基于在线等效电路模型的钒氧化还原液流电池功率状态与充电状态估计

Chun Zheng, X. Tian, Gengsheng Nie, Yafeng Yu, Yingxue Li, Sidi Dong, Jinrui Tang, Binyu Xiong
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引用次数: 2

摘要

准确的功率估算可以保证钒氧化还原流储能系统(VRB-ESS)安全可靠运行,使电池不违反安全运行限值。VRB等效电路模型(ECM)参数的变化会影响其功率状态(SoP)的准确估计,特别是在考虑电池老化效应的情况下。本文分别对荷电状态(SoC)和功率状态(SoP)进行了估计。首先采用递推最小二乘(RLS)方法在线辨识VRB等效电路参数,然后采用无scented卡尔曼滤波(UKF)预测VRB的荷电状态,最后根据极限条件下精确的电池端电压预测VRB的充放电功率。结果表明,UKF能够准确地估计SoC和SoP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of Power and State of Charge Estimation of Vanadium Redox Flow Battery Based on An Online Equivalent Circuit Model
Accurate power estimation can ensure safe and reliable operation of vanadium redox flow energy storage system (VRB-ESS) so that the battery does not violates the safe operating limits. The parameter variation of equivalent circuit model (ECM) of VRB affects the accurate estimation of state of Power (SoP), especially when considering the aging effects of the battery. In this paper, state of charge (SoC) and state of power (SoP) are estimated respectively. Firstly, the recursive least square (RLS) method is applied for online identification of the equivalent circuit parameters of VRB, then unscented Kalman filtering (UKF) is used to predict SoC of VRB, and lastly, the charged or discharged power can be predicted according to the accurate battery terminal voltage under limiting conditions. The results show that the UKF is capable for both the SoC and SoP estimation accurately.
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