Development of a low-order model of Li-Ion cell using machine learning techniques
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Due to multiscale character covering space distances from 10-6 up to 100 m of electrochemical cells and batteries, their mathematical modelling covering all aspects from chemical, electrical up to thermal problems are extremely demanding [1]. Number of mathematical approaches are available in literature but coupled chemical, electric and thermal computations of cells and batteries are very time consuming and therefore impossible to be used for optimisation or dynamic control [2-4]. The paper presents development of the low order time dependent model of electrochemical cell using machine learning techniques. In the first step an unsteady three-dimensional coupled thermal and electrochemical model of a cell was developed. This full scale model was validated against experimental data, and then used to build low order model that is capable of predicting crucial cell parameters. The work was supported by the Silesian University of Technology through the Rector’s grant number 08/060/SDU/10-07-03 RGP24.
