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State of charge estimation of an all-vanadium redox flow battery based on a thermal-dependent model

机译:基于热依赖性模型的全钒氧化还原液流电池的充电状态估算

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For large energy storage in microgrids, vanadium redox flow batteries (VRBs) have received much attention in recent years. VRBs are promising due to the design flexibility, low manufacturing cost for large scale, indefinite lifetime and recyclable electrolytes. VRB modeling is the prerequisite for battery analysis. In previous studies, the effect of temperature on the battery performance is neglected for simplicity. This temperature independent model may lead to large modeling errors when the surrounding temperature varies in a wide range. In this paper, a thermal-dependent electrical circuit model is proposed to describe the charge/discharge characteristics of VRB. The model is validated by the experimental data. State of charge (SOC) estimation is another key problem in management of VRB since an accurate estimation method can well prevent the over-charge/discharge of battery. Therefore, it is necessary to explore an appropriate method for this novel flow battery. Extended kalman filter (EKF) is implemented in this model to achieve a robust SOC estimation. Simulation results show that EKF based estimator is accurate in SOC prediction with temperature variations. The process and measurement errors are minimized by EKF. The estimation of SOC and temperature facilitates optimal battery operation and thermal management.
机译:对于微电网中的大型能量存储,近年来,钒氧化还原液流电池(VRB)备受关注。 VRB由于其设计的灵活性,大规模的低制造成本,无限的使用寿命和可循环使用的电解液而很有前途。 VRB建模是电池分析的前提。在以前的研究中,为简单起见,忽略了温度对电池性能的影响。当周围温度在较大范围内变化时,这种与温度无关的模型可能会导致较大的建模误差。在本文中,提出了一种与温度有关的电路模型来描述VRB的充电/放电特性。实验数据验证了该模型。充电状态(SOC)估计是VRB管理中的另一个关键问题,因为准确的估计方法可以很好地防止电池的过充/放电。因此,有必要探索一种适用于这种新型液流电池的方法。在该模型中实现了扩展卡尔曼滤波器(EKF),以实现可靠的SOC估计。仿真结果表明,基于EKF的估计器在具有温度变化的SOC预测中是准确的。 EKF可以最大程度地减少过程和测量误差。 SOC和温度的估算有助于实现最佳的电池运行和热管理。

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