Battery capacity estimation using state of charge initialization-on-the-fly concept

• US 9,539,912 B2
• Filed: 02/20/2014
• Issued: 01/10/2017
• Est. Priority Date: 02/20/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Li, Yonghua
• Primary Examiner(s): Jos, Basil T
• Application Number:
  US14/184,857
• Publication Number:
  US 20150231986A1
• Time in Patent Office: 1,055 Days
• US Class Current: 1/1
• CPC Class Codes: B60L 2210/10 DC to DC converters
• CPC Class Codes: B60L 2210/30 AC to DC converters
• CPC Class Codes: B60L 2210/40 DC to AC converters
• 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 2240/80 Time limits
• CPC Class Codes: B60L 2260/44 by parameter estimation
• 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 50/61 by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles
• CPC Class Codes: B60L 50/66 Arrangements of batteries
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60L 58/13 Maintaining the SoC within a determined range
• CPC Class Codes: B60L 58/20 having different nominal voltages
• CPC Class Codes: B60L 58/22 Balancing the charge of battery modules
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/3828 using current integration
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• 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: Y10S 903/903 : having energy storing means, e.g. battery, capacitor

Abstract

Hybrid-electric and pure electric vehicles include a traction battery comprised of many cells. Controlling a battery system may require that a battery state of charge and a battery capacity be known. The state of charge and battery capacity values may be derived from estimated traction battery model parameters. The accuracy of the estimated model parameters depends on the signal richness and convergence properties of the estimation scheme. Accurate model parameters may be estimated when a persistent excitation condition and an estimation convergence condition are met. If the conditions are not met, active excitation of the battery may be performed to improve the chances of meeting the conditions.

Claims

1. A battery control system for a vehicle comprising:

a traction battery including a plurality of cells; and

at least one controller programmed to generate open-loop and closed-loop model parameter estimates for the traction battery and, in response to detecting that a persistent excitation condition and an estimation convergence condition are satisfied, change from operating the traction battery according to an open-loop estimate derived state of charge to a closed-loop estimate derived state of charge.

2. The battery control system of claim 1 wherein the persistent excitation condition is satisfied when

3. The battery control system of claim 1 wherein the estimation convergence condition is satisfied when an error magnitude between at least one of the model parameter estimates and a corresponding measured model parameter value is less than a predetermined threshold for a predetermined period.

4. The battery control system of claim 1 wherein the at least one controller is further programmed to, in response to at least one of the persistent excitation condition and the estimation convergence condition not being satisfied, vary a battery power demand to cause a predetermined number of frequency component amplitudes of the battery power demand to exceed a predetermined magnitude without affecting acceleration of the vehicle.

5. The battery control system of claim 1 wherein the at least one controller is further programmed to operate the traction battery according to a battery capacity derived from a first state of charge and a second state of charge, wherein the second state of charge is evaluated after detecting at least a predetermined amount of current throughput from when the first state of charge was evaluated.

6. The battery control system of claim 5 wherein the first state of charge and the second state of charge are evaluated within a common ignition cycle.

7. The battery control system of claim 5 wherein the first state of charge and the second state of charge are evaluated when a battery temperature is above a predetermined temperature.

8. The battery control system of claim 5 wherein the at least one controller is further programmed to schedule the first state of charge and the second state of charge to be evaluated within a predetermined time window.

9. The battery control system of claim 8 wherein the at least one controller is further programmed to schedule the predetermined time window such that a time between successive predetermined time windows increases as an age of the traction battery increases.

10. The battery control system of claim 8 wherein the at least one controller is further programmed to, in response to at least one of the persistent excitation condition and the estimation convergence condition not being satisfied within the predetermined time window, cause a predetermined number of frequency component amplitudes of battery power demand to exceed a predetermined magnitude without affecting acceleration of the vehicle.

11. A vehicle comprising:

a traction battery including a plurality of cells; and

at least one controller programmed to generate model parameter estimates for the traction battery and, in response to a persistent excitation condition not being satisfied, vary a battery power demand to cause a predetermined number of frequency component amplitudes of the battery power demand to exceed a predetermined magnitude without affecting acceleration of the vehicle.

12. The vehicle of claim 11 wherein the persistent excitation condition is not satisfied when

13. The vehicle of claim 11 wherein the at least one controller is further programmed to, in response to an estimation convergence condition not being satisfied, vary the battery power demand to cause a predetermined number of frequency component amplitudes of the battery power demand to exceed a predetermined magnitude without affecting acceleration of the vehicle.

14. The vehicle of claim 13 wherein the estimation convergence condition is not satisfied when an error magnitude between at least one of the model parameter estimates and a corresponding measured model parameter value is greater than a predetermined threshold.

15. A method of operating a traction battery comprising:

scheduling a time window in which to learn a battery capacity;

in response to a persistent excitation condition and an estimation convergence condition being satisfied during the window, learning a first state of charge value and, after the battery experiences a predetermined amount of current throughput, learning a second state of charge value; and

operating the traction battery according to the battery capacity derived from the values.

16. The method of claim 15 wherein the persistent excitation condition is satisfied when

17. The method of claim 15 wherein the estimation convergence condition is satisfied when an error magnitude between an estimated model parameter and a corresponding measured model parameter value is less than a predetermined threshold for a predetermined period.

18. The method of claim 15 further comprising causing, in response to at least one of the persistent excitation condition and the estimation convergence condition not being satisfied within the time window, a predetermined number of frequency component amplitudes of battery power demand to exceed a predetermined magnitude without affecting vehicle acceleration.

19. The method of claim 15 wherein the first and second state of charge values are learned when a battery temperature is above a predetermined temperature.

20. The method of claim 15 wherein the time window is scheduled such that the time between successive time windows increases as an age of the traction battery increases.