计量学报2026，Vol.47Issue(1)：102-110,9.DOI:10.3969/j.issn.1000-1158.2026.01.13
基于PyConv-Transformer的锂离子电池剩余寿命预测
Residual Life Prediction for Lithium-ion Battery Based on PyConv-Transformer
摘要
Abstract
The remaining useful life(RUL)of lithium-ion batteries is an important parameter for battery health management.In the actual use process of batteries,the phenomenon of capacity regeneration will occur,and it is difficult to avoid noise interference in the process of battery data acquisition,which affects the quality of data.A battery RUL prediction model based on Transformer combined with pyramid convolutional(PyConv)network is proposed to address the above issues.Capacity is chosen as the health indicator,and feature information of the capacity sequence is extracted by the pyramid convolutional network with convolutional kernels of different sizes.The multi-head attention mechanism in Transformer is used to further learn the temporal features of the sequence.The weighted Huber loss function is used to improve the robustness of the model;Dropout technology is used to improve generalization ability of the model and prevent overfitting during training.The proposed prediction model is tested on the NASA and CALCE datasets and compared with other models.The experimental results show that the proposed model has higher prediction accuracy,on the NASA and CALCE datasets,the relative errors are 0.008 6、0.019 3 respectively;the mean absolute errors are 0.011 5 and 0.012 6 respectively;and root mean square errors are 0.017 3 and 0.018 9 respectively.关键词
电学计量/剩余使用寿命/锂电池容量/金字塔卷积网络/Transformer/加权Huber损失函数/DropoutKey words
electrical measurement/remaining useful lif/lithium battery capacity/pyramid convolution network/Transformer/weighted Huber loss function/Dropout分类
通用工业技术引用本文复制引用
吴忠强,吴江浩..基于PyConv-Transformer的锂离子电池剩余寿命预测[J].计量学报,2026,47(1):102-110,9.基金项目
河北省重点实验室绩效补助经费(22567612H) （22567612H）
