Development of a Dynamic Model of a PEMFC Stack System in Modelica Language

This thesis work has the main objective to develop and validate a dynamic model for a PEMFC stack system by using Modelica language, model which will represent the starting point for some future work in charge of Fondazione Bruno Kessler (FBK) within the framework of the European project CLEANER. After a brief introduction where statistics and data about today and future energy demand, renewable energy exploitation and emissions are presented along with some applications of hydrogen-based solutions and their providers, the discussion proceeds with a general description about the working principles of a fuel cell and an in-depth analysis of proton exchange membrane fuel cells. A dedicated section focuses on project CLEANER, in particular its targets and how to pursue them. The core of this discussion embeds the dynamic PEMFC stack system model which has been implemented by following a semi-empirical approach, according to a plentiful collection of references in literature, which have been carefully reviewed. The main character is represented by the cell model, to which the most time-consuming modeling efforts have been addressed. After a calibration against experimental data provided by PowerCell Sweden AB in the form of a polarization curve for a given set of operating conditions, the model has been correctly validated, with a maximum error below 3.5%. Some sensitivity analyses have been performed to evaluate the fuel cell dynamic response to different operating conditions, with some limitations due to unavailability of additional experimental results. Modelon Impact is the Modelica-based tool through which the model has been implemented. This work constitutes only the first piece of a future complete dynamic model of a PEMFC stack system able to run on low-grade hydrogen, which will be integrated with balance of plant, control system and diagnostic and prognostic capabilities in predicting the effect of contaminants, present in the fuel with a concentration up to 5%, on fuel cell performances and lifetime thanks to the introduction of a degradation data-driven model.