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首页> 外文期刊>Journal of Energy Storage >Dynamic model of a lithium-ion cell using an artificial feedforward neural network with dynamical signal preprocessing
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Dynamic model of a lithium-ion cell using an artificial feedforward neural network with dynamical signal preprocessing

机译：具有动态信号预处理的人工前馈神经网络锂离子电池的动态模型

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摘要

A common approach in lithium-ion cell modeling is based on an electrical equivalent circuit model. The advantage of the electrical model is its simple structure. However, estimation of its parameters usually requires several steps in post-processing of the measured data in order to achieve satisfying accuracy. In this paper, we propose a solution with an artificial feedforward neural network and dynamical signal preprocessing, which does not require complex estimation procedures and has good accuracy. In contrast to the common conviction that a neural network requires many tests in a training data set, we show that only a few tests are enough to train the neural network. In the paper, we present practical aspects of the training process including methods to overcome obstacles related to measurement inaccuracy. Finally, the results of the artificial neural network model are validated and compared with those from an electrical equivalent circuit model.
机译：锂离子电池建模中的一种常见方法基于电力等效电路模型。电气模型的优点是其结构简单。然而，对其参数的估计通常需要若干步骤在测量数据的后处理中，以实现满意的准确性。在本文中，我们提出了一种具有人工前馈神经网络的解决方案和动态信号预处理，这不需要复杂的估计程序并具有良好的精度。与神经网络在训练数据集中需要许多测试的共同定罪相比，我们表明只有少数测试足以训练神经网络。在论文中，我们展示了培训过程的实际方面，包括克服与测量不准确性相关的障碍的方法。最后，验证了人工神经网络模型的结果，并与电子等效电路模型的结果进行了验证。

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