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Set-Valued and Stochastic Approaches for the Online Identification of the Open-Circuit Voltage of Lithium-Ion Batteries
The charging/discharging dynamics of Lithium-ion batteries can be approximated by using equivalent circuit models. These models consist of a finite number of RC sub-networks as well as series resistances and a state of charge dependent voltage source. In classical state estimation approaches, the parameters are identified experimentally. But they are subject to aging- and temperature-induced variations. The aging of battery cells leads to a loss of the total capacity, an increasing Ohmic cell resistance and changes in the charging/discharging efficiency as well as changes of the time constants. Additionally, there are other influence factors such as the cell temperature. The first-mentioned variations can be estimated with the help of an augmented state vector, but this approach does not allow for estimating nonlinear dependencies of the circuit elements on the state of charge or other influence factors.
In this talk a two-stage set-valued identification approach and a two-stage stochastic identification approach are presented to identify the nonlinear dependency of the open-circuit voltage on the state of charge.
Marit Lahme received her Master of Science degree in Mechatronics from the Technische Universität Ilmenau, Germany, in 2018. She is now a doctoral candidate within the group “Distributed Control in Interconnected Systems” at the Carl von Ossietzky Universität Oldenburg, Germany. Her research interests include modeling of dynamic systems as well as state and parameter estimation.