LiCoO_2电池正极微结构模拟退火重构及传输物性预测

采用实验或数值方法对多孔复合电极微结构进行重构和特征化不仅是锂离子电池介观尺度数值模型的重要组成部分,也是通过数值技术由底向上进行电极微结构虚拟设计与优化的基础.本文以某商用LiCoO2电池正极的孔隙率、电极组成材料的组分体积分数、活性材料颗粒粒径分布、相关函数等重要结构与统计信息作为输入参数,采用模拟退火法对其微结构进行了数值重建,得到了明确区分活性材料、固体添加物以及孔相(电解液)的微结构,其重要特性参数与实际电极一致.对重构电极的特征化分析,得到了电极内部各组分的连通性、孔径分布等特征信息.同时,采用D3Q15格子Boltzmann模型计算了重构电极的有效热导率以及电解液(或固相)的有效传输系数.与随机行走模拟或Bruggemann等经验公式相比,基于实际电极微结构细节信息的介观数值方法对多孔电极有效传输系数的预测更为准确可靠.

Simulated annealing reconstruction of LiCoO2 cathode microstructure and prediction of its effective transport properties

Reconstruction and characterization of the porous composite electrode via experimental and numerical approaches is one of the most important ingredients of mesoscopic modeling. It is also the basis and prerequisite for bottom-to-up design and optimization of electrode microstructure. In the present work, a simulated annealing approach is employed to reconstruct the LiCoO₂ cathode of a commercial Li-ion battery. Important statistical characteristic parameters of the real LiCoO₂ cathode, such as porosity or component volume fraction, the real size distribution curve of LiCoO₂ particles, which are taken from experimental data or extracted from the source materials used to fabricate the cathode, are used to regulate the reconstruction process. The reconstructed electrode evidently distinguishes the three individual phases: LiCoO₂ as active material, pores or electrolyte, and additives. An extensive characterization is subsequently performed, which calculates some important structural and transport properties, including the geometrical connectivity of an individual phase, the specific surface area, etc. Particularly, a self-developed D3Q15 LB (lattice Boltzmann) model is utilized to calculate the effective thermal (or electric) conductivity and the effective species diffusivity in electrolyte (or solid) phase, and the tortuosity of an individual phase. The LB model predictions indicate that the effective transport coefficients are closely related to the micro-morphology in electrodes and the tortuosity values assessed by LBM are more reliable than those predicted by random walk simulation or the Bruggeman equation.