利用短期弛豫电压进行深度学习驱动的电池电压-容量曲线预测

IF 15 1区 工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2024-10-30 DOI:10.1016/j.etran.2024.100378
Aihua Tang , Yuchen Xu , Pan Liu , Jinpeng Tian , Zikang Wu , Yuanzhi Hu , Quanqing Yu
{"title":"利用短期弛豫电压进行深度学习驱动的电池电压-容量曲线预测","authors":"Aihua Tang ,&nbsp;Yuchen Xu ,&nbsp;Pan Liu ,&nbsp;Jinpeng Tian ,&nbsp;Zikang Wu ,&nbsp;Yuanzhi Hu ,&nbsp;Quanqing Yu","doi":"10.1016/j.etran.2024.100378","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage\",\"authors\":\"Aihua Tang ,&nbsp;Yuchen Xu ,&nbsp;Pan Liu ,&nbsp;Jinpeng Tian ,&nbsp;Zikang Wu ,&nbsp;Yuanzhi Hu ,&nbsp;Quanqing Yu\",\"doi\":\"10.1016/j.etran.2024.100378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.</div></div>\",\"PeriodicalId\":36355,\"journal\":{\"name\":\"Etransportation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.0000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Etransportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590116824000687\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116824000687","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

准确监控电池容量衰减对电池的稳定运行至关重要。然而,仅凭有限的循环信息来评估最大容量并不足以充分显示电池退化的程度。在此,本研究提出了一种使用松弛电压结合编码器-解码器的电池劣化监测方法,将传统的最大容量估算扩展到整个电压-容量(V-Q)曲线估算。编码器-解码器采用无监督预训练和迁移学习的两阶段训练策略。首先,将短时弛豫电压序列输入自动编码器进行无监督预训练。通过这一自动编码过程,编码器获得了在相同测试条件下对未标记的弛豫电压进行特征学习的能力。随后,通过冻结编码器权重和在解码器上执行迁移学习,将弛豫电压序列映射到相应的 V-Q 曲线,从而完成两阶段训练过程。在相同的历时下,与直接训练相比,所提出的方法实现了更先进的预测性能。这意味着使用 V-Q 曲线和推导出的增量容量曲线进行全面电池劣化监测的准确性更高。经过对来自不同实验室的 130 个电池样本的验证,所提出的方法能预测出高保真的 V-Q 曲线,均方根误差小于 0.03 Ah。这项研究强调了采用弛豫电压进行电池退化监测的能力,由于所需的弛豫时间较短,而且无需额外的循环信息,因此有望在非恒定电流充电情况下实现快速、全面的老化诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage
Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
期刊最新文献
Explosion characteristics of two-phase ejecta from large-capacity lithium iron phosphate batteries Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage Experimental analysis and optimal control of temperature with adaptive control objective for fuel cells Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives Trustworthy V2G scheduling and energy trading: A blockchain-based framework
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1