基于深度残差网络的电池容量预测

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Yankui Wang, Wenhao Yao, Min Dong, Yixuan Li, Longxing Zhu, Sheng Bi
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引用次数: 0

摘要

一致性对电池组的寿命至关重要。因此,锂电池在生产过程中有一个特殊的过程来确定其容量(即分级)。然而,这个过程需要很长时间。我们提出了一种基于深度学习的新方法,利用传感器在分级过程前收集的数据来预测电池容量,希望减少整个过程中消耗的时间。我们提出了一个端到端电池容量预测模型。在我们的处理步骤中不需要复杂的特征提取步骤。相反,我们使用残差网络来自动完成。我们修改了原来的ResNet以适应我们的任务。使用卷积一维层和全局池化层提取时间序列特征。为了提高模型的精度,我们设计了一个融合模型来处理多步过程的时间序列。迁移学习被用来帮助我们更快地训练模型。在测试集上的结果表明,我们的融合模型预测容量的均方根误差为4mAh,与基准模型相比下降了45%。我们将提取的特征可视化,对模型进行解释,并解释我们模型的可能机制。在此基础上,提出了提高预测性能的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Battery Capacity Based on Deep Residual Network
Consistency is essential to the life of battery packs. Therefore, there is a special process to determine the capacity of lithium batteries in their production process (aka grading). However, this process takes a very long time. We propose a new method based on deep learning, which uses data collected by sensors before the grading process to predict the battery capacity, hoping to reduce the time consumed in the whole process. We propose an end-to-end battery capacity prediction model. In our processing steps, complex feature extraction steps are not needed. On the contrary, we use a residual network to complete it automatically. We modified the original ResNet to suit our task. Convolution1D and global pooling layers are used to extract the time series feature. To improve the model's accuracy, we design a fusion model to deal with the time series of multi-step processes. Transfer learning is applied to help us train the model faster. The results on the test set show that the root mean square error of the predicted capacity of our fusion model is 4mAh, which is a 45% decline compared with the benchmark model. We visualize the extracted features, interpret the model and explain the possible mechanism of our model. Furthermore, based on our analysis, suggestions for improving prediction performance are put forward.
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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