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.

锂离子电池建模中的一种常见方法是基于等效电路模型。电气模型的优点是结构简单。然而，其参数的估计通常需要对测量数据进行后处理的几个步骤，以达到令人满意的精度。在本文中，我们提出了一种具有人工前馈神经网络和动态信号预处理的解决方案，该解决方案不需要复杂的估计程序并且具有良好的准确性。与通常认为神经网络需要训练数据集中的许多测试的信念相反，我们证明只有很少的测试足以训练神经网络。在本文中，我们介绍了培训过程的实际方面，包括克服与测量不准确相关的障碍的方法。最后，