An open-source toolbox for PEM fuel cell simulation

In this paper, an open-source toolbox that can be used to accurately predict the distribution of the major physical quantities that are transported within a proton exchange membrane (PEM) fuel cell is presented. The toolbox has been developed using the Open Source Field Operation and Manipulation (OpenFOAM) platform, which is an open-source computational fluid dynamics (CFD) code. The base case results for the distribution of velocity, pressure, chemical species, Nernst potential, current density, and temperature are as expected. The plotted polarization curve was compared to the results from a numerical model and experimental data taken from the literature. The conducted simulations have generated a significant amount of data and information about the transport processes that are involved in the operation of a PEM fuel cell. The key role played by the concentration constant in shaping the cell polarization curve has been explored. The development of the present toolbox is in line with the objectives outlined in the International Energy Agency (IEA, Paris, France) Advanced Fuel Cell Annex 37 that is devoted to developing open-source computational tools to facilitate fuel cell technologies. The work therefore serves as a basis for devising additional features that are not always feasible with a commercial cod

CFD modeling and simulation of PEM fuel cell using OpenFOAM

A proton exchange membrane (PEM) fuel cell is an electrolytic cell that converts chemical energy of hydrogen reacting with oxygen into electrical energy. To meet increasingly stringent application needs, improved performance and increased efficiency are paramount. Computational fluid dynamics (CFD) is an ideal means for achieving these improvements. In this paper, a comprehensive CFD-based tool that can accurately simulate the major transport phenomena which take place within a PEM fuel cell is presented. The tool is developed using OpenFOAM and it can be used to rapidly gain insights into the cell working processes. The base case results are compared with previous model results and experimental data. The present I-V curve shows better agreement with the experimental trend at low current densities. The simulation data also indicate that the chosen concentration constant has very significant impact on the concentration overpotential

On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-

Temperature control of open-cathode PEM fuel cells

Proper temperature control of Proton Exchange Membrane (PEM) Fuel Cells is a crucial factor for optimizing fuel cell performance. A robust temperature controller is required for optimal water management of PEM fuel cells. This paper describes a model-based characterization of the equilibrium points of an open-cathode fuel cell system as the baseline for proper controller design, highlighting the relation between fuel cell temperature, humidification and performance. Phase plane analysis of the nonlinear model versus a linearized model around different points of operation shows the potential of approximating the nonlinear system behavior with a linear model. The methodology for the system analysis presented in this paper finally serves for the development of control schemes using robust control techniques. The designed controller is validated in simulation with the nonlinear plant model.Postprint (published version

Sistema de gestión de energía para una estación de carga de vehículos cero emisiones basado en MPC

This work is within the lines of research of the Automática y Robótica Industrial group from the Ingeniería de Sistemas y Automática Department at University of Seville. In particular, it aims at serving as a base for the group contribution to the zero-emission vehicle recharging station future project, which will be held by the Excellent Research Group ENGREEN with the collaboration of researches from the whole Escuela Técnica Superior de Ingeniería. Due to environmental goals of ENGREEN, besides inspiring the use of electric and hydrogen-based vehicles, the charging station will consume renewable energy coming from solar power. The use and management of energy storage system such as hydrogen is crucial because of uncertainty in energy production and consumption. Predictive controllers, well-known as MPC, are used in this work for the resolution of such a complex and multi-objective energy flow problem in that system. Throughout this report, the microgrid is simulated in different generation and consumption scenarios, showing how a scheduler MPC, using both continuous and logic variables, and a reference tracking MPC, using only continuous variables, allow for optimal and efficient solutions. The software Simµgrid, developed by the group itself some years ago, is used over MATLAB/Simulink with Yalmip toolbox and solver CPLEX for the scheduler MPC and the solver quadprog without Yalmip for the reference tracking MPC. The work described above is expected to be used in the future as a starting point for a more complex and detailed management of zero-emission vehicles charging process.Este trabajo se encuentra dentro de las líneas de investigación del grupo Automática y Robótica Industrial del Departamento de Ingeniería de Sistemas y Automática de la Universidad de Sevilla. En concreto, trata de sentar las bases de la aportación de dicho grupo al futuro proyecto de estación de recarga de vehículos cero emisiones que llevará a cabo la Unidad de Excelencia ENGREEN, con componentes de toda la Escuela Técnica Superior de Ingeniería. Debido a los fines medioambientales de dicha unidad de excelencia, además de impulsar el uso del vehículo eléctrico y del basado en hidrógeno, la estación de recarga hará uso de fuentes de energía renovable como la solar. La incertidumbre en la producción y consumo de energía hace necesario el empleo de sistemas de almacenamiento de energía como el hidrógeno y su consiguiente gestión. El flujo de potencia en estos sistemas es un problema de control complejo y multiobjetivo para cuya resolución se emplean en este trabajo técnicas de control predictivo, o más conocido como MPC. A lo largo de esta memoria se describen las simulaciones de la microrred con diferentes escenarios de generación y consumo, mostrando cómo un MPC planificador, usando tanto variables continuas como lógicas, y otro de seguimiento, usando sólo variables continuas, permiten obtener una solución óptima y eficiente. Para ello se usa MATLAB/Simulink sobre el software Simµgrid, desarrollado en años anteriores por el propio grupo de investigación, con el toolbox Yalmip y el solver CPLEX para el MPC planificador y el solver quadprog sin Yalmip para el MPC de seguimiento. Lo anterior pretende emplearse en un futuro como punto de partida para una gestión más compleja y detallada del proceso de recarga de vehículos cero emisiones.Universidad de Sevilla. Máster en Ingeniería Electrónica, Robótica y Automátic

Building comfort control using MPC: Development of a Coupled EnergyPlus-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System

The urge of modernizing the building stock in the European Union comes from one clear evidence: it is the largest energy consuming sector, accounting for up to one-third of the total final energy consumption. The vast majority of houses and offices in EU countries were built before 1990 and did not undergo any renovation, meaning they show poor thermal insulation capability, and no smart technique is implemented for the control and reduction of both the electricity and heating demands. This results in significantly high emissions. Almost 40% of EU carbon dioxide emissions indeed come from the building sector [1] , indirectly in the construction process and directly during operation. The set of contaminants also include greenhouse gases such as hydrofluorocarbons, fine particles (PM2.5/PM10) and toxic dusts recognized as one of the main causes of cancer onset [2]. This is precisely related to the fuel mix each country employs to cover the sector needs: 38.2% of the OECD countries residential demand is covered by natural gas and a phasing-out 10% by oil [3]. Technologically advanced solutions such as hydrogen Fuel Cells, integrated with other renewable sources, can represent a clean solution to push down emissions but also energy consumptions by digitalizing the system and implementing control strategies to optimally match demand and generation. This study aims at developing a coupled EnergyPlus-MATLAB Simulation Framework of an integrated electrical and thermal residential renewable energy system with a Model Predictive Controller (MPC) to size and control the operation of a fuel cell stack. A recently renovated single-family house in the province of Turin (IT) is the case-study modelled in EnergyPlus. The simulation requires the building geometry and thermophysical properties and the weather conditions as inputs, and by designing appropriately the controller, a schedule for the heating demand and the resulting evolution of the indoor temperature is obtained.IncomingObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminan

Reliability modelling of PEM fuel cells with hybrid Petri nets

In this paper, a novel model for dynamic reliability analysis of a PEM fuel cell system is developed using Modelica language in order to account for multi-state dynamics and aging. The modelling approach constitutes 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 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. A case study of effects of a cooling system on fuel cell performance was investigated. Monte Carlo simulations of the process resulted in a distribution of system parameters, thus providing an estimate of best and worst scenarios of a fuel cell lifetime

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results

Design of Brush DC Motor’s Speed Controller Using PI Method with Adjusted Hydrogen Fuel Feed on The PEMFC

Hydrogen is one of the alternate way that become a solution of the crisis of energy problems. Fuel cell transform the hydrogen into electric power, heat, and water. The power that was made by the fuel cell can be used for a lot of things, and transportation is one of them. This research main point is to make a design of a speed controller for a DC motor as a main object using the power that was created by fuel cell. The controller is a system that will increase the efficiency of the hydrogen used ,as a main supply, with controlling the flow rate of hydrogen that flowed into the fuel cell in order to avoid the over flowing of hydrogen that will cause the hydrogen wasted. The controller will also boost the DC motor’s response. To conduct the research, the method that is the most suitable for the case is PI control system that will boosted the transient response from the system which stands from a proportional, derivative, and integral parameters that will be tuned by using a root-locus method with the purpose to make a system that has a quick rise time and low %overshoot that will make the system has rise time=10.1s, settling time=66s, peak time = 40.9s, % Overshoot=8.66%, steady state=1 as the transient response’s result

Renewable Energy Technologies and Hybrid Electric Vehicle Challenges

This paper introduces the utilization of selected renewable energy technologies such as solar cell, battery, proton exchange membrane (PEM) fuel cell (FC) and super-capacitors (SCs) in the electrical vehicle industry. Combination of multiple energy resources is imperative to balance the different characteristic of each resource. Concomitantly, the need of an efficient energy management system arises within the industry. Thus, existing system from past and present undergoing research papers are summarized to give a compact overview on the technology and know-how technique to readers