水准点(测量)
卷积神经网络
计算机科学
循环神经网络
特征提取
人工智能
电池(电)
深度学习
模式识别(心理学)
人工神经网络
机器学习
功率(物理)
物理
大地测量学
量子力学
地理
标识
DOI:10.1016/j.apenergy.2023.122399
摘要
The timely and accurate prediction of degradation patterns of lithium-ion batteries (LIBs) is crucial for investigating their lifespan. Although charging-discharging cycling data are common for this purpose, there has been limited exploration into simultaneously extracting features related to both intra- and inter-cycle behaviors, despite their significant relevance. In this study, machine learning methods are proposed to enable the simultaneous extraction of intra- and inter-cycle features from voltage-current-temperature (VIT) cycling datasets for predicting the knees of LIBs. To standardize the length of each cycling data, the time-series data are realigned based on voltage or employing zero-padding, depending on the dataset type. The cycling dataset is then organized as an array along the time and cycle axes before applying convolutional neural networks (CNNs) and/or recurrent neural networks (RNNs) for feature extraction. Three approaches to utilizing these nonlinear regression tools are explored: 2-dimensional CNN (2D CNN), RNN + 1-dimensional CNN (1D CNN), and RNN + 2D CNN. The resulting knees prediction models are evaluated using a benchmark dataset. Our results underscore the advantage of explicitly considering cycle-to-cycle behavior in conjunction with intra-cycle temporal behavior when constructing a data-driven prediction model. We also investigate reducing the input data requirement to facilitate early knee prediction. The findings demonstrate that the input size can be effectively reduced from 100 to the first 60 cycles without compromising prediction performance.
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