller displays the routes with the highest particulate filter regeneration efficiency and the probability of completing a PF regeneration event at the highest ranking, and the routes with the lowest particulate filter regeneration efficiency and the probability of completing a PF regeneration event at the lowest ranking. The list is then displayed to the driver so they can select a route based on the ranking.

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

[0112] The driver follows the predicted route and continues driving via route 404. However, after the first intermediate stop C, at time t3, the driver deviates from route 406. Furthermore, at time t3, based on the driver's interaction with traffic between times t1 and t3, the controller updates the driver's psychological state from the optimal psychological state to a second optimal psychological state (S). D1Due to the shift to the second optimal mental state, it is known that the driver can operate the vehicle more aggressively, and driving characteristics (such as increased frequency of brake application) can reduce the particulate filter regeneration efficiency and decrease the probability of completing PF regeneration in a given driving cycle. In response to the route change, based on the driver's history and updated mental state, the controller again predicts that the driver can take route 408 to the second intermediate stop D, and from there take route 410 to the third intermediate stop E, then take route 412 to the destination B. However, it is observed that the driver does not take the predicted routes 408, 410, and 412, but instead continues to the destination via new routes 414 and 416. The driver stops at intermediate point F on the way to the destination B and finally reaches the destination at time t4.

[0113] It was observed that the soot level in the PF reached a threshold level 421 at time t3, and passive soot regeneration was performed. However, because the driver did not follow the recommended route optimal for PF regeneration and due to the driver's suboptimal mental state, the PF regeneration level reached after reaching destination B at time t4 was lower than the PF regeneration level that would have been achieved if the driver had taken route 1002 from start point A to destination B. Moreover, the arrangement of passive PF regeneration (between times t3 and t4) may be impossible due to the driver's deviation from both the recommended and predicted routes. Dashed line 1022 shows the possible change in the soot level in the PF if route 1002 were taken and assuming the driver's mental state did not change from the first to the second. As observed from graphs 1020 and 1022, if the recommended route 1002 were taken, the amount of soot burned between times t3 and t4 would be higher relative to the amount burned during travel via routes 1014 and 1016.

[0114] At time t4, upon reaching destination B (after the vehicle key is turned off), the database can be updated using route information, starting point characteristics (such as geographical location), destination characteristics, the location of each stop, driver-traffic interactions (such as gear shifting frequency, pedal application and release frequency, brake application frequency, etc.), achieved PF regeneration level, engine conditions (such as engine speed, engine load, engine temperature, etc.), date and time information, driver psychological state, and traffic information. Furthermore, road gradient, terrain, and inclination information for each segment of the route can be included in the database. The data stored in the database can be used for future route selection and / or prediction.

[0115] In this way, particulate filter (PF) regeneration can be efficiently scheduled and executed by dynamically selecting a driving route from multiple routes available in a database based on the particulate filter regeneration requirements during a driving cycle. By considering the current driver mental state, which represents the real-time driving behavior of the driver, the likelihood of the driver choosing a recommended route (where PF regeneration efficiency is higher) increases. By estimating the driver's mental state in real time based on the interaction between the driver and traffic and environmental conditions, the ranking of navigation routes corresponding to the probability of achieving the desired PF regeneration level can be updated as driver behavior changes. By predicting the destination or segment of the upcoming route based on driving history and driving statistics stored in the route database, PF regeneration can even be scheduled during the trip when the driver has not yet specified a final destination or when the driver deviates from the selected route. The technical effect of maintaining a database of driver mental states and frequently driven routes, along with information including the possible levels of PF regeneration achievable on each route, is that when the vehicle key is turned on, an initial mental state can be selected from the database based on driver history, and a route can be selected from the database based on PF soot levels and the cost function chosen by the driver (including the highest fuel efficiency and lowest driving time in the driving cycle). In this way, during periods of PF soot load exceeding a threshold, timely regeneration of the PF can be achieved by selecting a favorable route, thereby reducing soot overload in the PF and improving engine performance. By allowing the PF to be regenerated in a timely manner, passive regeneration is used, reducing the need for active regeneration and thus providing additional fuel efficiency benefits.

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

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

[0118] In yet another example, the vehicle method includes: in response to the operator not indicating the end point of a driving cycle, determining the vehicle's current position, retrieving the operator's driving history from a database, predicting the end point based on the driving history, dynamically updating the selection of one or more upcoming road segments based on the vehicle's current position 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 ranking order. Any of the foregoing examples further includes, additionally or optionally, in response to the operator not selecting a route from one or more displayed upcoming road segments, dynamically updating 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 includes, additionally or optionally, updating the database with road segment information upon completion of the driving cycle, including operator behavior, the level of particulate filter regeneration achieved, engine conditions during the completed driving cycle, travel date and time information, and traffic information within the driving cycle.

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

[0120] In yet another further example, a method includes: at the start of a driving cycle, selecting a first particulate filter regeneration factor based on the operator's past driving history; displaying the operator one or more driving routes selected from a database, the routes being ranked based on the start and end points of the driving cycle, their respective regeneration completion efficiencies, and each of the first regeneration factors; displaying the operator navigation instructions for the route selected by the operator from the displayed one or more driving routes; and during the driving cycle, selecting a second particulate filter regeneration factor based on real-time interactions between the driver and traffic while driving along the operator-selected route. In any of the foregoing examples, additionally or optionally, each of the first and second regeneration factors 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 optionally, selecting the second regeneration factor includes applying a heterogeneous transformation model to select the second regeneration factor from a plurality of regeneration factors based on the probability of transitioning from the first regeneration factor to the second regeneration factor, the probability being based on real-time interactions between the driver and traffic. In any or all of the foregoing examples, additionally or optionally, the driver's past driving history includes routes traveled by the operator as a function of one or more of the following: time of day, day of week, start and end points of a driving cycle, and driving characteristics including frequency of brake use, average acceleration applied, and average lane change frequency. In any or all of the foregoing examples, additionally or optionally, real-time interaction between the driver and traffic includes one or more of the following during a driving cycle: frequency of stops, frequency of lane changes, accelerator pedal input, and brake input. In any or all of the foregoing examples, additionally or optionally, the selection of the first regeneration factor is further based on traffic conditions and environmental conditions at the start of the driving cycle, including ambient temperature, ambient humidity, and precipitation. In any or all of the foregoing examples, additionally or optionally, ranking one or more routes further includes ranking each of the one or more routes based on a weighted function of each of the following: regeneration completion efficiency, probability of particulate filter regeneration completion, fuel efficiency, and time to reach each end point of each of the one or more routes, the weighting function being scaled based on the first regeneration factor. Any or all of the foregoing examples further include, additionally or optionally, updating the weighting function scaled based on the second regeneration factor in response to selecting a second particulate filter regeneration factor, and then updating the ranking of 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 an exhaust passage upstream of the particulate filter, and a controller having computer-readable instructions stored in a non-transitory memory for: displaying a first route based on PF load and a first driver psychological state at the start of a driving cycle, and displaying multiple updated routes based on a second driver psychological state in response to driver-traffic interactions while driving on the first route, wherein the first driver psychological state is selected from a database based on PF load and each in driver history, and the change from the first driver psychological state to the second driver psychological state is based on driver-traffic interactions while driving on the first route. In any of the foregoing examples, additionally or optionally, 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 optionally, multiple 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 multiple updated routes includes ranking each of the multiple updated routes based on the second weighted PF regeneration efficiency. In any or all of the foregoing examples, additionally or optionally, the controller includes further instructions for: learning about driver-traffic interactions, displayed routes, traveled road segments, driver psychological state, and obtained particulate filter regeneration during a driving cycle, and updating the database based on the learned information after the driving cycle is completed.

[0122] In a further representation, the vehicle is a hybrid vehicle system. In any of the foregoing examples, additionally or optionally, an example method for a hybrid vehicle includes: at the start of a driving cycle, selecting a first value indicating the driver's mental state based on driving history; displaying to the driver one or more routes selected from a database, each of which has a regeneration factor based on the first value; and during the driving cycle, selecting a second value indicating an updated driver mental state in real time based on real-time interactions 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 as executable instructions in non-transitory memory and can be executed by a control system including controllers 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, multitasking, multithreading, etc. Therefore, the various actions, operations, and / or functions described can be executed in the order shown, in parallel, or omitted in some cases. Similarly, the processing order is not required to realize 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 shown actions, operations, and / or functions can be repeatedly executed. Furthermore, the described actions, operations, and / or functions can be graphically represented as code encoded into a non-transitory memory of a computer-readable storage medium in an engine control system, wherein the described actions are realized by cooperating with the electronic controller to execute instructions in a system including various engine hardware components.

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

[0125] The appended claims specifically point to certain combinations and sub-combinations that are considered novel and non-obvious. These claims may relate to a “one” element or a “first” element or its equivalent. These claims should be understood to include combinations of one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and sub-combinations of the disclosed features, functions, elements, and / or characteristics may be claimed by amending existing claims or by filing new claims in this or related applications. These claims, whether broader, narrower, identical, or different in scope from the original claims, are considered to be included within the subject matter of this disclosure.

Claims

1. A method for a vehicle, comprising: After a driving cycle, the particulate filter regeneration efficiency is determined based on the characteristics of the driving route and a function of the operator's behavior on the driving route, wherein the particulate filter regeneration efficiency includes the degree of particulate filter regeneration predicted for the driving cycle. Based on the route for updating the database; as well as At the start of a subsequent driving cycle, the operator is shown one or more routes selected from the database, the selection being based on the particulate filter soot load at the start of the subsequent driving cycle.

2. The method of claim 1, wherein the selection is further based on the end point of the driving cycle as indicated by the operator relative to the start point of the driving cycle.

3. The method of claim 1, wherein the selection is further based on an operator-selected cost function, the operator-selected cost function including one or more of the highest fuel efficiency and the lowest driving time in the driving cycle.

4. The method of claim 1, further comprising: The one or more routes are ranked based on a weighted function of each of the particulate filter regeneration efficiency, the probability of a particulate filter regeneration event, fuel efficiency, and travel time.

5. The method of claim 4, wherein the particulate filter regeneration efficiency includes the degree of particulate filter regeneration predicted for the driving cycle, and wherein one or more characteristics of the driving route include the number of stops, road gradient, and traffic conditions.

6. The method of claim 4, wherein when the particulate filter soot load is above a threshold, a higher weight is assigned to each of the particulate filter regeneration efficiency and the probability of completing particulate filter regeneration, and wherein when the particulate filter soot load is below the threshold, a higher weight is assigned to each of the fuel efficiency and the driving time.

7. The method of claim 1, further comprising: In response to the operator not selecting a route from the one or more routes displayed to the operator, the upcoming road segment is dynamically predicted based on the operator's driving history retrieved from the database, wherein the operator's driving history includes routes previously traveled as a function of one or more of the time of day, day of week, and traffic conditions.

8. The method of claim 1, further comprising: In response to the operator selecting a route from one or more routes displayed to the operator, and initially driving along the selected route but then deviating from the selected route, the upcoming road segment is dynamically predicted based on the operator's driving history retrieved from the database.

9. The method of claim 8, further comprising: Based on the route selected by the operator from one or more displayed routes, an active particulate filter regeneration event is scheduled during the driving cycle, wherein during the active particulate filter regeneration event, the temperature of the particulate filter is increased by allowing an electric current to flow through the particulate filter.

10. The method of claim 1, further comprising: When the operator does not specify the end point of the driving cycle, the end point is predicted based on the operator's driving history retrieved from the database, and one or more routes selected from the database are displayed to the operator based on the predicted end point.

11. The method of claim 1, wherein the start of the driving cycle includes a vehicle key unlocking event, and wherein updating the database based on the knowledge includes: When the vehicle key is turned off, the database is updated using route information, origin characteristics, destination characteristics, operator behavior, achieved particulate filter regeneration level, engine operating status, date and time information, and traffic information.

12. A vehicle system comprising: vehicle; The navigation system is wirelessly connected to an external network; monitor; An engine, comprising an intake system and an exhaust system, the exhaust system comprising a particulate filter (PF) coupled to an exhaust passage and a pressure sensor coupled upstream of the particulate filter to the exhaust passage; and The controller, having computer-readable instructions stored in non-transitory memory, is used for: In response to the operator's endpoint selection indicated via the display. Estimate the carbon soot load on the exhaust particulate filter; Determine the current location of the vehicle; Retrieve one or more routes from the current location to the destination from the database; The one or more routes are ranked based on each of the particulate filter regeneration efficiency, fuel efficiency, and driving time for each route, wherein the particulate filter regeneration efficiency includes the degree of particulate filter regeneration predicted for a driving cycle and is known after the driving cycle as a function of the characteristics of the route traveled and the operator’s behavior on the route traveled. as well as The operator is shown the one or more routes to the selected destination in the order of their ranking.

13. The system of claim 12, wherein the controller includes further instructions for: ranking the one or more routes in response to particulate filter soot load exceeding a threshold by assigning higher weights to particulate filter regeneration efficiency and lower weights to each of the fuel efficiency and the driving time.

14. The system of claim 13, wherein the controller includes further instructions for: ranking the one or more routes in response to particulate filter soot load below a threshold by assigning the higher weight to each of the fuel efficiency and the driving time and assigning the lower weight to the particulate filter regeneration efficiency.

15. The system of claim 12, wherein the controller includes further instructions for: in response to the operator not selecting a route from the one or more displayed routes or the operator deviating from a selected route, predicting one or more road segments from the current location to the destination based on the operator's driving history retrieved from the database, and scheduling passive regeneration of the particulate filter based on the one or more predicted road segments.

Images