Distance Until Charge Prediction for Vehicles

• US 20160082857A1
• Filed: 09/23/2014
• Published: 03/24/2016
• Est. Priority Date: 09/23/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Kristinsson, Johannes Geir, Naghshtabrizi, Payam
• Granted Patent: US 10,118,499 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 15/20 for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
• CPC Class Codes: B60L 2240/60 Navigation input
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2250/12 by confirmation, e.g. of the input
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 2260/52 drive range estimation, e.g. of estimation of available travel distance
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/15 with additional electric power supply with capacitors charged by engine-driven generators B60L50/40; with batteries charged by engine-driven generators B60L50/61
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• 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 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• 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
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles

Abstract

A vehicle includes a traction battery and a controller programmed to, in response to a first ignition cycle after a charging of the traction battery, output a distance until charge prediction based on one or more selected distance estimates from filtered sets of historical data that are derived by filtering historical data based on a time and a day of the first ignition cycle. The selected distance estimates may be from filtered sets having more than a predetermined number of members. The selected distance estimates may be from filtered sets having a variance that is less than an overall historic variance. The filtered sets may include members of an unfiltered set based on a time of day and day of week.

Claims

1. A vehicle comprising:

a traction battery; and

at least one controller programmed to, in response to a first ignition cycle after a charging of the traction battery, output a distance until charge prediction that is based a plurality of selected distance estimates from filtered sets of historical data having a variance less than a threshold and derived by filtering historical data based on a time and a day of the first ignition cycle.

2. The vehicle of claim 1 wherein the at least one controller is further programmed to select distance estimates from filtered sets of historical data that have more than a predetermined number of members.

3. The vehicle of claim 1 wherein the at least one controller is further programmed to select distance estimates from filtered sets that have a variance that is less than or equal to an overall historical data variance for each of the filtered sets.

4. The vehicle of claim 1 wherein the distance until charge prediction is an average of the selected distance estimates.

5. The vehicle of claim 1 wherein at least one of the filtered sets includes the historical data associated with a stored time of day that is within a predetermined time range that includes the time of the first ignition cycle.

6. The vehicle of claim 1 wherein at least one of the filtered sets includes the historical data associated with a stored day of week that matches the day of the first ignition cycle.

7. The vehicle of claim 1 wherein at least one of the filtered sets includes the historical data associated with a stored day type that matches a day type of the day of the first ignition cycle.

8. The vehicle of claim 1 wherein at least one of the filtered sets includes the historical data associated with a stored day of week that matches the day of the first ignition cycle and associated with a stored time of day that is within a predetermined time range that includes the time of the first ignition cycle.

9. The vehicle of claim 1 wherein the distance until charge prediction is further based on a recursive least squares estimation for each of the filtered sets.

10. An energy management system for a vehicle comprising:

at least one controller programmed to, in response to a first ignition cycle after a charging event, output a distance until charge prediction based on an average of selected distance estimates from a plurality of filtered sets of historical data, wherein the filtered sets are derived from an unfiltered set according to a day of week and a time of day of the first ignition cycle.

11. The energy management system of claim 10 wherein the unfiltered set includes a stored time of day, a stored day of week, and corresponding distance between charge values from previous operation of the vehicle.

12. The energy management system of claim 10 wherein the at least one controller is further programmed to select distance estimates from the filtered sets of historical data that have more than a predetermined number of members.

13. The energy management system of claim 10 wherein the at least one controller is further programmed to select distance estimates from the filtered sets of historical data that have a variance that is less than a variance of the unfiltered set.

14. The energy management system of claim 10 wherein the at least one controller is further programmed to generate the selected distance estimates from the filtered sets of historical data based on a recursive least squares estimation for each of the filtered sets.

15. The energy management system of claim 10 wherein at least one of the filtered sets includes those members of the unfiltered set associated with a stored time of day that is within a predetermined amount of the time of day.

16. The energy management system of claim 10 wherein at least one of the filtered sets includes those members of the unfiltered set associated with a stored day of week that matches the day of week.

17. A method comprising:

selecting, by a controller, distance estimates from filtered sets of historical data based on a variance of each of the filtered sets; and

operating, by the controller, a hybrid vehicle powertrain according to a battery operating mode selected according to a distance until charge prediction that is based on an average of the distance estimates, and a day and a time of a first ignition cycle after charging a traction battery.

18. The method of claim 17 further comprising selecting, by the controller, estimates from filtered sets of historical data further based on a number of members in each of the filtered sets.

19. The method of claim 17 wherein the filtered sets include selected members of an unfiltered set that includes a stored time of day, a stored day of week, and an associated distance between charge values from previous vehicle operation.

20. The method of claim 19 wherein the filtered sets are derived from the unfiltered set based on the day and the time of the first ignition cycle.