Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

Kristen Susuki, J.S. Chen, Jeff Allen

Research output: NRELPresentation


In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454,, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.
Original languageAmerican English
Number of pages24
StatePublished - 2024

Publication series

NamePresented at the Engineering Mechanics Institute (EMI) Conference, 28-31 May 2024, Chicago, Illinois

NREL Publication Number

  • NREL/PR-2C00-89940


  • electro-chemo-mechanical coupling
  • image-based modeling
  • interface-modified reproducing kernel approximation
  • li-ion battery
  • meshfree method
  • neural network-enhanced reproducing kernel particle method
  • reproducing kernel particle method


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