BATTERY MANAGEMENT SYSTEM

• US 20100017155A1
• Filed: 04/20/2007
• Published: 01/21/2010
• Est. Priority Date: 12/06/2006
• Status: Abandoned Application

• Current Assignee: ITI Scotland Limited (Scottish Government)
• Original Assignee: ITI Scotland Limited (Scottish Government)
• Inventors: Nareid, Helge
• Application Number:
  US12/517,545
• Publication Number:
  US 20100017155A1
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: G01R 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health

Abstract

A battery management system includes a neural network, for estimating an amount of charge stored in a battery following a recharging operation. Then, an amount of charge drawn during a discharging operation is subtracted from this estimated amount of charge to derive an estimate for the state of charge of the battery at a present time. A parameter representing the state of health of the battery may be provided as an input to the neural network, and the parameter representing the state of health of the battery may be generated by a second neural network.

Claims

1. A method of estimating a state of charge of a battery, comprising:

using a first neural network to form an estimate of a remaining amount of charge following a previous recharging operation;

measuring an amount of charge drawn from the battery since the previous recharging operation;

forming an estimate of the state of charge from the estimated remaining amount of charge and the amount of charge drawn.

2. A method as claimed in claim 1, comprising:

supplying at least one parameter representing a state of health of the battery as an input to the first neural network.

3. A method as claimed in claim 2, wherein the parameter representing the state of health of the battery represents the effective capacity of the battery.

4. A method as claimed in claim 3, wherein the parameter representing the state of health of the battery further represents the internal resistance of the battery.

5. A method as claimed in claim 3, wherein the parameter representing the state of health of the battery further represents a time constant of an equivalent circuit of the battery.

6. A method as claimed in claim 2, further comprising using a second neural network to form an estimate of the at least one parameter representing the state of health of the battery.

7. A method as claimed in claim 2, further comprising forming an estimate of the at least one parameter representing the state of health of the battery based on a series of measurements relating to operating parameters of the battery.

8. A method as claimed in claim 1, wherein the step of using the first neural network to form the estimate of the remaining amount of charge following a previous recharging operation comprises:

using the first neural network to form an estimate of a remaining amount of charge following a previous recharging operation, as a percentage of an effective capacity of the battery.

9. A method as claimed in claim 8, wherein the step of using the first neural network to form the estimate of the remaining amount of charge following a previous recharging operation further comprises:

multiplying said estimate of the remaining amount of charge following the previous recharging operation, as a percentage of an effective capacity of the battery, by a value for said effective capacity of the battery.

10. A method as claimed in claim 9, comprising using a second neural network to form said value for said effective capacity of the battery.

11. A method as claimed in claim 1, wherein the previous recharging operation is a full recharging operation.

12. A method as claimed in claim 1, wherein the previous recharging operation is a partial recharging operation.

13. A method as claimed in claim 1, further comprising:

providing an estimate of an effective capacity of the battery as an input to the first neural network; and

forming said estimate of the effective capacity of the battery based on said estimate of the remaining amount of charge following a previous recharging operation.

14. A method as claimed in claim 1, further comprising:

using a second artificial neural network to form said estimate of the effective capacity of the battery, and

providing a time derivative of said estimate of the remaining amount of charge following a previous recharging operation as an input to said second artificial neural network.

15. A battery management system, for estimating a state of charge of a battery, comprising:

a first neural network configured for forming an estimate of a remaining amount of charge following a previous recharging operation;

a mechanism operable to measure an amount of charge drawn from the battery since the previous recharging operation; and

a mechanism operable to form an estimate of the state of charge from the estimated remaining amount of charge and the amount of charge drawn.

16. A battery management system as claimed in claim 15, further comprising a mechanism operable to supply at least one parameter representing a state of health of the battery as an input to the first neural network.

17. A battery management system as claimed in claim 16, wherein the parameter representing the state of health of the battery represents the effective capacity of the battery.

18. A battery management system as claimed in claim 17, wherein the parameter representing the state of health of the battery further represents the internal resistance of the battery.

19. A battery management system as claimed in claim 17, wherein the parameter representing the state of health of the battery further represents a time constant of an equivalent circuit of the battery.

20. A battery management system as claimed in claim 16, further comprising a second neural network configured for forming an estimate of the at least one parameter representing the state of health of the battery.

21. A battery management system as claimed in claim 15, wherein the first neural network has been trained to form an estimate of a remaining amount of charge following a previous recharging operation, as a percentage of an effective capacity of the battery.

22. A battery management system as claimed in claim 21, further comprising a multiplier configured for multiplying said estimate of the remaining amount of charge following the previous recharging operation, as a percentage of an effective capacity of the battery, by a value for said effective capacity of the battery.

23. A battery management system as claimed in claim 21, further comprising a second neural network configured for forming said value for said effective capacity of the battery.

24. A battery management system as claimed in claim 15, further comprising a fuel gauge.

25. A battery management system as claimed in claim 24, wherein the fuel gauge provides a numerical display of a parameter

26. A battery management system as claimed in claim 24, wherein the fuel gauge provides a warning when the battery is nearly discharged.

27. A battery management system as claimed in claim 24, wherein the fuel gauge includes an analog display showing remaining battery capacity.

28. (canceled)

29. An electrochemical cell system including a battery and a battery management system, wherein the battery management system comprises:

a first neural network configured for forming an estimate of a remaining amount of charge in the battery following a previous recharging operation;

a mechanism operable to measure an amount of charge drawn from the battery since the previous recharging operation; and

a mechanism operable to form an estimate of the state of charge from the estimated remaining amount of charge and the amount of charge drawn.

30. An electrochemical cell system as claimed in claim 29, wherein the battery management system further comprises a mechanism operable to supply at least one parameter representing a state of health of the battery as an input to the first neural network.

31. An electrochemical system as claimed in claim 30, wherein the parameter representing the state of health of the battery represents the effective capacity of the battery.

32. An electrochemical cell system as claimed in claim 31, wherein the parameter representing the state of health of the battery further represents the internal resistance of the battery.

33. An electrochemical cell system as claimed in claim 31, wherein the parameter representing the state of health of the battery further represents a time constant of an equivalent circuit of the battery.

34. An electrochemical cell system as claimed in claim 30, wherein the battery management system further comprises a second neural network configured for forming an estimate of the at least one parameter representing the state of health of the battery.

35. An electrochemical cell system as claimed in claim 29, wherein the first neural network has been trained to form an estimate of a remaining amount of charge following a previous recharging operation, as a percentage of an effective capacity of the battery.

36. An electrochemical cell system as claimed in claim 35, wherein the battery management system further comprises a multiplier configured for multiplying said estimate of the remaining amount of charge following the previous recharging operation, as a percentage of an effective capacity of the battery, by a value for said effective capacity of the battery.

37. An electrochemical cell system as claimed in claim 35, wherein the battery management system further comprises a second neural network configured for forming said value for said effective capacity of the battery.

38. An electrochemical cell system as claimed in claim 29, wherein the battery management system further comprises a fuel gauge.

39. An electrochemical cell system as claimed in claim 38, wherein the fuel gauge provides a numerical display of a parameter

40. An electrochemical cell system as claimed in claim 38, wherein the fuel gauge provides a warning when the battery is nearly discharged.

41. An electrochemical cell system as claimed in claim 38, wherein the fuel gauge includes an analog display showing remaining battery capacity.