We propose a novel mathematical model for predicting the effective modulus of Si electrodes in Li-ion batteries by considering the large volume expansion of Si during lithiation and porosity variation, as well as the influence of change in Li-ion concentration and Poisson’s ratio, assuming the Si electrode as a particulate composite material. Though previous studies considered the Li-ion concentration and Poisson’s ratio in their models, they rarely considered the porosity and a large change in the volume of silicon, which degrade the performance of Liion batteries with Si electrodes. The proposed model is formulated on the basis of a threephase particulate composite material composed of silicon particles, pores, and binders. Through parametric studies, it is found that the Li-ion concentration in silicon increases nonlinearly and the Poisson’s ratio decreases during charging, regardless of the structure (crystalline or amorphous) of the silicon particles. The effective modulus of the three-phase particulate composite electrode decreases during charging as a result of the changes in the Li-ion concentration, Si and binder volumes, pores, and Poisson’s ratio. The accuracy of the developed model is validated by comparing the predicted moduli of this model with other 3 experimental data and 2 model predictions.

考虑到锂在锂化过程中的大体积膨胀和孔隙率变化以及锂离子浓度和泊松比变化的影响，我们提出了一种新的数学模型来预测锂离子电池中硅电极的有效模量Si电极作为颗粒复合材料。尽管先前的研究在模型中考虑了锂离子浓度和泊松比，但他们很少考虑孔隙率和硅体积的较大变化，这会降低带有硅电极的锂离子电池的性能。所提出的模型是基于由硅颗粒，孔和粘合剂组成的三相颗粒复合材料制定的。通过参数研究 发现在充电期间，硅中的锂离子浓度非线性地增加并且泊松比降低，而与硅颗粒的结构（晶体或非晶态）无关。由于锂离子浓度，硅和粘合剂体积，孔和泊松比的变化，三相颗粒复合电极的有效模量在充电过程中降低。通过将该模型的预测模数与其他3个实验数据和2个模型预测值进行比较，可以验证所开发模型的准确性。孔和泊松比。通过将该模型的预测模数与其他3个实验数据和2个模型预测值进行比较，可以验证所开发模型的准确性。孔和泊松比。通过将该模型的预测模数与其他3个实验数据和2个模型预测值进行比较，可以验证所开发模型的准确性。