Lithium Battery State of Charge Estimation Method Based on Transfer Learning

被引：0

作者：

Li L. ^{[1
,2
]}

Yan X. ^{[3
]}

Zhang Y. ^{[1
]}

Feng Y. ^{[1
]}

Hu H. ^{[2
]}

Duan Y. ^{[2
]}

Gui C. ^{[2
]}

机构：

[1] State Grid Shaanxi Electric Power Research Institute, Xi'an

[2] School of Electrical Engineering, Xi'an Jiaotong University, Xi'an

[3] State Grid Xi'an High-Tech Power Supply Company, Xi'an

来源：

Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2023年 / 57卷 / 11期

关键词：

battery state of charge; insufficient samples training; K-fold cross validation; transfer learning;

D O I：

10.7652/xjtuxb202311014

中图分类号：

学科分类号：

摘要：

Aiming at the difficulty of obtaining large data sets and the slow training speed of lithium battery state of charge estimation, a small sample lithium battery state of charge estimation method is proposed by combining deep learning and transfer learning. A deep neural network is constructed based on the convolution-long and short term memory network (CNN-LSTM). In the source domain, K-fold cross-validation is used to divide the NASA data set, select the network with the best SOC estimation performance, and then transfer learning is carried out to Panasonic small data with different temperatures and working conditions in the target domain. In order to improve the overall performance, the influence of network hyperparameters on SOC estimation results is discussed. Compared to non-few-shot transfer learning,the error of this method is reduced by 47. 29% under the same epoch. © 2023 Xi'an Jiaotong University. All rights reserved.

引用

页码：142 / 150

页数：8

共 25 条

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