An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods
An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods
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DOI：
10.1016/j.est.2021.102587
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发表时间：
2021-07
影响因子：
9.4
通讯作者：
Bahman Khaki;Pritam Das
中科院分区：
文献类型：
--
作者：
Bahman Khaki;Pritam Das
提出了一种基于电化学模型和等效电路模型的钒液流电池参数估计模型。通过一种新提出的优化方法找到等效电路元件，以最大限度地减少等效电路的戴维南阻抗和基于KVL的阻抗之间的误差。与大多数先前提出的仅针对恒流充电引入的电路模型相比，所提出的方法适用于所有充电过程，即，恒流、恒压、恒流-恒压充电程序。通过恒流恒压充电实验，在9节VRFB电堆上验证了该模型的正确性。如所观察到的，在恒流充电模式下，端电压模型与测量数据紧密匹配，具有低偏差;然而，端电压模型显示出与恒压充电中VRFB的测量数据的差异。为了改善所提出的电路模型在恒压模式下的差异，两个卡尔曼滤波器，混合扩展卡尔曼滤波和粒子滤波估计算法，在这项研究中使用。结果表明，所提出的等效的准确性与平均偏差为0.88%的端电压模型估计的扩展KF为基础的方法和平均偏差为0.79%的粒子滤波为基础的估计方法，而初始等效电路有7.21%的误差。此外，所提出的程序扩展到估计电池的充电状态。结果表明，在使用PF方法估计电池荷电状态时的平均偏差为4.2%，使用混合扩展KF方法估计电池荷电状态时的平均偏差为4.4%，而电化学SoC估计方法作为参考。这两种基于卡尔曼滤波的方法与使用库仑计数法的荷电状态的平均偏差（7.4%）相比更准确。
This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model's discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%.