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Optimal model based design of experiments applied to high current rate battery cells

机译:基于最优模型的实验设计应用于高电流率电池

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摘要

In this paper, a model based design of experiments algorithm for creating optimal test sequences for identification of battery cell models is presented. Based on an optimality criterion, which is determined using the Fisher information matrix, the test sequence is optimized by a gradient based algorithm. Real load cycle analysis defines the current ranges of interest. By adding constant charge and discharge currents, the test sequence is forced to pass the whole range of state of charge. The constant currents added to the test sequence, furthermore, take relaxation and voltage hysteresis effects into account. The results show that the optimized test sequence improves the high dynamic battery cell model significantly.
机译:在本文中,提出了一种基于模型的实验算法设计,用于创建用于确定电池模型的最佳测试序列。基于使用Fisher信息矩阵确定的最佳标准,通过基于梯度的算法优化测试序列。实际负载循环分析定义了当前的关注范围。通过增加恒定的充电和放电电流,测试序列被迫通过整个充电状态范围。此外,添加到测试序列中的恒定电流还要考虑松弛和电压滞后效应。结果表明,优化的测试程序可以显着改善高动态电池模型。

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