VEHICLE CONTROLS INCLUDING DYNAMIC VEHICLE MASS AND ROAD GRADE ESTIMATION DURING VEHICLE OPERATION

• US 20160082964A1
• Filed: 08/31/2015
• Published: 03/24/2016
• Est. Priority Date: 09/23/2014
• Status: Active Grant

• Current Assignee: Cummins Incorporated
• Original Assignee: Cummins Incorporated
• Inventors: Salemme, Gary, Vajapeyazula, Phani, Chunodkar, Apurva, Proctor, Timothy, Sujan, Vivek Anand, Follen, Kenneth, Wu, An
• Granted Patent: US 9,725,093 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0022 Gains, weighting coefficients or weighting functions
• CPC Class Codes: B60W 2510/0657 Engine torque
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2530/10 Weight
• CPC Class Codes: B60W 2530/16 Driving resistance
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/18 Propelling the vehicle
• CPC Class Codes: B60W 40/076 Slope angle of the road
• CPC Class Codes: B60W 40/13 Load or weight

Abstract

Apparatuses, methods and systems including dynamic estimations of vehicle mass and road grade estimation are disclosed. One exemplary embodiment is a method including operating a vehicle system to propel a vehicle, determining with a controller a vehicle mass estimate and an uncertainty of the vehicle mass estimate, evaluating with the controller the uncertainty of the vehicle mass estimate relative to at least one criterion, if the uncertainty of the vehicle mass estimate satisfies the criterion, determining with the controller a road grade estimate, and controlling with the controller utilizing the road grade estimate at least one of a vehicle speed and an engine output.

Claims

1. A method of controlling a vehicle system including an engine and a controller, the method comprising:

operating the vehicle system to propel the vehicle;

during operation of the vehicle system;

determining with the controller a vehicle mass estimate and an uncertainty of the vehicle mass estimate,

evaluating with the controller the uncertainty of the vehicle mass estimate relative to at least one criterion,

if the uncertainty of the vehicle mass estimate satisfies the criterion, determining with the controller a road grade estimate by recursively predicting a road grade based upon a model of the vehicle system and the vehicle mass estimate, correcting the prediction based upon a measurement of operation of the vehicle system, and providing the corrected prediction as the road grade estimate; and

controlling with the controller utilizing the road grade estimate at least one of a vehicle speed and an engine output.

2. The method of claim 1 wherein the criterion comprises an uncertainty threshold and the acts of determining with the controller the vehicle mass estimate and the uncertainty of the vehicle mass estimate and evaluating with the controller the uncertainty of the vehicle mass estimate relative to the criterion are repeated until the uncertainty of the vehicle mass estimate is below the uncertainty threshold.

3. The method of claim 2 wherein the vehicle mass estimate is fixed for a period of operation after the uncertainty of the vehicle mass estimate is evaluated to be below the uncertainty threshold.

4. The method of claim 2 wherein the vehicle mass estimate is repeatedly determined after the uncertainty of the vehicle mass estimate is evaluated to be below the uncertainty threshold.

5. The method of claim 4 wherein the repeated determination of the vehicle mass estimate is based in part upon the road grade estimate.

6. The method of claim 1 wherein the corrected prediction provided as the road grade estimate is combined with at least one additional estimate and the combined estimate is provided as an input for subsequent recursive determination of the road grade estimate.

7. The method of claim 6 wherein the at least one additional estimate comprises at least one of an estimate based upon information of an sensor, an estimate based upon a local prediction which fits road grade data to a predetermined curve, and road grade information based at least in part upon information received from an ITS system.

8. The method of claim 7 wherein the sensor is an inclinometer.

9. The method of claim 6 the wherein the corrected prediction provided as the road grade estimate is combined with at an estimate based upon information of a sensor and an estimate based upon a local prediction which fits road grade data to a predetermined curve

10. The method of claim 1 wherein the determining with the controller the road grade estimate utilizes an extended Kalman filter.

11. A system comprising:

a vehicle system including an engine structured to propel the vehicle; and

a controller structured to receive input signals from the vehicle system and provide output signals to control the vehicle system, the controller being structured to;

repeatedly determine a vehicle mass estimate and an uncertainty of the vehicle mass estimate during operation of the vehicle system,

when the uncertainty of the vehicle mass estimate is below an uncertainty threshold determine a road grade estimate by operating a digital filter to repeatedly predict a road grade using the vehicle mass estimate and a model of the vehicle system, correct the prediction using information of actual operation of the vehicle system, and set the corrected prediction as the road grade estimate, and

control at least one of a vehicle speed and an engine output using the road grade estimate.

12. The system of claim 11 wherein the controller is structured to stop the repeated determination of the vehicle mass estimate and the uncertainty of the vehicle mass estimate once the uncertainty of the vehicle mass estimate is below the uncertainty threshold until a re-start condition is satisfied.

13. The system of claim 11 wherein the digital filter is structured to dynamically change a first weighting factor for the prediction and a second weighting factor for the for information of actual operation.

14. The system of claim 13 wherein the digital filter is structured to dynamically change the first weighting factor and the second weighting factor based upon a schedule that decreases a relative weight of the second weighting factor to the first weighting factor as a function of the duration of operation of the filter.

15. The system of claim 13 wherein the digital filter is structured to dynamically change the first weighting factor and the second weighting factor based upon a set of criteria empirically determined to indicate the reliability of at least one of the prediction and the information of actual operation.

16. A method comprising:

operating an electronic control system to determine a vehicle mass estimate during operation of the vehicle using a first Kalman filter;

operating the electronic control system to determine whether an error associated with the vehicle mass estimate satisfies a first criterion;

if the first criterion is satisfied, operating the electronic control system to determine a road grade estimate using a second Kalman filter, the second Kalman filter utilizing the vehicle mass estimate in determining the road grade estimate; and

operating the electronic control system to set at least one of a vehicle speed limit and an engine output limit utilizing the road grade estimate.

17. The method of claim 16 wherein the second Kalman filter dynamically varies a relative weighting of a prediction associated with road grade and a measurement input to the second Kalman filter associated with road grade.

18. The method of claim 17 wherein the second Kalman filter dynamically varies the relative weighting based upon empirically determined criteria associated with the reliability of the prediction, the measurement input, or both the prediction and the measurement input.

19. The method of claim 16 wherein the road grade estimate generated by the second Kalman filter is one of combined with and replaced by at least one additional indication of road grade to provide a combined or replaced estimate which is utilized by subsequent iterations of the road grade estimate generated by the Kalman filter, said additional indication comprising at least one of:

information of an sensor, an estimate based upon a local prediction which fits road grade data to a predetermined curve, and road grade information based at least in part upon information received from an ITS system.

20. The method of claim 16 wherein the first Kalman filter and the second Kalman filter are structured as extended Kalman filters.

21. A method of controlling a vehicle system including an engine and a controller, the method comprising:

operating the vehicle system to propel the vehicle;

determining with the controller during operation of the vehicle system a vehicle mass estimate and an uncertainty of the vehicle mass estimate, evaluating with the controller the uncertainty of the vehicle mass estimate relative to at least one criterion, and if the uncertainty of the vehicle mass estimate satisfies the criterion, setting a value of the vehicle mass estimate and interrupting the determination of the vehicle mass estimate and the uncertainty of the vehicle mass estimate;

determining with the controller during operation of the vehicle system a road grade estimate by recursively predicting a road grade based upon a model of the vehicle system, correcting the prediction based upon a measurement of operation of the vehicle system, and setting the corrected prediction as the road grade estimate; and

controlling with the controller utilizing the road grade estimate at least one of a vehicle speed and an engine output.

22. The method of claim 21 wherein the criterion comprises an uncertainty threshold and the acts of determining with the controller the vehicle mass estimate and the uncertainty of the vehicle mass estimate and evaluating with the controller the uncertainty of the vehicle mass estimate relative to the criterion are repeated until the uncertainty of the vehicle mass estimate is below the uncertainty threshold.

23. The method of claim 21 wherein the determining with the controller during operation of the vehicle system a road grade estimate is suspended if at least one of a vehicle acceleration condition and a vehicle braking condition is satisfied.

24. The method of claim 23 wherein the vehicle acceleration condition comprises the vehicle acceleration being below a minimum threshold or above a maximum threshold

25. The method of claim 23 wherein the vehicle braking condition comprises the vehicle brake being actuated.

26. The method of claim 21 wherein the determining with the controller during operation of the vehicle system a vehicle mass estimate and an uncertainty of the vehicle mass estimate and the determining with the controller during operation of the vehicle system a road grade estimate are performed concurrently and independently of one another.

27. The method of claim 21 wherein the act of correcting the prediction is based on at least one additional indication of road grade, said additional indication of road grade being based upon one or more of:

information from a sensor, an estimate based upon a local prediction which fits road grade data to a predetermined curve, and road grade information based at least in part upon information received from an ITS system.