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Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery

机译:基于新型多时标估算器的钒液流电池在线荷电状态和模型参数共同估算

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A key function of battery management system (BMS) is to provide accurate information of the state of charge (SOC) in real time, and this depends directly on the precise model parameterization. In this paper, a novel multi-timescale estimator is proposed to estimate the model parameters and SOC for vanadium redox flow battery (VRB) in real time. The model parameters and OCV are decoupled and estimated independently, effectively avoiding the possibility of cross interference between them. The analysis of model sensitivity, stability, and precision suggests the necessity of adopting different timescales for each estimator independently. Experiments are conducted to assess the performance of the proposed method. Results reveal that the model parameters are online adapted accurately thus the periodical calibration on them can be avoided. The online estimated terminal voltage and SOC are both benchmarked with the reference values. The proposed multi-timescale estimator has the merits of fast convergence, high precision, and good robustness against the initialization uncertainty, aging states, flow rates, and also battery chemistries. (C) 2016 Elsevier Ltd. All rights reserved.
机译:电池管理系统(BMS)的关键功能是实时提供准确的充电状态(SOC)信息,而这直接取决于精确的模型参数设置。本文提出了一种新颖的多时标估计器,用于实时估计钒氧化还原液流电池(VRB)的模型参数和SOC。模型参数和OCV分别解耦和估计,有效避免了它们之间发生交叉干扰的可能性。对模型灵敏度,稳定性和精度的分析表明,有必要对每个估计量分别采用不同的时间尺度。实验进行了评估所提出的方法的性能。结果表明,模型参数可以在线进行精确调整,因此可以避免对其进行定期校准。在线估算的端子电压和SOC均以参考值作为基准。所提出的多时标估计器具有收敛快,精度高以及针对初始化不确定性,老化状态,流量以及电池化学性质的良好鲁棒性的优点。 (C)2016 Elsevier Ltd.保留所有权利。

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