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.
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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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
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@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},}
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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 -