Electrochemical Linked to Mechanical Simulation for an Assessment of State-of-Health of Thin-Flexible Li-Ion Batteries on Dynamic Flexing and Calendar Aging

Flexible hybrid electronics featuring wearable electronics offer numerous advantages such as function integration, light-weighting, and flexibility. However, the dynamic flexing of the flexible power sources during usage, along with flex-to-install, presents challenges for their durability. While previous research has focused on thick block batteries, the effects of daily motion-induced stresses on the state of health (SOH) degradation of thin-flexible batteries, in conjunction with usage parameters, are not well understood. Factors such as storage duration, operating temperature, flexing frequency, interval, and flex radius may vary, making it impractical and expensive to measure the battery response in every condition. Therefore, electrochemical simulation methods are necessary to predict the SOH degradation of the battery under various environmental conditions, which can assess conditions not previously measured. However, the degradation of the flexible battery is not only due to electrochemical aging but also due to mechanical aging. While electrochemical simulation is well-known, the effect of mechanical factors on degradation is relatively unknown. In this regard, this research seeks to make multiphysics simulations of SOH deterioration during charging/discharging of a flexible battery under dynamic folding, twisting, and static folding using a calendar-aged battery at elevated temperatures. Additionally, the method, which is to link the mechanical simulation to electrochemical simulation, was studied, which may be helpful in further understanding of unknown effects required for future study. The paper thoroughly discusses the developed model's capability to predict SOH degradation caused by mechanical stress and calendar aging. It also explores how accurately the model can illustrate degradation trends under various environmental conditions. The detailed results and their significance are presented comprehensively, providing a clear understanding of the model's effectiveness within the context of the study.