Optimal experimental design for large-scale inverse problems for battery models via PDE-Constrained optimization

Abstract Accurate parameter dependent electro-chemical numerical models for lithium-ion batteries are essential in industrial application. However, some parameters of each battery cell are unknown, so that a parameter estimation is necessary to infer them. The field of optimal input/experimental design deals with creating an optimal experimental settings facilitating the estimation problem. Here we apply two different input design algorithms that aim at maximizing the observability of the true, unknown parameters. As the design algorithms are built independent of the model, the same results and motivation are applicable to more complex battery cell models and, moreover, to other applications.