Electric vehicle operation to manage battery capacity

• US 10,026,998 B2
• Filed: 05/15/2014
• Issued: 07/17/2018
• Est. Priority Date: 05/15/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Li, Yonghua, Tseng, Fling, Ho, Szushen
• Primary Examiner(s): PELTON, NATHANIEL R
• Application Number:
  US14/278,011
• Publication Number:
  US 20150329003A1
• Time in Patent Office: 1,524 Days
• Field of Search: 320132, 320134
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/547 Voltage
• CPC Class Codes: B60L 2240/549 Current
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2260/46 by self learning
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/16 with provision for separate direct mechanical propulsion
• CPC Class Codes: B60L 53/66 Data transfer between charging stations and vehicles
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: H01M 10/44 Methods for charging or discharging circuits for charging H02J7/00
• CPC Class Codes: H01M 2220/20 Batteries in motive systems, e.g. vehicle, ship, plane
• CPC Class Codes: Y02E 60/10 Energy storage using batteries
• 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 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles

Abstract

Battery state of charge control systems and methods provide techniques to assist in managing battery capacity and battery life under various environmental, operating, and storage conditions. Dynamic management of desired battery SOC based on one or more parameters associated with usage patterns and environmental conditions, e.g., charge/discharge patterns, state of charge and/or temperature during storage may extend useful battery life. The present techniques can provide active state of charge monitoring and control to manage battery capacity during storage or other periods of non-use over the battery life to enhance battery performance.

Claims

1. A method for controlling an electric vehicle having a battery to propel the vehicle to dynamically manage battery capacity, comprising:

charging and discharging the battery to a battery target state of charge (SOC), the target SOC varying in response to at least a scheduled or predicted vehicle storage time and ambient temperature during scheduled or predicted vehicle storage time with actual SOC being a function of ambient temperature.

2. The method of claim 1, further comprising storing a state of charge profile which includes the target SOC as a function of at least one of the ambient temperature, projected energy usage, and storage time.

3. The method of claim 1, further comprising storing a predicted weekday driving pattern energy usage, determining a predictive matrix of vehicle use, and modifying the target SOC in response to a current weekday.

4. The method of claim 1, further comprising charging the battery from a storage target SOC, which is dependent on the ambient temperature during the vehicle storage time, to a use SOC based on a battery charging start time such that the battery is charged to the target SOC within a designated time period before scheduled operation of the vehicle.

5. The method of claim 1, further comprising discharging the battery to an electrical grid to reduce a current SOC to the target SOC to reduce battery capacity degradation while a vehicle is a non-use period.

6. The method of claim 1, further comprising partitioning battery energy usage into a plurality of time based patterns.

7. The method of claim 1, wherein the target SOC for a traction battery during vehicle storage is at least 90% when ambient temperature is less than 0°

C.

8. The method of claim 1, wherein the target SOC varies in response to a predicted average daily temperature during the scheduled or predicted storage time.

9. A method, comprising:

storing vehicle usage patterns including at least a first vehicle usage pattern with long-term storage; and

charging or discharging a vehicle battery based on a desired SOC from a stored state of charge (SOC) profile relating SOC to capacity of the vehicle batter, temperature for the first vehicle usage pattern, and sensed ambient temperature during vehicle storage, with the SOC being at least 90% when ambient temperature is less than 0°

C.

10. The method of claim 9, wherein the desired SOC includes a default SOC for a second vehicle usage pattern that includes an extended long-term storage.

11. The method of claim 9, wherein storing vehicle usage patterns includes storing a long-term storage pattern, and determining when a long-term storage will occur, and the desired SOC corresponds to a charge-sustaining point in the SOC profile.

12. The method of claim 9, wherein charging or discharging comprises discharging energy back to a utility grid to achieve the desired SOC, the desired SOC based on enough charge to start an engine.

13. The method of claim 9, further comprising sending a command to increase the desired SOC from a desired SOC associated with long term storage and charge the battery within a designated period before a predicted or scheduled end of storage.

14. A vehicle comprising:

a battery to power an electric motor to propel the vehicle, wherein the battery includes a state of charge (SOC);

a memory storing a battery temperature, projected energy usage and storage time; and

a processor operatively coupled to the memory, the processor controlling charging or discharging of the battery to an SOC storage point for the battery using the battery temperature, projected energy usage, ambient temperature during vehicle storage, and storage time to manage battery capacity during storage.

15. The vehicle of claim 14, wherein the memory comprises a state of charge profile that includes a relationship between SOC, storage time, and battery capacity.

16. The vehicle of claim 15, wherein the memory is updated through an external computing device after the battery is deployed, and wherein the processor is configured to generate a display of a similarity matrix showing probability of actual vehicle use matching predicted vehicle use including predicted storage time.

17. The vehicle of claim 15, wherein the processor is programmed to receive the projected energy usage that includes a predicted weekday driving pattern energy usage and to modify the SOC storage point based on predicted future use and storage cycles.

18. The vehicle of claim 17, wherein the processor is to determine a charging start time to achieve a target SOC different from the SOC storage point for vehicle use before predicted or scheduled operation of the vehicle.

19. The vehicle of claim 15, wherein the processor is to determine a feedback charge amount to send to an electrical utility grid from the battery to achieve the SOC storage point and to avoid a battery depletion zone.

20. The vehicle of claim 14, wherein the processor is to partition the energy usage into a plurality of time based patterns and is to set the SOC storage point to at least 90% of maximum SOC when the battery temperature is less than 0°

C.