[1]雷孟飞,梁泉,孙世豪,等.基于自注意力和GRU的锂电池健康状态估计[J].计算机技术与发展,2024,34(05):213-220.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0062]
　LEI Meng-fei,LIANG Quan,SUN Shi-hao,et al.Health State Estimation of Lithium-ion Batteries Based on Discharge Process and Self-Attention-GRU[J].,2024,34(05):213-220.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0062]
点击复制

基于自注意力和GRU的锂电池健康状态估计()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年05期
页码:
213-220
栏目:
新型计算应用系统
出版日期:
2024-05-10

文章信息/Info

Title:
Health State Estimation of Lithium-ion Batteries Based on Discharge Process and Self-Attention-GRU
文章编号:
1673-629X(2024)05-0213-08
作者:
雷孟飞; 梁泉; 孙世豪; 林勇强
福建理工大学 计算机科学与数学学院,福建 福州 350118
Author(s):
LEI Meng-fei; LIANG Quan; SUN Shi-hao; LIN Yong-qiang
School of Computer Science and Mathematics,Fujian University of Technology,Fuzhou 350118,China
关键词:
健康状态; 自注意力机制; 门控循环单元(GRU); 锂离子电池; 健康因子
Keywords:
state of health; self-attention mechanism; gate recurrent unit (GRU); lithium ion battery; health factors
分类号:
TP391.41
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0062
摘要:
为了解决锂离子电池使用中特征提取数据不足和模型需要大量历史数据的问题,通过分析锂离子电池使用中的数据,提出了放电过程中基于 Self-Attention-GRU 的锂离子电池健康状态估计方法。 在没有历史数据的锂电池上使用相同型号锂电池历史数据训练的模型估计电池健康状态;拥有一定量老化数据后,使用锂电池自身的老化数据训练模型估计电池健康状态。 提取放电过程的等压降放电时间、电压均方根和放电功率作为健康因子,利用融合自注意力机制的门控循环单元建立健康因子和健康状态( SOH)之间的映射关系。 使用 4 组 CALCE 电池老化数据进行实验验证。 模型在20% 老化数据作为训练集时 MAE 和 RMSE 分别达到 1. 03% 和 1. 25% ;在 30% ,40% 老化数据和相同型号电池全部老化数据作为训练集时模型的 MAE 和 RMSE 都小于等于 1% 。 说明该方法在估计锂离子电池健康状态估计方面具有较高的精确性和可靠性。
Abstract:
In order to solve the problem of insufficient feature extraction data for lithium-ion batteries in use and the need for a large amount of historical data in the model,a Self-Attention -GRU based health state estimation method for lithium- ion batteries during discharge process was proposed by analyzing the data of lithium-ion batteries in use. The battery health status was estimated by a model trained on historical data of the same type of lithium battery on lithium batteries without historical data. After having a certain amount of aging data,the aging data of the lithium battery itself was used to train the model and estimate the battery’s health status. The equal voltage drop discharge time,root mean square of voltage,and discharge power of the discharge process were extracted as health factors,and the mapping relationship between health factors and state of health (SOH) was established using a gating cycle unit fused with self attention mechanism. Experimental validation was conducted using 4 sets of CALCE battery aging data. When 20% of the aging data was used as the training set,the MAE and RMSE of the model reached 1. 03% and 1. 25% ,respectively. When 30% ,40% of the aging data and all aging data of the same type of battery were used as training sets,the MAE and RMSE of the model were both less than or equal to 1% . It was showed that the proposed method had high accuracy and reliability in estimating the health status of lithium-ion bat-teries.

相似文献/References:

[1]郑树挺,徐菲菲.基于改进 Self-Attention 的股价趋势预测[J].计算机技术与发展,2021,31(03):33.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 006]
　ZHENG Shu-ting,XU Fei-fei.Research on Stock Price Trend Prediction Based on Self-Attention Model[J].,2021,31(05):33.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 006]
[2]李肖南,王 蕾,程海霞,等.基于 SA-PointNetVLAD 的点云分类网络[J].计算机技术与发展,2022,32(05):36.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 006]
　LI Xiao-nan,WANG Lei,CHENG Hai-xia,et al.Point Cloud Classification Network Based on SA-PointNetVLAD[J].,2022,32(05):36.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 006]
[3]秦昊宇,葛 瑶,张力波,等.基于自注意力机制的视频超分辨率重建[J].计算机技术与发展,2022,32(08):42.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 007]
　QIN Hao-yu,GE Yao,ZHANG Li-bo,et al.Video Super-resolution Reconstruction Based on Self Attention Mechanism[J].,2022,32(05):42.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 007]
[4]黄 莉,何美玲*.基于 U-Net 改进模型的多模态脑肿瘤分割方法[J].计算机技术与发展,2022,32(11):58.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 009]
　HUANG Li,HE Mei-ling*.Multi-model Brain Tumor Segmentation Method Based on Improved U-Net Model[J].,2022,32(05):58.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 009]
[5]李 飞,陈 瑞,童 莹,等.基于增强特征和注意力机制的视频表情识别[J].计算机技术与发展,2022,32(11):183.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 027]
　LI Fei,CHEN Rui,TONG Ying,et al.Video Facial Expression Recognition Based on ECNN-SA[J].,2022,32(05):183.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 027]
[6]李丰翼,刘万里,杨晓辉,等.基于多头自注意力机制与 CNN 的文本分类模型[J].计算机技术与发展,2022,32(S1):18.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 004]
　LI Feng-yi,LIU Wan-li,YANG Xiao-hui,et al.Text Classification Model Based on Multi-headed Self-attention Mechanism and CNN[J].,2022,32(05):18.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 004]
[7]赵建强,朱万彤,陈 诚.基于多重卷积神经网络模型的命名实体识别[J].计算机技术与发展,2023,33(01):187.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 028]
　ZHAO Jian-qiang,ZHU Wan-tong,CHEN Cheng.Named Entity Recognition Based on Duplex Convolution Neural Network Model[J].,2023,33(05):187.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 028]
[8]黄汉琴,顾进广,符海东.融合依存句法和实体信息的临床时间关系抽取[J].计算机技术与发展,2024,34(01):128.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 019]
　HUANG Han-qin,GU Jin-guang,FU Hai-dong.Extraction of Clinical Temporal Relation Fusing Dependency Syntax and Entity Information[J].,2024,34(05):128.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 019]
[9]刘 洋,黎茂锋,黄 俊,等.面向方面级情感分析的双通道图卷积网络[J].计算机技术与发展,2024,34(03):49.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 008]
　LIU Yang,LI Mao-feng,HUANG Jun,et al.Aspect-based Sentiment Analysis via Dual-channel Graph Convolutional Network[J].,2024,34(05):49.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 008]
[10]李琪,刘春霞,高改梅.基于CapsNet和SRU的工业互联网入侵检测方法[J].计算机技术与发展,2024,34(07):93.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0097]
　LI Qi,LIU Chun-xia,GAO Gai-mei.Intrusion Detection Model Based on CapsNet and SRU for Industrial Internet[J].,2024,34(05):93.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0097]

更新日期/Last Update: 2024-05-10