The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
전기차(EV)용 리튬이온 배터리의 실시간 건강 상태(SOH) 추정은 EV 유지 관리에 필수적입니다. 본 논문에서는 긴 EV 배터리 용량 테스트 시간, 정기적인 일일 테스트 불가능, 충전 시설 빅데이터 플랫폼에 기록된 EV의 전체 수명주기 충전 데이터 가용성 등 실제 적용 상황에 따라 온라인 사용 상황을 연구합니다. 반복 확장 가우시안 프로세스 회귀 칼만 필터(GPR-EKF)를 사용하여 전기 자동차의 일일 충전 데이터를 기반으로 거시 시간 규모와 미시 시간 규모의 리튬 이온 배터리 데이터를 통합하는 EV 건강 상태 추정 방법. 이 방법은 컬러 측정 노이즈를 결정하기 위해 매크로 시간 규모에서 데이터 피팅을 수행하기 위해 중립 네트워크와 사이클을 통합하는 커널 함수 GPR(가우스 프로세스 회귀)을 제안합니다. 또한, 마이크로 시간 규모의 조각 전하 데이터는 실시간 반복을 통해 조정되어 상태 방정식으로 사용되며, 이는 실시간 SOC 교정 및 비선형화 문제를 효과적으로 해결합니다. 온라인 배터리 상태 추정에서 모델 알고리즘의 타당성, 효율성 및 실시간 성능은 실제 데이터를 통해 검증됩니다.
Di ZHOU
Harbin Institute of Technology,Shenzhen Academy of Metrology & Quality Inspection
Ping FU
Harbin Institute of Technology
Hongtao YIN
Harbin Institute of Technology
Wei XIE
Harbin University of Science and Technology
Shou FENG
Harbin Institute of Technology
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부
Di ZHOU, Ping FU, Hongtao YIN, Wei XIE, Shou FENG, "A Study of Online State-of-Health Estimation Method for In-Use Electric Vehicles Based on Charge Data" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1302-1309, July 2019, doi: 10.1587/transinf.2019EDP7010.
Abstract: The real-time state-of-health (SOH) estimation of lithium-ion batteries for electric vehicles (EV) is essential to EV maintenance. According to situations in practical applications such as long EV battery capacity test time, unavailability of regular daily tests, and availability of full-life-cycle charge data of EV recorded on the charging facility big data platform, this paper studies an online in-use EV state-of-health estimation method using iterated extended Gaussian process regression-Kalman filter (GPR-EKF) to incorporate lithium-ion battery data at the macro time scale and the micro time scale based on daily charge data of electric vehicles. This method proposes a kernel function GPR (Gaussian process regression) integrating neutral network with cycles to conduct fitting for data at the macro time scale to determine colored measurement noise; in addition, fragment charge data at the micro time scale is adjusted with real-time iteration to be used as the state equation, which effectively addresses issues of real-time SOC calibration and nonlinearization. The pertinence, effectiveness and real-time performance of the model algorithm in online battery state-of-health estimation is verified by actual data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7010/_p
부
@ARTICLE{e102-d_7_1302,
author={Di ZHOU, Ping FU, Hongtao YIN, Wei XIE, Shou FENG, },
journal={IEICE TRANSACTIONS on Information},
title={A Study of Online State-of-Health Estimation Method for In-Use Electric Vehicles Based on Charge Data},
year={2019},
volume={E102-D},
number={7},
pages={1302-1309},
abstract={The real-time state-of-health (SOH) estimation of lithium-ion batteries for electric vehicles (EV) is essential to EV maintenance. According to situations in practical applications such as long EV battery capacity test time, unavailability of regular daily tests, and availability of full-life-cycle charge data of EV recorded on the charging facility big data platform, this paper studies an online in-use EV state-of-health estimation method using iterated extended Gaussian process regression-Kalman filter (GPR-EKF) to incorporate lithium-ion battery data at the macro time scale and the micro time scale based on daily charge data of electric vehicles. This method proposes a kernel function GPR (Gaussian process regression) integrating neutral network with cycles to conduct fitting for data at the macro time scale to determine colored measurement noise; in addition, fragment charge data at the micro time scale is adjusted with real-time iteration to be used as the state equation, which effectively addresses issues of real-time SOC calibration and nonlinearization. The pertinence, effectiveness and real-time performance of the model algorithm in online battery state-of-health estimation is verified by actual data.},
keywords={},
doi={10.1587/transinf.2019EDP7010},
ISSN={1745-1361},
month={July},}
부
TY - JOUR
TI - A Study of Online State-of-Health Estimation Method for In-Use Electric Vehicles Based on Charge Data
T2 - IEICE TRANSACTIONS on Information
SP - 1302
EP - 1309
AU - Di ZHOU
AU - Ping FU
AU - Hongtao YIN
AU - Wei XIE
AU - Shou FENG
PY - 2019
DO - 10.1587/transinf.2019EDP7010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - The real-time state-of-health (SOH) estimation of lithium-ion batteries for electric vehicles (EV) is essential to EV maintenance. According to situations in practical applications such as long EV battery capacity test time, unavailability of regular daily tests, and availability of full-life-cycle charge data of EV recorded on the charging facility big data platform, this paper studies an online in-use EV state-of-health estimation method using iterated extended Gaussian process regression-Kalman filter (GPR-EKF) to incorporate lithium-ion battery data at the macro time scale and the micro time scale based on daily charge data of electric vehicles. This method proposes a kernel function GPR (Gaussian process regression) integrating neutral network with cycles to conduct fitting for data at the macro time scale to determine colored measurement noise; in addition, fragment charge data at the micro time scale is adjusted with real-time iteration to be used as the state equation, which effectively addresses issues of real-time SOC calibration and nonlinearization. The pertinence, effectiveness and real-time performance of the model algorithm in online battery state-of-health estimation is verified by actual data.
ER -