Degradation analysis and health monitoring projects
Project 3 - Fuel Cell Dynamic Reliability Assessment
Tackling climate change is arguably the biggest challenge humanity faces in 21st century. Rising average global temperatures threaten to destabilize the fragile ecosystem of the Earth and bring unprecedented changes to human lives if nothing is done to prevent it. This phenomenon is caused by the anthropogenic greenhouse effect due to the increasing atmospheric concentrations of carbon dioxide (CO2). One way to avert the disaster is to drastically reduce the consumption of fossil fuels in all spheres of human activities, including transportation. To do this, research and development of the electric vehicles (EVs) to make them more efficient, reliable and accessible is essential.
There are different types of EVs exist, but the focus of this research project is the Fuel Cell Electric Vehicles (FCEVs). FCEVs use a device called Polymer Electrolyte Membrane Fuel Cell (PEMFC) to generate the power on-board the vehicle by an electrochemical process that combines hydrogen and oxygen gases. The primary product of such reaction is useful electricity and the by-products are water vapour and heat. Consequently, fuel cells offer a zero-emissions solution for the transportation industry.
Unfortunately, current generation of PEMFCs faces a number of issues that prevent them from large-scale market adoption. These issues include high costs, absence of hydrogen fuelling infrastructure and reliability concerns. This research project is focused on the modelling methods for PEMFCs that would reveal the potential configurations improvements and hardware optimizations for the improved reliability metrics. During this project a novel model for dynamic reliability analysis of a PEM fuel cell system is developed using Modelica language. The model takes into account for multi-state dynamics and aging of system components. This is achieved through the combination of physical and stochastic sub-models with shared variables. The physical model consist of deterministic calculations of the system state described by variables such as temperature, pressure, mass flow rates and voltage output. Additionally, estimated component degradation rates are also taken into account. The non-deterministic sub-model, on the other hand, is implemented with stochastic Petri nets which represent different events that can occur at random times during fuel cell lifetime. The hybrid nature of the resulting model makes it possible to gather statistical information of most probable lifetime scenarios of the system with given starting operational parameters. The analysis of this statistics provides the tools to identify the variables that can be adjusted in order to improve the overall lifetime performance of the PEMFCs.