Apparatus and method for testing state of charge in battery

• US 20070005276A1
• Filed: 06/13/2006
• Published: 01/04/2007
• Est. Priority Date: 06/13/2005
• Status: Abandoned Application

• Current Assignee: LG Chem Limited (LG Corporation)
• Original Assignee: LG Chem Limited (LG Corporation)
• Inventors: Jung, Do, Kim, Do, Cho, Il
• Application Number:
  US11/452,007
• Publication Number:
  US 20070005276A1
• Time in Patent Office: Days
• US Class Current: 702/60
• 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 2260/44 by parameter estimation
• CPC Class Codes: B60L 2260/46 by self learning
• CPC Class Codes: B60L 2260/48 by fuzzy logic
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 58/21 having the same nominal voltage
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/374 with means for correcting the measurement for temperature or ageing
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

Disclosed is an apparatus and method for estimating a state of charge (SOC) in a battery, in which the battery SOC is estimated using a fusion type soft computing algorithm, thereby accurately estimating the battery SOC in a high C-rate environment. The apparatus includes a detector unit for detecting current, voltage and temperature of a battery cell; and soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm. Especially, the soft computing unit combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, and thereby adaptively updates the parameters of the neural network algorithm.

Claims

1. An apparatus for estimating a state of charge (SOC) in a battery, the apparatus comprising:

a detector unit for detecting current, voltage and temperature of a battery cell; and

a soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm.

2. The apparatus according to claim 1, wherein the soft computing unit:

combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, all of which adaptively update parameters; and

adaptively updates the parameters of the neural network algorithm.

3. The apparatus according to claim 1, wherein the neural network algorithm is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.

4. The apparatus according to claim 3, wherein the target value is a reference value obtained through a corresponding test on a specific condition.

5. The apparatus according to claim 3, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

6. The apparatus according to claim 3, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.

7. The apparatus according to claim 2, wherein the neural network algorithm, which is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.

8. The apparatus according to claim 7, wherein the target value is a reference value obtained through a corresponding test on a specific condition.

9. The apparatus according to claim 8 using fusion type soft computing, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

10. The apparatus according to claim 7 using fusion type soft computing, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.

11. A method for estimating a state of charge (SOC) in a battery, the method comprising the steps of:

detecting current, voltage and temperature of a battery cell; and

outputting a battery SOC estimation value of processing the current, voltage and temperature detected by the detector unit using a radial function based on a neural network algorithm.

12. The method according to claim 11, wherein the neural network algorithm:

is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, all of which adaptively update parameters; and

adaptively update the parameters of the neural network algorithm.

13. The method according to claim 11, wherein the neural network algorithm is updated based on a learning algorithm in which, when a difference between the estimation value and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.

14. The method according to claim 13, wherein the target value is a reference value obtained through a corresponding test on a specific condition.

15. The method according to claim 13, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

16. The method according to claim 13, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.

17. The method according to claim 12, wherein the neural network algorithm, which is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.

18. The method according to claim 17, wherein the target value is a reference value obtained through a corresponding test on a specific condition.

19. The method according to claim 18 using fusion type soft computing, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

20. The method according to claim 17 using fusion type soft computing, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.