{"title":"基于改进电热耦合模型和 ANFIS 的锂离子电池内部温度估算","authors":"Jianping Wen, Zhensheng Li, Haodong Zhang, Chuanwei Zhang","doi":"10.1115/1.4064353","DOIUrl":null,"url":null,"abstract":"Accurate estimation of the internal temperature of lithium-ion batteries plays an important role in the development of a suitable battery thermal management system, safeguarding the healthy and safe operation of batteries, and improving battery performance. In order to accurately estimate the internal temperature of the battery, this paper proposes a method for estimating the internal temperature of lithium-ion batteries based on an improved electro-thermal coupling model and an Adaptive Network-based Fuzzy Inference System (ANFIS). First, a parameterization method of the electrical model is proposed, and an electrical model whose parameters are affected by temperature and SOC is established. Second, to overcome the complex nonlinear modeling problem of lithium-ion batteries, the ANFIS thermal model is established. Then, an improved electro-thermal coupling model for lithium-ion batteries is established by combining the proposed electrical model and the ANFIS thermal model to improve the accuracy of estimating the internal temperature of the battery. Finally, the effectiveness of the proposed method is verified by simulation and experiment.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Internal temperature estimation of lithium-ion battery based on improved electro-thermal coupling model and ANFIS\",\"authors\":\"Jianping Wen, Zhensheng Li, Haodong Zhang, Chuanwei Zhang\",\"doi\":\"10.1115/1.4064353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate estimation of the internal temperature of lithium-ion batteries plays an important role in the development of a suitable battery thermal management system, safeguarding the healthy and safe operation of batteries, and improving battery performance. In order to accurately estimate the internal temperature of the battery, this paper proposes a method for estimating the internal temperature of lithium-ion batteries based on an improved electro-thermal coupling model and an Adaptive Network-based Fuzzy Inference System (ANFIS). First, a parameterization method of the electrical model is proposed, and an electrical model whose parameters are affected by temperature and SOC is established. Second, to overcome the complex nonlinear modeling problem of lithium-ion batteries, the ANFIS thermal model is established. Then, an improved electro-thermal coupling model for lithium-ion batteries is established by combining the proposed electrical model and the ANFIS thermal model to improve the accuracy of estimating the internal temperature of the battery. Finally, the effectiveness of the proposed method is verified by simulation and experiment.\",\"PeriodicalId\":15579,\"journal\":{\"name\":\"Journal of Electrochemical Energy Conversion and Storage\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-12-28\",\"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/10.1115/1.4064353\",\"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/10.1115/1.4064353","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ELECTROCHEMISTRY","Score":null,"Total":0}
Internal temperature estimation of lithium-ion battery based on improved electro-thermal coupling model and ANFIS
Accurate estimation of the internal temperature of lithium-ion batteries plays an important role in the development of a suitable battery thermal management system, safeguarding the healthy and safe operation of batteries, and improving battery performance. In order to accurately estimate the internal temperature of the battery, this paper proposes a method for estimating the internal temperature of lithium-ion batteries based on an improved electro-thermal coupling model and an Adaptive Network-based Fuzzy Inference System (ANFIS). First, a parameterization method of the electrical model is proposed, and an electrical model whose parameters are affected by temperature and SOC is established. Second, to overcome the complex nonlinear modeling problem of lithium-ion batteries, the ANFIS thermal model is established. Then, an improved electro-thermal coupling model for lithium-ion batteries is established by combining the proposed electrical model and the ANFIS thermal model to improve the accuracy of estimating the internal temperature of the battery. Finally, the effectiveness of the proposed method is verified by simulation and experiment.
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.