KR100793616B1 - 배터리 잔존량 추정 장치 및 방법 - Google Patents
배터리 잔존량 추정 장치 및 방법 Download PDFInfo
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- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
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- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
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- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
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- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/44—Control modes by parameter estimation
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- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/46—Control modes by self learning
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
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Abstract
Description
Claims (20)
- 배터리 잔존량을 추정하는 장치에 있어서,배터리 셀의 전류, 전압 및 온도를 검출하는 센싱부;파라미터를 적응적으로 갱신하는 소프트 컴퓨팅 알고리즘과 신경망 알고리즘이 결합된 퓨전 타입의 소프트 컴퓨팅 알고리즘에 의해 상기 센싱부에서 검출된 전류, 전압 및 온도를 처리하여 배터리 잔존량의 추정값을 출력하는 소프트 컴퓨팅부;를 포함하는 것을 특징으로 하는 배터리 잔존량 추정 장치.
- 제1항에 있어서,상기 소프트 컴퓨팅부는 상기 신경망 알고리즘에 파라미터를 적응적으로 갱신하는 퍼지 알고리즘, 유전 알고리즘, 셀룰러 오토메타 알고리즘, 면역 시스템 알고리즘 또는 러프-세트 알고리즘 중의 어느 하나를 결합하여,상기 신경망 알고리즘의 파라미터를 적응적으로 갱신함을 특징으로 하는 배터리 잔존량 추정장치.
- 제1항에 있어서,상기 신경망 알고리즘은,상기 소프트 컴퓨팅부에서 출력된 추정값과 소정 목표값의 차가 임계범위를 벗어나면 상기 소정 목표값을 추종하도록 학습시키는 학습 알고리즘에 따라 갱신됨을 특징으로 하는 배터리 잔존량 추정장치.
- 제3항에 있어서, 상기 목표값은특정 조건에서 해당 실험을 통하여 얻은 기준값인 것을 특징으로 하는 배터리 잔존량 추정 장치.
- 제3항에 있어서, 상기 목표값은배터리의 정격 용량에서 충방전기로부터 입력되는 Ah 카운팅 값과 배터리의 OCV(Open Circuit Voltage) 값을 상호 보완한 값인 것을 특징으로 하는 배터리 잔존량 추정 장치.
- 제3항에 있어서, 상기 학습 알고리즘은역전파 학습 알고리즘, 칼만 필터, 유전 알고리즘, 퍼지 학습 알고리즘 중 어느 하나인 것을 특징으로 하는 배터리 잔존량 추정 장치.
- 제2항에 있어서,상기 퍼지 알고리즘, 상기 유전 알고리즘, 상기 셀룰러 오토메타 알고리즘, 상기 면역 시스템 알고리즘 또는 상기 러프-세트 알고리즘 중 어느 하나와 결합된 신경망 알고리즘은,상기 소프트 컴퓨팅부에서 출력된 추정값과 소정 목표값의 차가 임계범위를 벗어나면 상기 소정 목표값을 추종하도록 학습시키는 학습 알고리즘에 따라 갱신됨 을 특징으로 하는 배터리 잔존량 추정장치.
- 제7항에 있어서, 상기 목표값은특정 조건에서 해당 실험을 통하여 얻은 기준값인 것을 특징으로 하는 배터리 잔존량 추정 장치.
- 제7항에 있어서, 상기 목표값은배터리의 정격 용량에서 충방전기로부터 입력되는 Ah 카운팅 값과 배터리의 OCV(Open Circuit Voltage) 값을 상호 보완한 값인 것을 특징으로 하는 퓨전 형태의 소프트 컴퓨팅을 이용한 배터리 잔존량 추정 장치.
- 제7항에 있어서, 상기 학습 알고리즘은역전파 학습 알고리즘, 칼만 필터, 유전 알고리즘, 퍼지 학습 알고리즘 중 어느 하나인 것을 특징으로 하는 퓨전 형태의 소프트 컴퓨팅을 이용한 배터리 잔존량 추정 장치.
- 배터리 잔존량을 추정하는 방법에 있어서,배터리 셀의 전류, 전압 및 온도를 검출하는 단계;파라미터를 적응적으로 갱신하는 소프트 컴퓨팅 알고리즘과 신경망 알고리즘이 결합된 퓨전 타입의 소프트 컴퓨팅 알고리즘에 의해 상기 검출된 전류, 전압 및 온도를 처리하여 배터리 잔존량의 추정값을 출력하는 단계;를 포함하는 것을 특징으로 하는 배터리 잔존량 추정 방법.
- 제11항에 있어서,상기 신경망 알고리즘은 파라미터를 적응적으로 갱신하는 퍼지 알고리즘, 유전 알고리즘, 셀룰러 오토메타 알고리즘, 면역 시스템 알고리즘 또는 러프-세트 알고리즘 중의 어느 하나와 결합되어,상기 신경망 알고리즘의 파라미터를 적응적으로 갱신함을 특징으로 하는 배터리 잔존량 추정방법.
- 제11항에 있어서,상기 신경망 알고리즘은,상기 추정값과 소정 목표값의 차가 임계범위를 벗어나면 상기 소정 목표값을 추종하도록 학습시키는 학습 알고리즘에 따라 갱신됨을 특징으로 하는 배터리 잔존량 추정방법.
- 제13항에 있어서, 상기 목표값은특정 조건에서 해당 실험을 통하여 얻은 기준값인 것을 특징으로 하는 배터리 잔존량 추정방법.
- 제13항에 있어서, 상기 목표값은배터리의 정격 용량에서 충방전기로부터 입력되는 Ah 카운팅 값과 배터리의 OCV(Open Circuit Voltage) 값을 상호 보완한 값인 것을 특징으로 하는 배터리 잔존량 추정방법.
- 제13항에 있어서, 상기 학습 알고리즘은역전파 학습 알고리즘, 칼만 필터, 유전 알고리즘, 퍼지 학습 알고리즘 중 어느 하나인 것을 특징으로 하는 배터리 잔존량 추정방법.
- 제12항에 있어서,상기 퍼지 알고리즘, 상기 유전 알고리즘, 상기 셀룰러 오토메타 알고리즘, 상기 면역 시스템 알고리즘 또는 상기 러프-세트 알고리즘 중 어느 하나와 결합된 신경망 알고리즘은,상기 소프트 컴퓨팅부에서 출력된 추정값과 소정 목표값의 차가 임계범위를 벗어나면 상기 소정 목표값을 추종하도록 학습시키는 학습 알고리즘에 따라 갱신됨을 특징으로 하는 배터리 잔존량 추정방법.
- 제17항에 있어서, 상기 목표값은특정 조건에서 해당 실험을 통하여 얻은 기준값인 것을 특징으로 하는 배터리 잔존량 추정방법.
- 제17항에 있어서, 상기 목표값은배터리의 정격 용량에서 충방전기로부터 입력되는 Ah 카운팅 값과 배터리의 OCV(Open Circuit Voltage) 값을 상호 보완한 값인 것을 특징으로 하는 퓨전 형태의 소프트 컴퓨팅을 이용한 배터리 잔존량 추정방법.
- 제17항에 있어서, 상기 학습 알고리즘은역전파 학습 알고리즘, 칼만 필터, 유전 알고리즘, 퍼지 학습 알고리즘 중 어느 하나인 것을 특징으로 하는 퓨전 형태의 소프트 컴퓨팅을 이용한 배터리 잔존량 추정방법.
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US (2) | US20070005276A1 (ko) |
EP (1) | EP1896925B1 (ko) |
JP (1) | JP5160416B2 (ko) |
KR (1) | KR100793616B1 (ko) |
CN (1) | CN101198922B (ko) |
TW (1) | TWI320977B (ko) |
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
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KR101020904B1 (ko) * | 2008-12-03 | 2011-03-09 | 현대자동차주식회사 | 자동차의 배터리 잔존용량 계산 시스템 및 방법 |
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JP2008546989A (ja) | 2008-12-25 |
US20070005276A1 (en) | 2007-01-04 |
EP1896925B1 (en) | 2020-10-21 |
US20100324848A1 (en) | 2010-12-23 |
JP5160416B2 (ja) | 2013-03-13 |
CN101198922A (zh) | 2008-06-11 |
EP1896925A1 (en) | 2008-03-12 |
TWI320977B (en) | 2010-02-21 |
TW200707823A (en) | 2007-02-16 |
KR20060129962A (ko) | 2006-12-18 |
WO2006135175B1 (en) | 2007-03-29 |
WO2006135175A1 (en) | 2006-12-21 |
EP1896925A4 (en) | 2017-10-04 |
US8626679B2 (en) | 2014-01-07 |
CN101198922B (zh) | 2012-05-30 |
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