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Analysis of the Thermal Conductivity of Lithium ion battery Cathode Materials Through Hybrid Callaway Defect Model and Artificial Neural Network
REN Fei 1,DAI Yang 2,WANG Huiqiong 3,SHUAI Jianwei 1,ZHENG Jin-Cheng 4 * #
1.Department of Physics, Xiamen University, Xiamen 361005, China
2.Department of Chemical Engineering, School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
3.Department of Physics, Xiamen University, Xiamen 361005, China;Xiamen University Malaysia Campus, 439000 Sepang, Selangor, Malaysia
4.Department of Physics, Xiamen University, Xiamen 361005, China;Xiamen University Malaysia Campus, 439000 Sepang, Selangor, Malaysia;Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen 361005, China
*Correspondence author
#Submitted by
Subject:
Funding: National High-tech R&D Program of China (863 Program)（No.2014AA052202）, Shanghai Science and Technology Talent Program （No.12XD1421900）, Specialized Research Fund for the Doctoral Program of Higher Education （No.20120121110021）, National Natural Science Foundation of China （No.21103109, 21373137, U1332105）
Opened online: 6 June 2016
Accepted by: none
Citation: REN Fei,DAI Yang,WANG Huiqiong.Analysis of the Thermal Conductivity of Lithium ion battery Cathode Materials Through Hybrid Callaway Defect Model and Artificial Neural Network[OL]. [ 6 June 2016] http://en.paper.edu.cn/en_releasepaper/content/4694510
 
 
Thermal conductivity of cathode materials plays a paramount role in the safety and longevity in both current and next generation batteries. In this paper, we develop a modified Callaway defect model for understanding the dependence of the thermal conductivity on lithiation of the intercalation cathode materials. By recasting the Callaway model with hybrid defect scattering and performing numerical calculations, the physic picture of phonon scatterings on lithiation process of lithium cathode material are clearly illustrated and revealed. The model results are in excellent agreement with the experimental data of LiCoO2. What's more, we utilize a BP artificial neural network to analyze the experimental data about the heat conductivity for LiCoO2, and build up a model between composition x of LixCoO2 and its thermal conductivity. Our results may be useful to predict the thermal conductivity of layered structure electrode materials and should be helpful for the development of lithium ion battery with safety and longevity.
Keywords:Heat Transfer; Callaway model; Thermal conductivity; Lithium ion battery; Cathode materials; Artificial neural network
 
 
 

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