REAL-TIME FUEL CONSUMPTION ESTIMATION

• US 20150046000A1
• Filed: 08/06/2013
• Published: 02/12/2015
• Est. Priority Date: 08/06/2013
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Zhao, Yanan, Kuang, Ming Lang, Phillips, Anthony Mark
• Granted Patent: US 9,193,351 B2
• Time in Patent Office: Days
• US Class Current: 701/22
• CPC Class Codes: B60K 6/445 Differential gearing distribution type
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 20/12 using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60W 20/15 Control strategies specially adapted for achieving a particular effect
• CPC Class Codes: B60Y 2300/52 Engine fuel consumption
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y10S 903/93 Conjoint control of different elements

Abstract

An engine, electric machine and battery of a vehicle are operated, in certain examples, such that a state of charge of the battery is generally maintained while the real-time fuel consumption is estimated and minimized by the use of route segmentation and a predictive average power calculation.

Claims

1. A vehicle comprising:

an engine;

an electric machine; and

at least one controller programmed to, for each of a plurality of segments defining a route for the vehicle, operate the engine and electric machine according to a selected battery state of charge setpoint associated with a predicted fuel consumption of the engine that is based on a predicted average wheel power demand and potential average battery power demand for the segment.

2. The vehicle of claim 1, wherein the predicted fuel consumption is a minimum of a plurality of predicted fuel consumptions of the engine for the segment.

3. The vehicle of claim 2, wherein each of the plurality of predicted fuel consumptions is associated with a particular combination of engine power and electric machine power sufficient to satisfy predicted driver demand for the segment.

4. The vehicle of claim 2, wherein each of the plurality of predicted fuel consumptions is associated with a predicted engine speed derived from an energy management strategy in energy management optimization and the energy management strategy is used in vehicle control to derive a current engine speed.

5. The vehicle of claim 1, wherein the predicted average wheel power demand is based on a grade profile of the segment.

6. The vehicle of claim 1, wherein the predicted average wheel power demand is based on a predicted vehicle speed for the segment.

7. The vehicle of claim 1 further comprising a battery, wherein the potential average battery power demand is based on a state of charge of the battery at a beginning of the segment.

8. The vehicle of claim 1 further comprising a battery, wherein the potential average battery power demand is based on a state of charge of the battery at an end of the segment.

9. The vehicle of claim 1, wherein the segments are defined such that each has a substantially constant road grade, substantially constant vehicle acceleration and a powertrain operating mode.

10. A method for operating a hybrid electric powertrain comprising:

for each of a plurality of segments defining a route,

generating a plurality of predicted fuel consumption values for combinations of engine power and electric machine power sufficient to satisfy a predicted total power demand, wherein the predicted total power demand is based on a predicted average wheel power demand and potential battery power demand for the segment,

selecting a battery state of charge setpoint associated with one of the predicted fuel consumption values, and

operating an electric motor, an electric generator and an engine according to the battery state of charge setpoint to reduce fuel consumed by the engine for the route.

11. The method of claim 10, wherein the one of the predicted fuel consumption values is a minimum of the predicted fuel consumption values.

12. The method of claim 10, wherein the predicted average wheel power demand is based on a grade profile of the segment.

13. The method of claim 10, wherein the predicted average wheel power demand is based on a predicted vehicle speed for the segment.

14. The method of claim 10, wherein the potential battery power demand is based on a state of charge of a battery at a beginning of the segment.

15. The method of claim 10, wherein the potential battery power demand is based on a state of charge of a battery at an end of the segment.

16. The method of claim 10, wherein the segments are defined such that each has a substantially constant road grade, substantially constant vehicle acceleration and a powertrain operating mode.

17. A vehicle comprising:

an engine;

an electric machine; and

at least one controller programmed to, for each of a plurality of segments defining a route for the vehicle, operate the engine and electric machine according to a selected battery state of charge setpoint associated with a minimum of a plurality of predicted fuel consumption values, wherein each of the predicted fuel consumption values is associated with a particular combination of engine power and electric machine power sufficient to satisfy a predicted total power demand and wherein the predicted total power demand is based on a predicted average wheel power demand and predicted average battery power demand for the segment.

18. The vehicle of claim 17, wherein the predicted average wheel power demand is based on a grade profile of the segment.

19. The vehicle of claim 17, wherein the predicted average wheel power demand is based on a predicted vehicle speed for the segment.

20. The vehicle of claim 17 further comprising a battery, wherein the predicted average battery power demand is based on a state of charge of the battery at a beginning of the segment and a state of charge of the battery at an end of the segment.