Composite structural battery design and development via micromechanics simulation and data-driven machine-learning approaches

Abstract

Functionalization of materials holds promise for advancing structural energy storage technologies, such as structural batteries and supercapacitors. In this context, carbon fibers (CFs) serve as reinforcement, active electrodes, and current collectors, while the polymer matrix functions as ion-conduction and load-transfer media. However, the mechanical and electrochemical properties of these constituent materials are inherently intertwined, leading to a trade-off relationship. Carbon Fibers (CFs) can be used as a structural substitute for graphite particles enabling both electrochemical and mechanical characteristics. As an ionic/mechanical load transfer medium, a high ionic conductivity, mechanical strength, and most importantly compatibility with CFs is desired for high-performance structural batteries.

In this dissertation, firstly, a multifunctional epoxy-based Solid Polymer Electrolyte (SPE) was developed via in-situ micro-phase separation during polymerization reaction. The SPE was developed with cure-kinetics approach, and the interfacial shear strength (IFSS) between the CF/SPE was examined. Secondly, robust, high pressure fabrication methods were utilized to fabricate a highly uniform lamina with superior performance. Thirdly, a hybrid electrolyte system was developed, and the thermal stability, compatibility, and flammability were examined, as electrochemical cells were assembled. Fourthly, a Cohesive Zone Model (CZM) is developed to simulate the crack initiation and propagation to describe the interfacial behavior between CF/SPE. The CZM envelopes were calibrated and validated against experimental microdroplet Interfacial Shear Strength (IFSS), enabling the development of a Representative Volume Element (RVE) that can homogeneously describe the stochastic morphology of the structural battery’s cross-section. The RVE can be employed to predict the macro mechanical properties such as the elastic/shear moduli and develop a failure design envelope that can be used as design framework for multifunctional structural battery systems, while to predict and design the battery a machine learning algorithm is conceptualized based on Artificial Neural Network (ANN). The developed methodology offers promising potential suggesting its application in multifunctional energy storage systems. This study provides insights into designing, optimizing, and predicting multifunctional structural composite batteries.