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Thermal behavior analysis of a pouch type Li[Ni0.7Co0.15Mn0.15]O2-based lithium-ion battery

Thermal behavior analysis of a pouch type Li[Ni_(0.7)Co_(0.15)Mn_(0.15)]O_2-based lithium-ion battery

作     者:Feng-Ling Yun Ling Tang Wen-Cheng Li Wei-Ren Jin Jing Pang Shi-Gang Lu 

作者机构:General Research Institute for Nonferrous MetalslR&D Center for Vehicle Battery and Energy StorageBeijing 100088China 

出 版 物:《Rare Metals》 (稀有金属(英文版))

年 卷 期:2016年第35卷第4期

页      面:309-319页

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 0806[工学-冶金工程] 0805[工学-材料科学与工程(可授工学、理学学位)] 0703[理学-化学] 0702[理学-物理学] 

基  金:financially supported by the Program from Ministry of Science and Technology of China(No.2011AA11A254) the National High Technology Research and Development Program of China(No.2012AA110102) 

主  题:Lithium-ionLi[Ni0.7Co0.15Mn0.15]O2 battery Thermal behavior High specific energy 

摘      要:Since lithium-ion battery with high energy density is the key component for next-generation electrical vehicles, a full understanding of its thermal behaviors at different discharge rates is quite important for the design and thermal management of lithium-ion batteries (LIBs) pack/module. In this work, a 25 Ah pouch type Li[Ni0.7 Co0.15Mn0.15]O2/graphite LIBs with specific energy of 200 Wh.kg-1 were designed to investigate their thermal behaviors, including temperature distribution, heat generation rate, heat capacity and heat transfer coefficient with environment. Results show that the temperature increment of the charged pouch batteries strongly depends on the discharge rate and depth of discharge. The heat generation rate is mainly influenced by the irreversible heat effect, while the reversible heat is important at all discharge rates and contributes much to the middle evolution of the tem- perature during discharge, especially at low rate. Subse- quently, a prediction model with lumped parameters was used to estimate the temperature evolution at different discharge rates of LIBs. The predicted results match well with the experimental results at all discharge rates. Therefore, the thermal model is suitable to predict the average temperature for the large-scale batteries under normal operating conditions.

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