Accuracy Versus Simplicity in Online Battery Model Identification
Accuracy Versus Simplicity in Online Battery Model Identification
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DOI：
10.1109/tsmc.2016.2599281
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
A. Fotouhi;D. Auger;K. Propp;S. Longo
中科院分区：
文献类型：
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作者：
A. Fotouhi;D. Auger;K. Propp;S. Longo
本文提出了一种在线实时应用中电池建模的框架，其中精度很重要，但速度是关键。该框架允许用户选择具有最少数量的参数的模型结构，这些参数与目标应用程序的精度要求一致。在电池模型识别过程中的准确性和速度之间的权衡，探索使用不同的模型结构和参数拟合算法。帕累托最优集获得，允许设计者选择一个适当的精度和速度之间的妥协。为了更清楚地理解电池模型识别问题，提出了“识别表面”。作为电池识别表面的一个结果，一个新的解析解推导出电池模型识别使用的封闭形式的公式，以获得电池的欧姆电阻和开路电压从测量数据。该解析解被用作比较其他拟合算法的基准，并且其自身也用于实际情况中的充电状态估计。仿真研究表明，所提出的框架的有效性和仿真结果进行了验证实验测试的小型镍氢电池组。
This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.