APPARATUS AND METHOD FOR TESTING STATE OF CHARGE IN BATTERY

• US 20100324848A1
• Filed: 08/26/2010
• Published: 12/23/2010
• Est. Priority Date: 06/13/2005
• Status: Active Grant

• Current Assignee: LG Energy Solution Limited (LG Corporation)
• Original Assignee: LG Chem Limited (LG Corporation)
• Inventors: Jung, Do Yang, Kim, Do Youn, Cho, Il
• Granted Patent: US 8,626,679 B2
• Time in Patent Office: Days
• US Class Current: 702/63
• 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 including a current detector for detecting current from a battery cell, a voltage detector for detecting voltage from the battery cell, and a temperature detector for detecting temperature from the battery cell;

a soft computing unit for calculating and outputting a battery SOC estimation value by processing the current, the voltage and the temperature detected by the detector unit using a first computing algorithm, which is a combination of a neural network algorithm and a second computing algorithm which adaptively updates parameters, the soft computing unit storing the battery SOC estimation value in a memory; and

a comparator operably coupled to the soft computing unit and a charger-discharger, the charger-discharger being further operably coupled to the battery cell, the comparator comparing the battery SOC estimation value with a predetermined target value that varies with charging or discharging of the battery, the comparator further providing an algorithm update signal to the soft computing unit to update the first computing algorithm based on a learning algorithm when a difference between the battery SOC estimation value and the predetermined target value is outside of critical range.

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 first computing algorithm.

3. The apparatus according to claim 1, wherein the predetermined target value is a reference value obtained through a corresponding test on a specific condition.

4. The apparatus according to claim 1, wherein the predetermined target value is a value of complementing an amp-hour (Ah) counting value or an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

5. The apparatus according to claim 1, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.

6. The apparatus according to claim 2, wherein the predetermined target value is a reference value obtained through a corresponding test on a specific condition.

7. The apparatus according to claim 2 using the first computing algorithm, wherein the predetermined target value is a value of complementing an amp-hour (Ah) counting value or an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

8. The apparatus according to claim 2 using the first computing algorithm, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.

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

detecting current, voltage and temperature from a battery cell, utilizing a current detector, a voltage detector, and a temperature detector, respectively;

calculating a battery SOC estimation value by processing the detected current, voltage and temperature using a first computing algorithm, which is a combination of a neural network algorithm and a second computing algorithm which adaptively updates parameters, utilizing a soft computing unit;

storing the battery SOC estimation value in a memory utilizing the soft computing unit;

calculating a difference value between the calculated battery SOC estimation value and a predetermined target value varying with charging or discharging of the battery utilizing a comparator operably coupled to the soft computing unit and a charger-discharger;

sending an algorithm update signal from the comparator to the soft computing unit when a difference between the battery SOC estimation value and the predetermined target value is outside of a critical range; and

updating the first computing algorithm based on a learning algorithm utilizing the soft computing unit in response to the algorithm update signal.

10. The method according to claim 9, 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 first computing algorithm.

11. The method according to claim 9, wherein the predetermined target value is a reference value obtained through a corresponding test on a specific condition.

12. The method according to claim 9, wherein the predetermined target value is a value of complementing an amp-hour (Ah) counting value or an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.

13. The method according to claim 9, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.

14. The method according to claim 10, wherein the predetermined target value is a reference value obtained through a corresponding test on a specific condition.

15. The method according to claim 10 using the first computing algorithm, wherein the predetermined target value of complementing an amp-hour (Ah) counting value or 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 10 using the first computing algorithm, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.