Statistical physics-based modeling and simulation of chemical and mechanical degradation in lithium ion batteries

Lithium ion batteries undergo chemical and mechanical degradation during operation. The main chemical degradation mechanism is the growth of the solid electrolyte interphase (SEI) in the negative electrode of lithium ion batteries. The growth of the SEI layer causes a loss of lithium ions that induces capacity fade. In addition, it increases the ion transport resistance and decreases the total porosity. Mechanical degradation includes nucleation of nano-cracks and their growth caused by the impact of diffusion- induced stress during Li-ion intercalation. Particle agglomeration and breakage are other mechanical effects that contribute to morphological changes. This thesis presents a physical-statistical model of chemical and mechanical degradations in the negative electrode of lithium ion batteries. The model employs a population balance formalism based on the Fokker-Planck equation to describe the propagation of the particle density distribution function in the electrode. Structure-transforming processes at the level of individual particles are accounted for by using a set of kinetic and transport equations. These processes alter the particle density distribution function, and cause changes in battery performance. The population balance model is integrated into porous electrode theory to study the temporal evolution of the particle density across the electrode thickness. A parametric study of the model singles out the first moment of the initial SEI thickness distribution as the determining factor in predicting the capacity fade due to chemical degradation. Another parametric study reveals the population of small particles and the width of the initial particle size distribution as the main parameters that determine changes in electrochemical performance and capacity fade due to chemical and mechanical degradation. The model-based treatment of experimental data allows analyzing processes that control SEI growth, crack growth, particle breakage, and particle agglomeration and extracting their controlling parameters. The model is applied to experimental data in order to isolate and quantify the impact of different degradation mechanisms