Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical and Aerospace Engineering

Committee Chair

George Nelson

Committee Member

Kavan Hazeli


Primary batteries--Testing--Mathematical models, Bayesian statistical decision theory


The growing use of batteries in vehicular applications and its associated risk of crashes that develop large deformations has raised the salience of how mechanical loads can lead to battery failure and cascade to battery pack fires. Batteries are complicated devices containing many materials with complex behaviors, making calibration of mechanical simulations an involved process. There are many techniques in the inverse problem field which could complement existing model tuning procedures through reducing the required number of experimental tests by extracting more information out of each data set and identifying which observations can most improve the model parameters at the margin. This thesis applies a Bayesian Model Calibration (BMC) approach to the calibration of the plasticity and damage behaviors for an LR61 alkaline battery model. BMC produces both a tuned parameter set and an uncertainty quantification (UQ) describing how likely that parameter set is to be the best estimate. The calibrated LR61 simulation produced in this work accurately reflects the experimentally observed deformation behavior and illustrates how the BMC process can be a useful tool in furthering mechanical battery simulation development.



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