Optimization of LiMn2O4 electrode properties in a gradient- and surrogate-based framework

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• 作者：Wenbo Du (139)
  Nansi Xue (139)
  Amit Gupta (139) (239)
  Ann M. Sastry (339)
  Joaquim R. R. A. Martins (139)
  Wei Shyy (139) (439)
  
• 关键词：Lithium ; ion battery ; Optimization ; Surrogate modeling ; Porous electrode model
• 刊名：Acta Mechanica Sinica
• 出版年：2013
• 出版时间：June 2013
• 年：2013
• 卷：29
• 期：3
• 页码：335-347
• 全文大小：864KB
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• 作者单位：Wenbo Du (139)
  Nansi Xue (139)
  Amit Gupta (139) (239)
  Ann M. Sastry (339)
  Joaquim R. R. A. Martins (139)
  Wei Shyy (139) (439)
  
  139. Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA
  239. Department of Mechanical Engineering, Indian Institute of Technology, New Delhi, India
  339. Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA
  439. Department of Mechanical Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
  

文摘

In this study, the effects of discharge rate and LiMn2O4 cathode properties (thickness, porosity, particle size, and solid-state diffusivity and conductivity) on the gravimetric energy and power density of a lithium-ion battery cell are analyzed simultaneously using a cell-level model. Surrogate-based analysis tools are applied to simulation data to construct educed-order models, which are in turn used to perform global sensitivity analysis to compare the relative importance of cathode properties. Based on these results, the cell is then optimized for several distinct physical scenarios using gradient-based methods. The complementary nature of the gradient- and surrogate-based tools is demonstrated by establishing proper bounds and constraints with the surrogate model, and then obtaining accurate optimized solutions with the gradient-based optimizer. These optimal solutions enable the quantification of the tradeoffs between energy and power density, and the effect of optimizing the electrode thickness and porosity. In conjunction with known guidelines, the numerical optimization framework developed herein can be applied directly to cell and pack design.