Systems and methods for particulate filter regeneration

• US 10,393,533 B2
• Filed: 05/26/2017
• Issued: 08/27/2019
• Est. Priority Date: 05/26/2017
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Upadhyay, Devesh, Hopka, Michael Brendan
• Primary Examiner(s): Sweeney, Brian P
• Application Number:
  US15/606,898
• Publication Number:
  US 20180340785A1
• Time in Patent Office: 823 Days
• Field of Search: 701414
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: F01N 2900/0402 using adaptive learning
• CPC Class Codes: F01N 2900/102 Travelling distance
• CPC Class Codes: F01N 2900/12 said parameters being related to the vehicle exterior
• CPC Class Codes: F01N 2900/1606 Particle filter loading or soot amount
• CPC Class Codes: F01N 9/002 of filter regeneration, e.g. detection of clogging
• CPC Class Codes: F02D 2041/1412 using a predictive controller
• CPC Class Codes: F02D 2200/0812 Particle filter loading
• CPC Class Codes: F02D 2200/701 Information about vehicle position, e.g. from navigation system or GPS signal
• CPC Class Codes: F02D 29/02 peculiar to engines driving vehicles; peculiar to engines driving variable pitch propellers
• CPC Class Codes: F02D 41/029 the exhaust gas treating apparatus being a particulate filter
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: Y02T 10/40 Engine management systems

Abstract

Methods and systems are provided for selecting a first travel route for a vehicle from a database based on particulate filter regeneration requirements and an inferred initial driver state of mind. In one example, the initial driver state of mind may be selected based on a past driver history, and during travel along the first travel route, the driver state of mind may be updated based on the driver interactions with traffic. The route selection may also be updated based on the updated driver state of mind.

Claims

1. A method, comprising:

at an onset of a drive cycle, an electronic controller displaying a first driving route responsive to each of a particulate filter (PF) loading and past driving history; and

during travel along the first driving route, the electronic controller displaying an updated route responsive to each of traffic conditions and a comparison of a real-time driving history along the first driving route on the drive cycle relative to the past driving history.

2. The method of claim 1, wherein displaying the first driving route includes selecting the first driving route from a database, including a plurality of driving routes, based on a first inferred driver state of mind, the first inferred driver state of mind based on the past driving history, and wherein displaying the updated route includes selecting the updated route from the database based on an updated driver state of mind.

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

4. The method of claim 3, wherein displaying the first driving route further includes, responsive to an operator indicated destination for the drive cycle, displaying one or more routes retrieved from the plurality of driving routes included in the database, the one or more routes ranked as a first function of corresponding PF regeneration efficiency, a probability of completion of a PF regeneration event during the drive cycle, and a first PF regeneration factor associated with the first inferred driver state of mind, wherein the corresponding PF regeneration efficiency for each of the one or more routes is determined as a function of a corresponding degree of PF regeneration predicted for the drive cycle and the first PF regeneration factor.

5. The method of claim 4, wherein displaying the updated route includes, responsive to the comparison, displaying the one or more routes, the one or more routes ranked as a second function of updated PF regeneration efficiency, the probability of completion of the PF regeneration event during the drive cycle, and a second PF regeneration factor associated with the updated driver state of mind, wherein the corresponding PF regeneration efficiency for each of the one or more routes is determined as a function of the corresponding degree of PF regeneration predicted for the drive cycle and the second PF regeneration factor.

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

7. The method of claim 2, further comprising, learning, at completion of the drive cycle, a degree of PF regeneration attained during the drive cycle and then updating the database with the learned degree of PF regeneration attained for the drive cycle, the first inferred driver state of mind, and the updated driver state of mind.

8. The method of claim 1, wherein the real-time driving history includes real-time driver interactions with traffic including real-time accelerator pedal usage and real-time brake usage during the drive cycle while driving along the first driving route, and wherein the past driving history includes frequency of brake usage, average acceleration force used, and average lane change frequency while driving along the first driving route in one or more drive cycles prior to the drive cycle.

9. A vehicle system, comprising:

a vehicle;

a navigation system wirelessly connected to an external network;

a display;

an engine including an intake system and an exhaust system, the exhaust system including a particulate filter (PF) coupled to an exhaust passage and a pressure sensor coupled to the exhaust passage upstream of the particulate filter; and

a controller with computer readable instructions stored on non-transitory memory for;

at an onset of a drive cycle,

displaying a first route based on PF load and a first driver state of mind; and

responsive to driver interactions with traffic while travelling on the first route,

displaying a plurality of updated routes based on a second driver state of mind, wherein the first driver state of mind is selected from a database based on each of the PF load and a driver history, and a change from the first driver state of mind to the second driver state of mind is based on the driver interactions with traffic while traveling on the first route.

10. The system of claim 9, wherein the first route is selected based on a first weighted PF regeneration efficiency, the first weighted PF regeneration efficiency based on a first PF regeneration factor corresponding to the first driver state of mind.

11. The method of claim 9, wherein the plurality of updated routes is selected based on a second weighted PF regeneration efficiency, the second weighted PF regeneration efficiency based on a second PF regeneration factor corresponding to the second driver state of mind, and wherein the displaying of the plurality of updated routes includes ranking each route of the plurality of updated routes based on the second weighted PF regeneration efficiency.

12. The system of claim 9, wherein the controller contains further instructions for:

learning, during the drive cycle, the driver interactions with traffic, displayed routes, road segments traveled, driver state of mind, particulate filter regeneration attained; and

after completion of the drive cycle, updating the database based on the learning.