AMaSiS 2021 - Abstract

Krewer, Ulrike

Reaction kinetic modeling of electrochemical (energy) cells

Karlsruher Institut für Technologie, Germany

Our future sustainable energy systems rely on electrochemical cells, such as fuel cells, batteries and electrolysers. Only few cell technologies so far succeeded to enter mass market. This can partly be attributed to performance losses due to reaction kinetic issues at the electrodes. Processes at electrodes are highly complex: electrochemical reaction pathways may contain strongly adsorbed species that hamper fast reactant conversion and lead to high overpo-tential losses; further, unwanted side reactions, chemical reactions in the electrolyte or desorption of intermediates may cause low efficiencies and complex dynamic or hysteresis behavior. In addition, surface changes such as dissolution, restructuring, changes in oxidation state or other degradation phenomena, are frequently found in electrodes. Qualitative and especially quantitative understanding of the reactions at the electrode surface is thus an important key to improving cell performance and durability, and to identify optimal material and operating conditions. Further, models that reproduce the observed phenomena allow for a knowledge-driven design and improvement of performance of electrochemical cells. Focus of this talk is on model-assisted analysis and identification of reaction kinetics in electrochemical cells. Besides complex multistep reactions, the role of adsorbates, local operating conditions and transport, side reactions and surface changes of electrodes will be elucidated. Examples cover established technologies like PEM electrolysis and Li-ion batteries, and electrodes of next generation cells. Kinetic Monte Carlo methods complement continuum-type microkinetic and microkinetic models. The combination of mechanistic modelling and dynamic electrochemical measurements is shown to yield not only a better quantitative and qualitative understanding of electrode performance, but to be the base for a knowledge-driven, model-based electrode and cell development.