Modelling and diagnosis of solid oxide fuel cell (SOFC)

The development of mathematical models and numerical simulations is crucial to design improvement, optimization, and control of solid oxide fuel cells (SOFCs). The current study introduces a novel and computationally efficient pseudo-two-dimensional (pseudo-2D) model for simulating a single cell of a high-temperature hydrogen-fueled SOFC. The simplified pseudo-2D model can evaluate the cell polarization curve, species concentrations along the channel, cell temperature, and the current density distribution. The model takes the cell voltage as an input and computes the total current as an output. A full-physics three-dimensional model is then developed in ANSYS Fluent, with a complete step-by-step modeling approach being explained, to study the same cell with the identical operating conditions. The 3D model is validated against the other numerical and experimental studies available in the literature. It is shown that although the pseudo-2D solution converges significantly faster in comparison with the 3D case, the results of both models thoroughly match especially for the case of species distributions. The simplified model was then used to conduct sensitivity analysis of the effects of multi-physiochemical properties of porous electrodes on the polarization curve of the cell. A systematic inverse approach was then used to estimate the mentioned properties by applying the pattern search optimization algorithm to the polarization curve found by the pseudo-2D model. Finally, nine different input parameters of the model were changed to find the hydrogen distribution for each case, and a huge dataset of nearly half a million operating points was generated. The data was successfully employed to design a novel classifier-regressor pair as a virtual hydrogen sensor for online tracking of hydrogen concentration along the cell to avoid fuel starvation

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem

Contributions for microgrids dynamic modelling and operation

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

CFD and neuro-fuzzy modelling of fuel cells

This thesis presents some model developments for the simulation and optimization of the design of fuel cells, in particular for the Solid Oxide Fuel Cell (SOFC) and Proton Exchange Membrane Fuel Cell (PEMFC). However, the approaches and models presented can be basically applied to any type of fuel cell. In this study, the multicomponent diffusion processes in the porous medium of a SOFC anode has been investigated through comparison of the Stefan Maxwell Model, Dusty Gas Model and Binary Friction Model in terms of their prediction performance of the concentration polarization of a SOFC anode to mainly investigate the effect of the Knudsen diffusion on the predictions. The model equations are first solved in 1 D using an in-house code developed in MATLAB. Then the diffusion models have been implemented into COMSOL to obtain 2D and 3D solutions. The model predictions have been evaluated for different parameters and operating condi- tions for an isothermal system and assuming that reaction kinetics are not rate limiting. The results show that the predictions of the models are similar and the differences in the predictions of the models reported previously are mainly due to the definition of the effective diffusion coefficient, i.e. the tortuosity parame- ter, and with a tortuosity parameter fitted for each model, the models that take into account the Knudsen diffusion and that do not predict similar concentration polarization. Moreover, in this research, the application of an Adaptive Neuro- Fuzzy Inference System (ANFIS) to predict the performance of an Intermediate Temperature Solid Oxide Fuel Cell and a Proton Exchange Membrane Fuel Cell (PEMFC) have been presented. The results show that a well trained and tested ANFIS model can be used as a viable tool to predict the performance of the fuel cell under different operational conditions to facilitate the understanding of the combined effect of various operational conditions on the performance of the fuel cell and this can assist in reducing the experimentation and associated costs

Performance prediction of proton exchange membrane fuel cells (PEMFC) using adaptive neuro inference system (ANFIS)

This investigation explored the performance of PEMFC for varying ambient conditions with the aid of an adaptive neuro-fuzzy inference system. The experimental data obtained from the laboratory were initially trained using both the input and output parameters. The model that was trained was then evaluated using an independent variable. The training and testing of the model were then utilized in the prediction of the cell-characteristic performance. The model exhibited a perfect correlation between the predicted and experimental data, and this stipulates that ANFIS can predict characteristic behavior of fuel cell performance with very high accuracy

Thermal Analysis For The Purpose Of Fault Diagnosis of Commercial Proton Exchange Membrane Fuel Cells (PEMFC)

The world\u27s awareness towards the amount of destruction that our extensive and ignorant lifestyles in the past few decades have imposed on the environment is growing day after day. This resulted in an increased governmental and research interest towards the development and use of green technology. Fuel Cells are one of the green technologies that received a major share of research interest in the past decade. However, despite their promising features, Fuel Cell systems still lack a solid fault diagnosis and predictive maintenance study. There are numerous faults that have to be detected and diagnosed on a fuel cell power generator system, ranging from chemical faults, to electrical and power electronics faults such as: reactant leakages inside the Fuel Cell, Fuel Cell flooding and membrane drying out, membrane humidification and reactive gas feeding, the accumulation of nitrogen and/or water in the anode compartment, etc. The aim of this dissertation work is to develop and implement a model based fault diagnostic scheme for a commercial Proton Exchange Membrane Fuel Cell (PEMFC) system in order to improve its safety and reliability; despite the lack of important system information. To achieve this aim, a diagnosis-oriented model of a fuel cell power generator is developed and validated using actual experimental data. Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) were both utilized in the parameter identification of two commercial PEMFC systems\u27 models. GA and PSO were used to extract the correct parameter values of the fuel cell system to minimize differences between experimental and simulated results. Furthermore, PSO was found to outperform GA in the identification process. The effect of severe environmental conditions of hot climate countries such as the UAE on a commercial PEMFC system is then studied and analyzed in simulation using MATLAB/SIMULINK through the developed dynamic system model. The thermal analysis results suggested that the fuel cell system under study would fail to start properly at ambient temperatures of 40°C and higher. Moreover, the faults that may affect the PEM fuel cell system are listed and their severity is analyzed. In a next step, a more comprehensive system model is developed and validated in LMS AMESim software using actual experimental data, and an appropriate fault diagnostic technique using residual generation is developed, tested and validated in LMS AMESim in order to detect and identify five potential abrupt faults, namely: drying of the membrane, flooding of the membrane, air leakage, hydrogen leakage in the supply manifold and cooling system failure. The use of LMS AMESim in the proposed modeling and fault diagnosis approach of this dissertation makes it possible to develop a fault diagnosis oriented model for any commercial PEMFC system despite the lack of crucial system information that are usually considered essential for modeling PEMFCs

PEMFC performance improvement through oxygen starvation prevention, modeling, and diagnosis of hydrogen leakage

Catalyst degradation results in emerging pinholes in Proton Exchange Membrane Fuel Cells (PEMFCs) and subsequently hydrogen leakage. Oxygen starvation resulting from hydrogen leaks is one of the primary life-limiting factors in PEMFCs. Voltage reduces as a result of oxygen starvation, and the cell performance deteriorates. Starved PEMFCs also work as a hydrogen pump, increasing the amount of hydrogen on the cathode side, resulting in hydrogen emissions. Therefore, it is important to delay the occurrence of oxygen starvation within the Membrane Electrode Assembly (MEA) while simultaneously be able to diagnose the hydrogen crossover through the pinholes. In this work, first, we focus on catalyst configuration as a novel method to prevent oxygen starvation and catalyst degradation. It is hypothesized that the redistribution of the platinum catalyst can increase the maximum current density and prevent oxygen starvation and catalyst degradation. Therefore, a multi-objective optimization problem is defined to maximize fuel cell efficiency and to prevent oxygen starvation in the PEMFC. Results indicate that the maximum current density rises about eight percent, while the maximum PEMFC power density increases by twelve percent. In the next step, a previously developed pseudo two-dimensional model is used to simulate fuel cell behavior in the normal and the starvation mode. This model is developed further to capture the effect of the hydrogen pumping phenomenon and to measure the amount of hydrogen in the outlet of the cathode channel. The results obtained from the model are compared with the experimental data, and validation shows that the proposed model is fast and precise. Next, Machine Learning (ML) estimators are used to first detect whether there is a hydrogen crossover in the fuel cell and second to capture the amount of hydrogen cross over. K Nearest Neighbour (KNN) and Artificial Neural Network (ANN) estimators are chosen for leakage detection and classification. Eventually, a pair of ANN classifier-regressor is chosen to first isolate leaky PEMFCs and then quantify the amount of leakage. The classifier and regressor are both trained on the datasets that are generated by the pseudo two-dimensional model. Different performance indexes are evaluated to assure that the model is not underfitting/overfitting. This ML diagnosis algorithm can be employed as an onboard diagnosis system that can be used to detect and possibly prevent cell reversal failures

Control of Proton Exchange Membrane Fuel Cell System

265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system