Learning Battery Physics from Images

Explore cutting-edge research on lithium-ion battery materials through advanced synchrotron light sources and data-driven physics discovery. Delve into the combination of electrochemical models, PDE-constrained optimization, and Bayesian inference to extract hidden information from experimental datasets. Examine in-situ scanning transmission x-ray microscopy (STXM) images of reactive lithium iron phosphate (LFP) nanoparticles, correlative imaging of lithium concentration and strain maps in relaxed LFP particles, and in-operando X-ray diffraction data of lithium transition metal oxide. Discover how researchers extract thermodynamic and reaction kinetic models, spatial heterogeneity, and chemo-mechanical coupling from these data streams. Gain insights into the full utilization of datasets and the extraction of difficult-to-measure physical quantities, advancing understanding of battery physics and informing the engineering of high-performance materials.

Hongbo Zhao - Learning Battery Physics from Images