Substitution of a Microstructure-Simulation with a Data-Driven Approach for Modelling Mechanical Degradation of Electrodes

Abstract

One significant aspect of battery aging involves the mechanical degradation of battery electrodes. The intercalation of ions into the active material particles of a lithium-ion battery creates a concentration profile along the radial direction of the active material particle, influenced by transport properties and kinetics. Mechanical stresses arise due to the spatial constraints of intercalated ions, leading to the mechanical degradation of the electrode.Common cathode materials used in practical applications, such as Lithium Nickel Manganese Cobalt Oxide (NMC) or Lithium Iron Phosphate (LFP), undergo a transition from a lithium-poor phase to a lithium-rich phase during the intercalation process. Along this phase boundary, high mechanical stresses occur due to the inconsistency of the concentration profile, resulting in particle fractures. This can lead to electrical isolation and, consequently, the electrochemical inertization of the active particle material, manifested as a reduced battery capacity on a macroscopic scale.Simulating these processes poses computational challenges. Given current computational power, accurately simulating a sufficient number of cycles for a microstructure simulation, which describes transport processes within a battery, is computationally prohibitive. Therefore, this study employs data-driven models. To predict radial concentration profiles within the particle, a physics-informed neural network (PINN) proves useful by extending the cost function with the transport equation. Classical neural networks (ANN) are utilized to calculate the electrical voltage of active material particles. These models are trained with results from microstructure simulations to develop a grey-box model, enabling the inclusion of mechanical degradation in the electrode simulation.Mechanical stresses and degradations are thus investigated under various galvanostatic discharge rates to define an aging-resistant operational window for the battery.