System and method for estimating cutoff duration of a vehicle

• US 10,442,440 B2
• Filed: 06/01/2017
• Issued: 10/15/2019
• Est. Priority Date: 06/01/2017
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Tricarico, Davide
• Primary Examiner(s): Trivedi, Atul
• Application Number:
  US15/611,245
• Publication Number:
  US 20180345986A1
• Time in Patent Office: 866 Days
• Field of Search: 701 99
• CPC Class Codes: B60W 2030/18081 With torque flow from driveshaft to engine, i.e. engine being driven by vehicle
• CPC Class Codes: B60W 2050/0031 Mathematical model of the vehicle
• CPC Class Codes: B60W 2050/0088 Adaptive recalibration
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2510/1005 Transmission ratio engaged
• CPC Class Codes: B60W 2510/182 Brake pressure, e.g. of fluid or between pad and disc
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2540/12 Brake pedal position
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2554/801 Lateral distance
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/18136 Engine braking
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: B60W 40/12 related to parameters of the vehicle itself , e.g. tyre models
• CPC Class Codes: B60W 50/04 Monitoring the functioning of the control system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

In accordance with an exemplary embodiment, a cutoff duration estimation system for a vehicle includes a vehicle model module having a vehicle profile model of the vehicle, a front vehicle detection module having a front vehicle deceleration model operable to determine a rate of close between the vehicle and a front vehicle, and a cutoff duration estimation system operable to determine an amount of time an engine of the vehicle will be operating in an overrun condition based on the vehicle profile model and the front vehicle deceleration model.

Claims

1. A cutoff duration estimation system for a vehicle comprising:

a vehicle model module including a vehicle profile model of the vehicle;

a front vehicle detection module including a front vehicle deceleration model operable to determine a rate of close between the vehicle and a front vehicle;

a cutoff duration estimation system operable to determine an amount of time an engine of the vehicle will be operating in an overrun condition based on the vehicle profile model and the front vehicle deceleration model; and

an event selection and run module operable to select and run one of a diagnostic event and a learning event during the amount of time the engine of the vehicle is operating in the overrun condition.

2. The cutoff duration estimation system according to claim 1, wherein the vehicle model module includes a brake pedal model module operable to determine an estimated deceleration of the vehicle based a braking requirement to bring the vehicle to a stop.

3. The cutoff duration estimation system according to claim 2, wherein the vehicle model module includes a vehicle friction model module operable to estimate a rate of deceleration based on at least one of a road characteristic and an operating gear of the vehicle.

4. The cutoff duration estimation system according to claim 3, wherein the vehicle model module includes a vehicle deceleration model module operable to determine a predicted rate of deceleration of the vehicle based on inputs from the brake pedal model module and the vehicle friction model module.

5. The cutoff duration estimation system according to claim 1, further comprising:

a front vehicle sensing system operable to determine a distance between the vehicle and the front vehicle.

6. The cutoff duration estimation system according to claim 5, wherein the front vehicle sensing system includes at least one of a sonar system and a light detection and ranging (LIDAR) sensor.

7. The cutoff duration estimation system according to claim 1, further comprising:

a street model module for determining environmental conditions at the vehicle including at least one of a traffic condition, a position condition, a route condition, and a weather condition, a cutoff time estimation module operable to determine an amount of time an engine of the vehicle will be operating in an overrun condition based on the environmental condition.

8. A method of estimating a cutoff duration for a vehicle comprising:

sensing an overrun condition;

detecting a condition of the vehicle based on a vehicle profile model determined by a vehicle model module;

determining a change in distance between the vehicle and a front vehicle;

estimating a cutoff duration of the overrun condition based on the vehicle profile model and the change in distance between the vehicle and the front vehicle; and

activating one of a diagnostic test and a learning operation for the cutoff duration.

9. The method of claim 8, wherein detecting the condition of the vehicle includes detecting a friction condition including at least one of a vehicle gear and a road condition.

10. The method of claim 9, further comprising:

determining a change in distance between the vehicle and the front vehicle.

11. The method of claim 10, wherein detecting the change in distance between the vehicle and the front vehicle includes actively sensing the front vehicle with at least one of a sonar system and a light detection and ranging (LIDAR) system.

12. The method of claim 10, wherein detecting the condition of the vehicle includes determining an estimated brake application pressure based on the change in distance between the vehicle and the front vehicle.

13. The method of claim 12, further comprising:

determining an estimated rate of deceleration based on at least one of the estimated brake application pressure and the friction condition.

14. The method of claim 8, wherein activating the one of a diagnostic test, and a learning operation includes selecting the one of the diagnostic test, and the learning operation that has a completion time less than the cutoff duration.