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A Study of Online State-of-Health Estimation Method for In-Use Electric Vehicles Based on Charge Data

Di ZHOU, Ping FU, Hongtao YIN, Wei XIE, Shou FENG

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1302-1309
Publication Date
2019/07/01
Publicized
2019/03/29
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDP7010
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

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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