Apparatus and Method for Rapidly Charging Batteries

• US 20120098481A1
• Filed: 10/21/2011
• Published: 04/26/2012
• Est. Priority Date: 10/22/2010
• Status: Active Grant

• Current Assignee: Indigo Technologies, Inc.
• Original Assignee: Nucleus Scientific, Incorporated
• Inventors: Hunter, Ian, Lafontaine, Serge R.
• Granted Patent: US 9,397,516 B2
• Time in Patent Office: Days
• US Class Current: 320/106
• CPC Class Codes: H02J 7/00712 the cycle being controlled or terminated in response to electric parameters
• CPC Class Codes: H02J 7/007194 of the battery
• CPC Class Codes: Y02B 40/00 Technologies aiming at improving the efficiency of home appliances, e.g. induction cooking or efficient technologies for refrigerators, freezers or dish washers

Abstract

An apparatus and methods for ultra-fast charging one or more batteries, including, for example, lithium ion batteries. A charging current is determined by optimization of a model based on functions of a set of internal state variables associated with a battery, and a set of model parameters or nonparametric data characterizing the battery. Instantaneous internal state variables are determined, and an optimized charging current is applied to the battery subject to a set of battery-specific constraints. Internal state variables are updated recursively based on behavior of the battery under charge as well as the behavior, stored in a database, or acquired via a network, of cognate batteries.

Claims

1. An apparatus for charging a battery, the apparatus comprising:

a. an interface for electrical coupling to the battery;

b. circuitry for concurrently measuring battery voltage and current;

c. a database for accumulating battery parameter data from a plurality of batteries; and

d. a processor for recursively updating data characterizing a model representing the battery based both on jointly measured battery voltage and current and accumulated parameter data received from the database; and

e. a current supply for supplying current for charging the battery as governed by the processor.

2. An apparatus according to claim 1, wherein the processor is adapted for updating a dynamic representation of cell dynamics for the battery based at least in part upon system identification.

3. An apparatus according to claim 1, wherein the processor is adapted for updating a dynamic representation of cell dynamics for the battery based at least in part upon nonlinear system identification.

4. An apparatus according to claim 1, wherein the circuitry for concurrently measuring battery voltage and current includes a four-port configuration.

5. An apparatus according to claim 1, wherein the interface for electrical coupling to the battery is adapted for coupling to reference electrodes of the battery.

6. An apparatus according to claim 1, wherein the processor further comprises an input for receiving a signal from a sensor sensitive to a characteristic of the battery.

7. An apparatus according to claim 1, further comprising a temperature controller for controlling a temperature of the battery.

21. A method according to claim 6, wherein the step of determining a charging profile for the battery based on the dynamic representation is performed during a course of charging the battery.

8. A method for charging a battery, the method comprising:

a. providing a dynamic representation of cell dynamics for the battery;

b. determining a charging profile for the battery based on the dynamic representation; and

c applying an optimized charging current to the battery based, at least in part, on the charging profile and on tracking total charge that has been stored in the battery.

9. A method according to claim 8, wherein applying the optimized current is also subject to a set of battery-specific constraints.

10. A method according to claim 9, further comprising deriving the set of battery-specific constraints based on an iterative sequence of charge/discharge cycles.

11. A method according to claim 8, wherein the charging profile is a charging current profile.

12. A method according to claim 8, further comprising tracking temperature of the battery, and wherein applying an optimized charging current to the battery is also based, at least in part, on the tracked temperature of the battery.

13. A method according to claim 8, wherein applying the optimized current is based at least in part on tracking instantaneous state variables for the battery.

14. A method according to claim 8, wherein tracking the total charge comprises determining at least one instantaneous internal state variable for the battery during charging the battery.

15. A method according to claim 8, wherein tracking the total charge comprises determining at least one instantaneous internal state variable for the battery during discharging the battery.

16. A method according to claim 8, wherein the dynamic representation of cell dynamics for the battery is derived by means of nonlinear system identification.

17. A method according to claim 8, wherein the dynamic representation of cell dynamics for the battery includes a set of internal state variables.

18. A method according to claim 8, the dynamic representation of cell dynamics for the battery includes a set of model parameters.

19. A method according to claim 8, wherein the dynamic representation of cell dynamics for the battery includes non-parametric data characterizing a model, at least in part.

20. A method according to claim 8, wherein the step of determining a charging profile for the battery based on the dynamic representation is performed during a course of discharging the battery.

22. A method according to claim 18, wherein the set of model parameters includes parameters derived from a database.

23. A method according to claim 18, the set of model parameters includes parameters derived from a database updated during the course of charging the battery.

24. A method according to claim 8, wherein the dynamic representation of cell dynamics for the battery includes a set of internal state variables specific to a particular individual cell.

25. A method according to claim 8, wherein the dynamic representation of cell dynamics for the battery includes a set of model parameters specific to a genus of cells.

26. A method according to claim 8, further comprising providing a dynamic representation based, at least in part, on charge/discharge cycles of a plurality of networked charging systems.

27. A network for deriving empirical battery model data, the network comprising:

a. a plurality of charging systems, each charging system including a processor and a power supply for supplying current to a separate battery; and

b. a server for receiving data from each of the plurality of processors and for returning updated data with respect to a dynamic representation of cell dynamics for incorporation by each of the plurality of charging systems.

28. A network according to claim 27, wherein the processor of at least one of the plurality of charging systems is adapted for updating a dynamic representation of cell dynamics for the battery based at least in part upon system identification.

29. A network according to claim 27, wherein the processor of at least one of the plurality of charging systems is adapted for updating a dynamic representation of cell dynamics for the battery based at least in part upon nonlinear system identification.