Proposal of a Data-Driven Surrogate Models with the Aim to Estimate the Mechanical Degradation of Cathodes in Lithium-Ion Batteries
Authors: Erhardt Nikolai , Nirschl Hermann 
Published: December 2024

Battery aging, a complex process crucial to the longevity and effectiveness of lithium-ion batteries, relies on the intricate interplay between electrochemical reactions and structural changes within battery electrodes. An essential element of battery aging involves the deterioration of electrode materials, which occurs due to the mechanical stresses induced during the insertion and extraction of ions within the active material particles.
Ion intercalation, which governs the charging and discharging behavior of lithium-ion batteries, creates concentration variations within the active material particles. These variations, affected by transport processes and kinetics, lead to mechanical stresses as ions are spatial confined within the particles. These stresses are a cause of electrode degradation, resulting in particle fractures and the deterioration of electrode integrity.
Understanding electrode degradation requires considering phase transitions experienced by common cathode materials like Lithium Nickel Manganese Cobalt Oxide (NMC) or Lithium Iron Phosphate (LFP) during ion intercalation. The transition between lithium-poor and lithium-rich phases, especially in the transition zone, creates significant mechanical stresses due to concentration differences. These stresses often exceed the material's resilience, causing particle fractures and impairing electrode functionality.
Despite advances in computational techniques, accurately simulating these complex processes remains challenging due to the computational resources required. This has led researchers to explore innovative methods such as data-driven modeling, including physics-informed neural networks (PINNs). Here the PINN integrate the transport equation into the training algorithm to predict concentration profiles within active material particles, which lead to a improved accuracy of the concentration estimation inside the particle without computational bottlenecks. The proposed PINN possess a novel design, to precisely solve the moving boundary Stefan-Problem of the active material in phase transition.
Classical neural networks (ANNs) are also valuable for estimating the electrical potential of active material particles, using results from existingmicrostructural simulations to develop models capturing electrochemical-mechanical interactions within batteries.
Furthermore, to understand the extent of particle fracture and its impact on electrode performance, a phase-field-like damage model is integrated into the grey-box model. The governing equations are derived from the stationary point of the potential energy functional of the active particle system.
The aim is to establish an operational range resilient to battery aging by systematically studying mechanical stresses and degradation under various operating conditions. This approach, combining experimental data, computational simulations, and theoretical frameworks, promises to advance our understanding of battery aging and drive the development of durable battery technologies