Journal
JOURNAL OF POWER SOURCES
Volume 230, Issue -, Pages 244-250Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2012.12.057
Keywords
Lithium-ion battery; State of charge estimation; Extended Kalman filter; Sigma point Kalman filter; Convergence behavior; Robust estimation
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One of the most important aspects in battery management systems (BMS) in electric vehicles is the state of charge (SOC) estimation. SOC needs to be accurately determined for safety and performance reasons but cannot be measured directly due to the flatness and hysteresis of the open circuit voltage (OCV) curve of Lithium-ion chemistries as LiFePO4. The classical approach of current integration (Coulomb counting) cannot solve the problems of accumulative error and inaccurate initial values, thus advanced estimation algorithms are applied to determine the sate of charge. In this work, three model-based state observer designs including Luenberger observer, Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF) are carried out and studied. These estimation approaches are verified using measurement data acquired from commercial LiFePO4 cells. In addition, computational tests analyze the systems performances in terms of tracking accuracy, estimation robustness against temperature uncertainty, sensor drift, and convergence behavior with an initial SOC offset. (C) 2012 Elsevier B.V. All rights reserved.
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