人工神经网络
荷电状态
电池(电)
计算机科学
多层感知器
感知器
卷积神经网络
人工智能
试验数据
机器学习
功率(物理)
物理
量子力学
程序设计语言
作者
Sebastian Pohlmann,Ali Mashayekh,Manuel Kuder,Antje Neve,Thomas Weyh
出处
期刊:Energies
[MDPI AG]
日期:2023-09-21
卷期号:16 (18): 6750-6750
被引量:1
摘要
Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data.
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