Distance to Empty Prediction with Long Term Distance Compensation

• US 20150369872A1
• Filed: 06/19/2014
• Published: 12/24/2015
• Est. Priority Date: 06/19/2014
• Status: Active Application

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Meyer, Jason, Sangameswaran, Sangeetha
• Application Number:
  US14/309,070
• Publication Number:
  US 20150369872A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 58/13 Maintaining the SoC within a determined range
• CPC Class Codes: G01R 31/3647 for determining the ability of a battery to perform a critical function, e.g. cranking
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

A vehicle is provided which may include an energy conversion device, an energy source to supply power to the energy conversion device, and at least one controller. The controller may be programmed to, in response to detecting one or more noise factors expected to affect propulsive energy consumption of the energy conversion device from vehicle start until the energy source is empty, output a distance to empty (DTE) based on a change in energy consumption rate due to the one or more noise factors and predicted to last at least until the energy source is empty. The controller may further include a DTE prediction architecture including a feed-forward energy consumption estimator, an energy consumption learning filter, and a DTE calculator. A method for estimating distance to empty for a vehicle is also provided which may output a DTE modified by a predicted change in energy consumption rate selected to include a compensation factor corresponding to and correcting for a noise factor.

Claims

1. A method for estimating distance to empty (DTE) for a vehicle comprising:

in response to detecting a noise factor expected to affect propulsive energy consumption from vehicle start to empty, outputting by a controller a DTE modified by a predicted change in energy consumption rate selected to include a compensation factor corresponding to and correcting for the noise factor.

2. The method of claim 1, wherein the DTE is based on a predicted energy consumption rate and an amount of energy available in an energy source of the vehicle.

3. The method of claim 2, wherein the predicted energy consumption rate is based on a historical nominal energy consumption rate and a current energy consumption rate.

4. The method of claim 3, wherein the noise factor is a change in air density that occurred between an end of a last drive cycle and a subsequent vehicle start.

5. The method of claim 3, wherein the noise factor is a change in position of a window or convertible top of the vehicle.

6. The method of claim 1 further comprising tracking a theoretical energy consumption rate by an energy consumption learning filter configured to remove effects of the noise factor to generate baseline operating conditions.

7. The method of claim 1, wherein the compensation factor is a predicted DTE range adjustment corresponding to an estimated effect of the noise factor projected forward to empty.

8. The method of claim 1, wherein the noise factor is detectable, predictable, and constant from vehicle start to empty.

9. A vehicle comprising:

an energy conversion device;

an energy source to supply power to the energy conversion device; and

at least one controller programmed to, in response to detecting one or more noise factors expected to affect propulsive energy consumption of the energy conversion device from vehicle start until the energy source is empty, output a distance to empty (DTE) based on a change in energy consumption rate due to the one or more noise factors and predicted to last at least until the energy source is empty.

10. The vehicle of claim 9, wherein the controller further comprises a DTE prediction architecture including a feed-forward energy consumption estimator, an energy consumption learning filter, and a DTE calculator.

11. The vehicle of claim 9, wherein the DTE is based on a predicted energy consumption rate and an amount of energy available in the energy source.

12. The vehicle of claim 11, wherein the predicted energy consumption rate is based on a historical nominal energy consumption rate and a current energy consumption rate.

13. The vehicle of claim 9, wherein the one or more noise factors include a change in air density that occurred between an end of a last drive cycle and a subsequent vehicle start.

14. The vehicle of claim 9, wherein the one or more noise factors include a change in position of a window or convertible top of the vehicle.

15. The vehicle of claim 9, wherein the energy source is a fuel tank or a traction battery.

16. A vehicle comprising:

one or more sensors configured to monitor vehicle components and a traction battery pack; and

a controller configured to receive input from the sensors, to detect one or more noise factors expected to affect propulsive energy consumption from vehicle start to empty based on the input, and to output a modified distance to empty (DTE) based on a change in energy consumption rate predicted to compensate for the one or more noise factors until empty.

17. The vehicle of claim 16, wherein the controller is further configured to update the change in energy consumption rate based on a change in the one or more noise factors.

18. The vehicle of claim 16, wherein the controller comprises a DTE prediction architecture including a feed-forward energy consumption estimator, an energy consumption learning filter, and a DTE calculator.

19. The vehicle of claim 16, wherein the energy consumption rate is based on a historical nominal energy consumption rate.

20. The vehicle of claim 19, wherein the energy consumption rate is further based on a current energy consumption rate.