期刊
JOURNAL OF POWER SOURCES
卷 287, 期 -, 页码 1-12出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2015.04.020
关键词
Lithium-ion battery; Remaining useful life; Particle filter; Single particle model; Equivalent circuit model; Unscented Kalman filter
A particle filter (PF) is shown to be more accurate than non-linear least squares (NLLS) and an unscented Kalman filter (UKF) for predicting the remaining useful life (RUL) and time until end of discharge voltage (EODV) of a Lithium-ion battery. The three algorithms, i.e. PF, UKF, and NLLS track four states with correct initial estimates of the states and 5% variation on the initial state estimates. The four states are data-driven, equivalent circuit properties or Lithium concentrations and electroactive surface areas depending on the model. The more accurate prediction performance of PF over NLLS and UKF is reported for three Lithium-ion battery models: a data-driven empirical model, an equivalent circuit model, and a physics-based single particle model. (C) 2015 Elsevier B.V. All rights reserved.
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