A statistical distribution-based pack-integrated model towards state estimation for lithium-ion batteries

IF 15 1区工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2024-01-01 DOI:10.1016/j.etran.2023.100302

Xinan Zhou , Sida Zhou , Zichao Gao , Gaowu Wang , Lei Zong , Jian Liu , Feng Zhu , Hai Ming , Yifan Zheng , Fei Chen , Ning Cao , Shichun Yang

{"title":"A statistical distribution-based pack-integrated model towards state estimation for lithium-ion batteries","authors":"Xinan Zhou , Sida Zhou , Zichao Gao , Gaowu Wang , Lei Zong , Jian Liu , Feng Zhu , Hai Ming , Yifan Zheng , Fei Chen , Ning Cao , Shichun Yang","doi":"10.1016/j.etran.2023.100302","DOIUrl":null,"url":null,"abstract":"<div>The estimation of lithium battery pack is always an essential but troubling issue which has difficulty on considering the inconsistency during state estimation. Herein, an innovative statistical distribution-based pack-integrated model for lithium-ion batteries is proposed and applied for state estimation including state of charge and state of energy. The proposed method highlights the modelling concepts that the terminal voltage of the pack-integrated virtual cell is determined by all cells inside the pack, which takes the advantages of a designed dynamic-weighted terminal voltage according to the voltage distribution inside battery pack. Then, the issue of battery pack modelling and state estimation can be transferred into a virtual single cell and no longer have to consider the inconsistency within battery pack, with the advantages for further extending application from conventional battery modelling method based on single cell. Two kinds of mainstream batteries are experimented for validating, including lithium iron phosphate battery and LiNi0·5Co0·2Mn0·3O2, battery, and both have satisfactory precision, where the maximum error is about 1%–2%, and root mean squared error (RMSE) is eliminated to about 1%. The proposed method is validated with better precision performances on estimating states of battery pack with less calculation and storage, and can be applied both on embedded systems and cloud management platforms.</div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-01-01","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/S2590116823000772","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

Abstract

The estimation of lithium battery pack is always an essential but troubling issue which has difficulty on considering the inconsistency during state estimation. Herein, an innovative statistical distribution-based pack-integrated model for lithium-ion batteries is proposed and applied for state estimation including state of charge and state of energy. The proposed method highlights the modelling concepts that the terminal voltage of the pack-integrated virtual cell is determined by all cells inside the pack, which takes the advantages of a designed dynamic-weighted terminal voltage according to the voltage distribution inside battery pack. Then, the issue of battery pack modelling and state estimation can be transferred into a virtual single cell and no longer have to consider the inconsistency within battery pack, with the advantages for further extending application from conventional battery modelling method based on single cell. Two kinds of mainstream batteries are experimented for validating, including lithium iron phosphate battery and LiNi_0·5Co_0·2Mn_0·3O₂, battery, and both have satisfactory precision, where the maximum error is about 1%–2%, and root mean squared error (RMSE) is eliminated to about 1%. The proposed method is validated with better precision performances on estimating states of battery pack with less calculation and storage, and can be applied both on embedded systems and cloud management platforms.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于统计分布的锂离子电池包集成状态估计模型

锂电池组的状态估计一直是一个重要而又棘手的问题，在状态估计中难以考虑到不一致性。本文提出了一种创新的基于统计分布的锂离子电池包集成模型，并将其应用于包括充电状态和能量状态在内的状态估计。该方法突出了电池组集成虚拟电池的终端电压由电池组内所有电池决定的建模概念，利用了根据电池组内电压分布设计动态加权终端电压的优点。这样就可以将电池组的建模和状态估计问题转移到虚拟的单个电池中，不再需要考虑电池组内部的不一致性，从而可以从传统的基于单个电池的电池建模方法中进一步扩展应用。对磷酸铁锂电池和LiNi0·5Co0·2Mn0·3O2电池两种主流电池进行了实验验证，均具有满意的精度，最大误差约为1% - 2%，均方根误差(RMSE)消除至1%左右。实验证明，该方法具有较好的电池组状态估计精度，且计算量和存储量较少，可应用于嵌入式系统和云管理平台。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Etransportation Engineering-Automotive Engineering

CiteScore

19.80

自引率

12.60%

发文量

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.