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Renewable energy researchers use computer simulation to aid the design oflithium ion storage devices. The underlying models contain several physicalinput parameters that affect model predictions. Effective design and analysismust understand the sensitivity of model predictions to changes in modelparameters, but global sensitivity analyses become increasingly challenging asthe number of input parameters increases. Active subspaces are part of anemerging set of tools for discovering and exploiting low-dimensional structuresin the map from high-dimensional inputs to model outputs. We extend linear andquadratic model-based heuristic for active sub- space discovery totime-dependent processes and apply the resulting technique to a lithium ionbattery model. The results reveal low-dimensional structure and sensitivitymetrics that a designer may exploit to study the relationship betweenparameters and predictions.

Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model