采用迁移学习的小样本锂电池荷电状态估算方法

李龙; 燕旭朦; 张钰声; 冯雅琳; 胡红利; 段羽洁; 崔晨辉

doi:10.7652/xjtuxb202311014

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采用迁移学习的小样本锂电池荷电状态估算方法

—————————————————————— | 更新时间：2025-12-08

- 采用迁移学习的小样本锂电池荷电状态估算方法
- Lithium Battery State of Charge Estimation Method Based on Transfer Learning
- 西安交通大学学报 2023年57卷第11期页码：142-150
- 作者机构：
  
  1. 国网陕西省电力有限公司电力科学研究院,西安,710199
  2. 西安交通大学电气工程学院,西安,710049
  3. 国网西安高新供电公司,西安,710076
- 作者简介：
  
  : 2023-02-24。作者简介: 李龙(1988—),男,博士,工程师
  胡红利(通信作者),男,教授,博士生导师。基金项目: 国网陕西省电力有限公司科技资助项目(5226KY220002)
- 基金信息：
  
  : 2023-02-24。作者简介: 李龙(1988—),男,博士,工程师;胡红利(通信作者),男,教授,博士生导师。基金项目: 国网陕西省电力有限公司科技资助项目(5226KY220002);国家自然科学基金资助项目(52177009)。
- DOI：10.7652/xjtuxb202311014
  中图分类号： TM912
- 网络首发：2023-11-10，
  
  纸质出版：2023
- 稿件说明：
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李龙, 燕旭朦, 张钰声, 等. 采用迁移学习的小样本锂电池荷电状态估算方法[J]. 西安交通大学学报, 2023,57(11):142-150.

LI Long, YAN Xumeng, ZHANG Yusheng, et al. Lithium Battery State of Charge Estimation Method Based on Transfer Learning[J]. 2023, 57(11): 142-150.
李龙, 燕旭朦, 张钰声, 等. 采用迁移学习的小样本锂电池荷电状态估算方法[J]. 西安交通大学学报, 2023,57(11):142-150. DOI： 10.7652/xjtuxb202311014.

LI Long, YAN Xumeng, ZHANG Yusheng, et al. Lithium Battery State of Charge Estimation Method Based on Transfer Learning[J]. 2023, 57(11): 142-150. DOI： 10.7652/xjtuxb202311014.

摘要

针对锂电池荷电状态(SOC)估算面临的大型数据集获取困难和训练速度慢的问题

结合深度学习和迁移学习提出一种小样本锂电池荷电状态估算方法。基于卷积-长短期记忆网络(CNN-LSTM)构建深度神经网络结构。在源域上采用K折交叉验证对NASA数据集进行划分

选取SOC估计性能最优的网络

利用目标域内具有多种工况和温度条件的Panasonic小样本数据进行迁移学习。为了提升方法的整体性能

分析了网络超参数对SOC估计结果的影响。实验结果表明:在相同的迭代次数下

该方法在不同的工况下可以较准确地实现小样本电池SOC估计

相较于非小样本迁移学习处理方法的均方根误差降低了47.29%。

Abstract

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.

关键词

Keywords

references

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