Muhammad Fikri Irsyad Mat Razi, Zul Hilmi Che Daud, Zainab Asus, Izhari Izmi Mazali, Anuar Abu Bakar, Mohd Kameil Abdul Hamid
{"title":"Li-NMC Temperature Modelling Based on Realistic Internal Resistance","authors":"Muhammad Fikri Irsyad Mat Razi, Zul Hilmi Che Daud, Zainab Asus, Izhari Izmi Mazali, Anuar Abu Bakar, Mohd Kameil Abdul Hamid","doi":"10.37934/cfdl.16.12.140148","DOIUrl":null,"url":null,"abstract":"Lithium-ion battery (LIB) produce heat when it is put under charging and discharging process. The heat generated during charging and discharging are directly related to the internal in the battery. This heat generation will cause the battery temperature to rise. The operating temperature for LIB is significantly important because its affect the performance and health of the battery. Gathering battery thermal behavior through experiment is a time consuming, high cost and a fussy process. The process can be made easier through battery thermal modelling. The purpose of this study is to provide a thermal battery model that can predict the battery thermal behavior at wide range of temperature by using realistic internal resistance value from experiment. In this study, a Nickel-Manganese-Cobalt Lithium-ion battery with capacity 40 Ah was discharged with 120 A (3C) and 160 A (4C) current continuously to heat up the battery until a set of targeted temperature achieved. The battery is then discharged with 40 A (1C) pulse current, and the voltage response is measured. The process was repeated until 80°C. From the voltage response data, the internal resistance for the battery was calculated and used as the main input in the thermal model based on heat generation equation to predict the battery temperature. The result shows that the developed thermal model managed to precisely predict battery thermal behaviour with a low average relative error of around 0.634 % to 5.244%. The significance of this study is to provide a battery model that can predict battery thermal behavior precisely at wide range of temperature. This information is important in designing a better battery management system (BMS) to prolong the battery lifetime, slowing degradation rate and avoid safety risk.","PeriodicalId":9736,"journal":{"name":"CFD Letters","volume":"07 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CFD Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/cfdl.16.12.140148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
Abstract
Lithium-ion battery (LIB) produce heat when it is put under charging and discharging process. The heat generated during charging and discharging are directly related to the internal in the battery. This heat generation will cause the battery temperature to rise. The operating temperature for LIB is significantly important because its affect the performance and health of the battery. Gathering battery thermal behavior through experiment is a time consuming, high cost and a fussy process. The process can be made easier through battery thermal modelling. The purpose of this study is to provide a thermal battery model that can predict the battery thermal behavior at wide range of temperature by using realistic internal resistance value from experiment. In this study, a Nickel-Manganese-Cobalt Lithium-ion battery with capacity 40 Ah was discharged with 120 A (3C) and 160 A (4C) current continuously to heat up the battery until a set of targeted temperature achieved. The battery is then discharged with 40 A (1C) pulse current, and the voltage response is measured. The process was repeated until 80°C. From the voltage response data, the internal resistance for the battery was calculated and used as the main input in the thermal model based on heat generation equation to predict the battery temperature. The result shows that the developed thermal model managed to precisely predict battery thermal behaviour with a low average relative error of around 0.634 % to 5.244%. The significance of this study is to provide a battery model that can predict battery thermal behavior precisely at wide range of temperature. This information is important in designing a better battery management system (BMS) to prolong the battery lifetime, slowing degradation rate and avoid safety risk.