基于模型数据融合的快速充电锂离子电池健康状态预测方法

IF 2.7 4区工程技术 Q3 ELECTROCHEMISTRY

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2023-07-19 DOI:10.1115/1.4062990

Hailin Feng, Yatian Liu

{"title":"基于模型数据融合的快速充电锂离子电池健康状态预测方法","authors":"Hailin Feng, Yatian Liu","doi":"10.1115/1.4062990","DOIUrl":null,"url":null,"abstract":"\n Fast charging has become the norm for various electronic products. The research on the state of health (SOH) prediction of fast-charging lithium-ion battery deserves more attention. In this paper, a model-data fusion SOH prediction method which can reflect the degradation mechanism of fast-charging battery is proposed. Firstly, based on the Arrhenius model, the logarithmic-power function (LP) model and logarithmic-linear (LL) model related to the fast-charging rate are established. Secondly, combined with Gaussian process regression (GPR) prediction, particle filter is used to update the parameters of models in real time. Compared with the single GPR, the average root mean square error of LP and LL are reduced by 71.56% and 69.11%, respectively. Finally, the sensitivity and superiority of the two models are analyzed by using Sobol method, Akaike and Bayesian Information Criterion. The results show that the two models are more suitable for fast-charging lithium batteries than the traditional Arrhenius model, and LP model is better than LL model.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A approach for fast-charging lithium-ion batteries state of health prediction based on model-data fusion\",\"authors\":\"Hailin Feng, Yatian Liu\",\"doi\":\"10.1115/1.4062990\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Fast charging has become the norm for various electronic products. The research on the state of health (SOH) prediction of fast-charging lithium-ion battery deserves more attention. In this paper, a model-data fusion SOH prediction method which can reflect the degradation mechanism of fast-charging battery is proposed. Firstly, based on the Arrhenius model, the logarithmic-power function (LP) model and logarithmic-linear (LL) model related to the fast-charging rate are established. Secondly, combined with Gaussian process regression (GPR) prediction, particle filter is used to update the parameters of models in real time. Compared with the single GPR, the average root mean square error of LP and LL are reduced by 71.56% and 69.11%, respectively. Finally, the sensitivity and superiority of the two models are analyzed by using Sobol method, Akaike and Bayesian Information Criterion. The results show that the two models are more suitable for fast-charging lithium batteries than the traditional Arrhenius model, and LP model is better than LL model.\",\"PeriodicalId\":15579,\"journal\":{\"name\":\"Journal of Electrochemical Energy Conversion and Storage\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrochemical Energy Conversion and Storage\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062990\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ELECTROCHEMISTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrochemical Energy Conversion and Storage","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062990","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ELECTROCHEMISTRY","Score":null,"Total":0}

引用次数: 0

摘要

快速充电已经成为各种电子产品的常态。快速充电锂离子电池的健康状态预测研究值得关注。本文提出了一种能够反映快充电池退化机理的模型数据融合SOH预测方法。首先，在Arrhenius模型的基础上，建立了与快速充电率相关的对数功率函数（LP）模型和对数线性（LL）模型。其次，结合高斯过程回归（GPR）预测，使用粒子滤波器实时更新模型参数。与单一GPR相比，LP和LL的平均均方根误差分别降低了71.56%和69.11%。最后，利用Sobol方法、Akaike和贝叶斯信息准则分析了这两种模型的敏感性和优越性。结果表明，这两种模型比传统的Arrhenius模型更适合锂电池的快速充电，LP模型比LL模型更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A approach for fast-charging lithium-ion batteries state of health prediction based on model-data fusion

Fast charging has become the norm for various electronic products. The research on the state of health (SOH) prediction of fast-charging lithium-ion battery deserves more attention. In this paper, a model-data fusion SOH prediction method which can reflect the degradation mechanism of fast-charging battery is proposed. Firstly, based on the Arrhenius model, the logarithmic-power function (LP) model and logarithmic-linear (LL) model related to the fast-charging rate are established. Secondly, combined with Gaussian process regression (GPR) prediction, particle filter is used to update the parameters of models in real time. Compared with the single GPR, the average root mean square error of LP and LL are reduced by 71.56% and 69.11%, respectively. Finally, the sensitivity and superiority of the two models are analyzed by using Sobol method, Akaike and Bayesian Information Criterion. The results show that the two models are more suitable for fast-charging lithium batteries than the traditional Arrhenius model, and LP model is better than LL model.

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

Journal of Electrochemical Energy Conversion and Storage Engineering-Mechanics of Materials

CiteScore

4.90

自引率

4.00%

发文量

期刊介绍： The Journal of Electrochemical Energy Conversion and Storage focuses on processes, components, devices and systems that store and convert electrical and chemical energy. This journal publishes peer-reviewed archival scholarly articles, research papers, technical briefs, review articles, perspective articles, and special volumes. Specific areas of interest include electrochemical engineering, electrocatalysis, novel materials, analysis and design of components, devices, and systems, balance of plant, novel numerical and analytical simulations, advanced materials characterization, innovative material synthesis and manufacturing methods, thermal management, reliability, durability, and damage tolerance.