Multiscale Modeling and Machine Learning-Driven Characterization of Degradation Mechanism in Li-Ion Battery Materials

Selis L.A.; Galvez-Aranda D.E. · Advanced Materials and Chemical Engineering (2026) · DOI: 10.4018/979-8-3373-4402-7.ch005

Resumen

Degradation in lithium-ion batteries (LIBs) arises from the coupling of phenomena that span multiple length scales from atomic-scale interatomic interactions to micrometer-scale particle morphologies and millimeter-scale electrode structures and time scales from femtosecond electronic rearrangements to year-long calendar aging. No single modeling or experimental technique is able to capture the entire spectrum. For example, bond breaking, defect migration and interfacial reactions occur at the electronic/atomic scale, while particle cracking, pore clogging, and electrolyte depletion emerge atthe mesoscale, andcapacity fade or impedance growth manifest at the cell/device scale. This inherently multiscale and multimodal nature of degradation demands an integrated modeling framework combining quantum mechanical, atomistic, mesoscopic, and continuum approaches. Copyright

Instituciones: Universidad Nacional de Ingeniería