A Framework based on Population Balance Modeling for Predicting Li–O2 Battery Discharge and Life Cycle Behavior

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Original abstract

The growing integration of renewable energy sources such as solar and wind power has intensified the demand for advanced energy storage technologies. Lithium–air (Li–O2) batteries are particularly attractive due to their exceptionally high theoretical specific energy, which surpasses that of the conventional lithium-ion system. However, their practical application is hindered by poor reversibility during discharge, primarily due to the formation and decomposition of lithium peroxide (Li2O2), which causes cathode passivation and capacity fading. Since the electrochemical performance of Li–O2 batteries is strongly influenced by the morphology, size, and spatial distribution of Li2O2 crystals, understanding the mechanisms governing their nucleation and growth is critical. To address this challenge, this work proposes a computational framework based on population balance modeling (PBM) to describe Li2O2 crystallization dynamics during battery discharge. The framework integrates population, mass, and energy balances, allowing the coupled analysis of electrochemical kinetics, supersaturation effects, and the evolution of crystal size distributions. Compared with continuum-scale and phase-field models, the PBM approach offers reduced computational cost while naturally accounting for particle size distributions and linking microscopic crystallization phenomena to macroscopic battery performance and degradation. Although simplified assumptions were adopted in this initial formulation, the framework successfully captures the essential discharge behavior of Li–O2 systems. As such, it provides a robust foundation for future model refinements incorporating additional physicochemical mechanisms and more detailed electrochemical and transport phenomena.

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