基于PyConv-Transformer的锂离子电池剩余寿命预测OA

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.