4.8 Article

An adaptive model for vanadium redox flow battery and its application for online peak power estimation

期刊

JOURNAL OF POWER SOURCES
卷 344, 期 -, 页码 195-207

出版社

ELSEVIER
DOI: 10.1016/j.jpowsour.2017.01.102

关键词

Model parameters identification; Peak power; Estimation; Battery model; Vanadium redox flow battery

资金

  1. National Research Foundation from Republic of Singapore through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in Tropics (SinBerBest) Program

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An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed two-step verification method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed. (C) 2017 Elsevier B.V. All rights reserved.

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