基于热相关模型的全钒氧化还原液流电池电荷状态估计

Xiong Binyu, Jiyun Zhao, Wei Zhongbao, Zhang Chen-da
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引用次数: 11

摘要

钒氧化还原液流电池(vrb)作为微电网中的大型储能技术,近年来受到了广泛的关注。vrb具有设计灵活性、大规模制造成本低、无限寿命和可回收电解质等优点。VRB建模是电池分析的前提。在以往的研究中,为了简单起见,忽略了温度对电池性能的影响。当周围温度变化范围较大时,这种与温度无关的模型可能导致较大的建模误差。本文提出了一种热相关电路模型来描述VRB的充放电特性。实验数据验证了模型的正确性。荷电状态估计是VRB管理中的另一个关键问题,准确的荷电状态估计方法可以很好地防止电池过充放电。因此,有必要探索一种适合这种新型液流电池的方法。该模型采用扩展卡尔曼滤波(EKF)实现鲁棒SOC估计。仿真结果表明,基于EKF的估计方法能够准确预测温度变化下的荷电状态。EKF减小了过程误差和测量误差。SOC和温度的估计有助于优化电池运行和热管理。
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 charge estimation of an all-vanadium redox flow battery based on a thermal-dependent model
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.
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