Session: 03-11 Energy Transition

Paper Number: 124809

124809 - A Fitting Process for the Optimal Modelling of an Anion Exchange Membrane (AEM) Electrolyser 

Empirical and semi-empirical numerical models of different kinds of electrolysers and fuel cells are widely used nowadays to predict their behaviour. The development of a numerical model and its validation through experimental data allow to study how the performance of the analysed technology changes; regarding the previously mentioned technologies, it consists on the analysis of polarization and efficiency curves, as well as both pressure and temperature dependency. This means that, with a few experimental tests, it is possible to forecast the cell behaviour at different operating conditions correctly (e.g., cell pressure, cell temperature, gas composition, etc.) without performing any additional experimental test, thus saving a considerable amount of time.

Despite of their high reliability, numerical models do not always resemble the system performance properly. This is due to the lack of knowledge of important parameters that are required to model the technology, that is, the exchange current density, the charge transfer coefficient, and the activation energy of both anode and cathode electrodes. Indeed, these parameters are difficult to find in the scientific literature, and they are barely available from the manufacturers of electrolysers and fuel cells, except in terms of numerical ranges.  

That said, this paper aims at providing important information on how these right parameters can be easily obtained through a fitting process applied to a semi-empirical model, which has been developed in the Python environment, for resembling the behaviour of an Anion Exchange Membrane (AEM) electrolyser.

Presenting Author: Francesca Mennilli Università Politecnica Delle Marche, Ancona

Presenting Author Biography: Francesca Mennilli is a second year PhD student in Energy Systems. Her PhD is focused on the utilisation of hydrogen as an energy vector to decarbonise the energy system. She is currently dealing with the development of numerical models on Python to resemble the behaviour of green hydrogen technologies, that is, electrolyser and fuel cells of different kinds. The models developed can be then integrated in more complex scenarios, such as hard-to-abate sectors and coupling with renewable sources, to find the optimal configuration that guarantees the best performance of the overall system.

Authors:

Francesca Mennilli Università Politecnica Delle Marche, Ancona
Lingkang Jin Università Politecnica Delle Marche
Mosè Rossi Università Politecnica Delle Marche
Gabriele Comodi Università Politecnica Delle Marche