Vehicle acceleration determination

• US 9,738,284 B2
• Filed: 12/08/2015
• Issued: 08/22/2017
• Est. Priority Date: 12/08/2015
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Pallett, Tobias John, Lin, Hui, Lauffer, Scott J., Elwart, Shane, Williams, Wayne
• Primary Examiner(s): NGUYEN, TAN QUANG
• Application Number:
  US14/962,248
• Publication Number:
  US 20170158199A1
• Time in Patent Office: 623 Days
• Field of Search: 701 93, 701 96, 701 44, 701532, 340439
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2510/0676 Engine temperature
• CPC Class Codes: B60W 2510/244 Charge state
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/16 Pitch
• CPC Class Codes: B60W 2520/30 Wheel torque
• CPC Class Codes: B60W 2520/40 Torque distribution
• CPC Class Codes: B60W 2530/10 Weight
• CPC Class Codes: B60W 2530/20 Tyre data
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2555/40 Altitude
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 2720/103 Speed profile
• CPC Class Codes: B60W 2720/106 Longitudinal acceleration
• CPC Class Codes: B60W 30/18009 related to particular drive situations
• CPC Class Codes: B60W 30/18145 : Cornering
• CPC Class Codes: B60W 30/18163 : Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 30/188 : Controlling power parameters of the driveline, e.g. determining the required power
• CPC Class Codes: B60W 40/107 : Longitudinal acceleration

Abstract

A system of one or more computers having a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to identify a current state of a vehicle and determine a maximum acceleration capability of the vehicle. The system also determines a desired acceleration profile to follow based at least in part the maximum acceleration capability and controls an acceleration of the vehicle based at least in part the desired acceleration profile.

Claims

1. A system, comprising a computer having a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to:

identify a current state of a vehicle;

determine a maximum acceleration capability of the vehicle;

determine a desired acceleration profile to follow based at least in part the maximum acceleration capability; and

control an acceleration of the vehicle based at least in part the desired acceleration profile.

2. The system of claim 1, wherein the current state of the vehicle includes at least one of an internal vehicle state and an environmental condition.

3. The system of claim 1, wherein the maximum acceleration capability is determined at least in part from one of a predicted set of torque response values, a vehicle mass, a road grade, a road surface mu, a pitch angle of the vehicle and a wheel radius.

4. The system of claim 1, wherein the current state is at least one of a current vehicle speed, a current battery state of charge, a current wheel torque value, a torque balance distribution, a barometric pressure, a componentry status, a controller area network (CAN) bus status, a power supply level, an engine temperature, a fuel volatility rating, a fuel octane rating, a fuel additive, a humidity level, a combustion chamber deposit value, an altitude value and an engine age and a tire saturation value.

5. The system of claim 3, wherein the predicted set of torque response values is based at least in part on the current state of the vehicle, a first set of torque values and a second set of torque values.

6. The system of claim 5, wherein the computer is further programmed to identify the first set of torque values from a transient region of the predicted set of torque response values.

7. The system of claim 5, wherein the computer is further programmed to identify the second set of torque values from a steady state region of the predicted set of torque response values.

8. The system of claim 1, wherein the computer is further programmed to determine the desired acceleration profile to follow from at least one of a map stored in the memory, a desired route to follow, the maximum acceleration capability of the vehicle, a communication from a second vehicle, a communication from an external road infrastructure system and an obstacle detection sensor.

9. The system of claim 8, wherein the obstacle detection sensor includes at least one of a LIDAR sensor, a SONAR sensor and an optical range determination system.

10. The system of claim 1, wherein the computer is further programmed to select an at least one discrete value of the maximum acceleration capability;

send the at least one discrete value to a second computer; and

regenerate the maximum acceleration capability by interpolating the at least one discrete value of the maximum acceleration capability.

11. A method, comprising:

identifying a current state of a vehicle;

determining a maximum acceleration capability of the vehicle;

determining a desired acceleration profile to follow based at least in part the maximum acceleration capability; and

controlling an acceleration of the vehicle based at least in part the desired acceleration profile.

12. The method of claim 11, wherein the current state of the vehicle includes at least one of an internal vehicle state and an environmental condition.

13. The method of claim 11, wherein the maximum acceleration capability is determined at least in part from one of a predicted set of torque response values, a vehicle mass, a road grade, a road surface mu, a pitch angle of the vehicle and a wheel radius.

14. The method of claim 11, wherein the current state is at least one of a current vehicle speed, a current battery state of charge, a current wheel torque value, a torque balance distribution, a barometric pressure, a componentry status, a controller area network (CAN) bus status, a power supply level, an engine temperature, a fuel volatility rating, a fuel octane rating, a fuel additive, a humidity level, a combustion chamber deposit value, an altitude value and an engine age and a tire saturation value.

15. The method of claim 13, wherein the predicted set of torque response values is based at least in part on the current state of the vehicle, a first set of torque values and a second set of torque values.

16. The method of claim 15, further comprising identifying the first set of torque values from a transient region of the predicted set of torque response values.

17. The method of claim 15, further comprising identifying the second set of torque values from a steady state region of the predicted set of torque response values.

18. The method of claim 11, further comprising determining the desired acceleration profile to follow from at least in part from one of a map stored in a memory, a desired route to follow, the maximum acceleration capability of the vehicle, a communication from a second vehicle, a communication from an external road infrastructure system and an obstacle detection sensor.

19. The method of claim 18, wherein the obstacle detection sensor includes at least one of a LIDAR sensor, a SONAR sensor and an optical range determination system.