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...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 comple-mentary 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 frame-work developed herein can be applied directly to cell and pack design.展开更多
基金supported by the General Motors and University of Michigan Advanced Battery Coalition for Drivetrains (ABCD)
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 comple-mentary 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 frame-work developed herein can be applied directly to cell and pack design.
