Complex dynamical systems such as energy storage systems (ESS) have high-order models, which are costly to develop as they require the tedious task of determining the material and physical parameters of the system. We aim to mitigate these shortcomings by developing data-driven models of such systems from the input/output response data. We utilize tools from subspace identification, sparsity promoting regularization, and switching systems theory to determine the optimal time-varying data-driven models. Further, we develop Koopman operators’ modeling techniques and extend them to control ESS.

Publications:

Ahmadzadeh, O., Rodriguez, R., and Soudbakhsh, D. “Modelling of Li-ion Batteries for Real-Time Analysis: A Data-Driven Approach”, American Control Conference, 2022.