eprintid: 10193647
rev_number: 13
eprint_status: archive
userid: 699
dir: disk0/10/19/36/47
datestamp: 2024-10-17 11:09:40
lastmod: 2024-10-17 11:09:40
status_changed: 2024-10-17 11:09:40
type: thesis
metadata_visibility: show
sword_depositor: 699
creators_name: Zhou, Shangwei
title: Advanced Diagnostics of Polymer Electrolyte Fuel Cells
ispublished: unpub
divisions: UCL
divisions: B04
divisions: C05
divisions: F43
note: Copyright © The Author 2024.  Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).  Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.  Access may initially be restricted at the author’s request.
abstract: Polymer electrolyte fuel cells (PEFCs) are considered one of the most sustainable substitutions for combustion engines in the drive towards a net-zero society, with the potential to achieve high efficiency, environmentally friendly operation (water is the only byproduct) and independence from fossil fuels when operated with green hydrogen. However, manufacturing cost and durability limit the commercialisation process of PEFCs. Due to the variety of operational strategies, cell designs and materials, fuel cells exhibit complex hydro-electro-thermal characteristics. Moreover, as the core component of a PEFC system, the stack needs to be operated under narrow conditions. Deviating the operating conditions from the optimum will accelerate the degradation and cause system downtime. A comprehensive understanding of the coupled hydro-electro-thermal phenomenon is required with advanced diagnostic techniques.
This thesis integrates diverse methodologies to enhance the efficiency and durability of PEFCs. Neutron imaging's precision in water distribution mapping is combined with the widely-used electrochemical impedance spectroscopy (EIS), aiming to realise on-board, cost-effective water content estimation in fuel cell stacks. The necessity for real-time diagnosis in ensuring PEFC system safety is addressed through a novel approach utilising AC voltage response and the 1D convolutional neural network (CNN) for rapid water management diagnosis, overcoming limitations of time-consuming and steady-state conditions. Local inconsistencies in PEFC performance due to uneven reactant and heat distribution are examined through combined thermal imaging and simultaneous multi-channel EIS, providing insights into temperature distribution and individual impedance in a commercial open-cathode multiple-cell module.
Furthermore, the effects of local heating on voltage by mapping current density distribution across the active area are explored, offering practical guidance for optimal thermal management to achieve uniform reactions through the heterogeneous design. Collectively, these integrated studies contribute to a comprehensive understanding of diagnostic and performance optimisation strategies for PEFCs, with broader implications for advancing the age of electrochemical power.
date: 2024-06-28
date_type: published
full_text_type: other
thesis_class: doctoral_embargoed
thesis_award: Ph.D
language: eng
verified: verified_manual
elements_id: 2287437
lyricists_name: Zhou, Shangwei
lyricists_id: SZHOB52
actors_name: Zhou, Shangwei
actors_id: SZHOB52
actors_role: owner
full_text_status: restricted
pagerange: 1-181
pages: 181
institution: UCL (University College London)
department: Chemical Engineering
thesis_type: Doctoral
citation:        Zhou, Shangwei;      (2024)    Advanced Diagnostics of Polymer Electrolyte Fuel Cells.                   Doctoral thesis  (Ph.D), UCL (University College London).    
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10193647/13/Zhou_10193647_thesis.pdf