Modeling and Simulation of Fuel cell (Dicks Larminie Model) based 3-Phase Voltage Source Inverter

In the present work, performance of three phase voltage source inverter, while feeding different power factor loads, has been investigated. Fuel cells model namely Dicks Larminie model is used in input side as a DC source while dynamic load has been used at the output side. Dynamic load used is induction motor (IM). Performance of IM has been investigated under various loading conditions. ANN based control strategy has been proposed to find the conduction angle of a 3-Phase VSI and verified for IM load. Simulations have been performed using PSIM 7.0.5 and MATLAB 7.0.4.DOI:http://dx.doi.org/10.11591/ijece.v4i5.550

Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.This research was funded by the Basque Government, Diputación Foral de Álava and UPV/EHU, respectively, through the projects EKOHEGAZ (ELKARTEK KK-2021/00092), CONAVANTER and GIU20/063

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

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

International audienceThis paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems

Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System

settings Open AccessArticle Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System by Mohamed Derbeli 1,2,* [OrcID] , Oscar Barambones 1 [OrcID] , Mohammed Yousri Silaa 1 [OrcID] and Cristian Napole 1 [OrcID] 1 Engineering School of Vitoria, University of the Basque Country UPV/EHU, Nieves Cano 12, 1006 Vitoria, Spain 2 National Engineering School of Gabes, University of Gabes, Omar Ibn-Elkhattab, 6029 Gabes, Tunisia * Author to whom correspondence should be addressed. Actuators 2020, 9(4), 105; https://doi.org/10.3390/act9040105 Received: 30 August 2020 / Revised: 25 September 2020 / Accepted: 10 October 2020 / Published: 16 October 2020 (This article belongs to the Section High Torque/Power Density Actuators) Download PDF Browse Figures Abstract Polymer electrolyte membrane (PEM) fuel cells demonstrate potential as a comprehensive and general alternative to fossil fuel. They are also considered to be the energy source of the twenty-first century. However, fuel cell systems have non-linear output characteristics because of their input variations, which causes a significant loss in the overall system output. Thus, aiming to optimize their outputs, fuel cells are usually coupled with a controlled electronic actuator (DC-DC boost converter) that offers highly regulated output voltage. High-order sliding mode (HOSM) control has been effectively used for power electronic converters due to its high tracking accuracy, design simplicity, and robustness. Therefore, this paper proposes a novel maximum power point tracking (MPPT) method based on a combination of reference current estimator (RCE) and high-order prescribed convergence law (HO-PCL) for a PEM fuel cell power system. The proposed MPPT method is implemented practically on a hardware 360W FC-42/HLC evaluation kit. The obtained experimental results demonstrate the success of the proposed method in extracting the maximum power from the fuel cell with high tracking performance.This work was partially supported by Eusko Jaurlaritza/Gobierno Vasco [grant number SMAR3NAK ELKARTEK KK-2019/00051]; the Provincial Council of Alava (DFA) [grant number CONAVAUTIN 2] (Collaboration Agreement)

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources - photovoltaics modules and a wind turbine generator - have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Development of solid oxide fuel cell stack models for monitoring, diagnosis and control applications

2011 - 2012In the present thesis different SOFC stack models have been presented. The results shown were obtained in the general framework of the GENIUS project (GEneric diagNosis Instrument for SOFC systems), funded by the European Union (grant agreement n° 245128). The objective of the project is to develop “generic” diagnostic tools and methodologies for SOFC systems. The “generic” term refers to the flexibility of diagnosis tools to be adapted to different SOFC systems. In order to achieve the target of the project and to develop stack models suitable for monitoring, control and diagnosis applications for SOFC systems, different modeling approaches have been proposed. Particular attention was given to their implementability into computational tools for on-board use. In this thesis one-dimensional (1-D), grey-box and blackbox stack models, both stationary and dynamic were developed. The models were validated with experimental data provided by European partners in the frame of the GENIUS project. A 1-D stationary model of a planar SOFC in co-flow and counter-flow configurations was presented. The model was developed starting from a 1- D model proposed by the University of Salerno for co-flow configuration (Sorrentino, 2006). The model was cross-validated with similar models developed by the University of Genoa and by the institute VTT. The crossvalidation results underlined the suitability of the 1-D model developed. A possible application of the 1-D model for the estimation of stack degradation was presented. The results confirmed the possibility to implement such a model for fault detection. A lumped gray-box model for the simulation of TOPSOE stack thermal dynamics was developed for the SOFC stack of TOPSOE, whose experimental data were made available in the frame of the GENIUS project. Particular attention was given to the problem of heat flows between stack and surrounding and a dedicated model was proposed. The black-box approach followed for the implementation of the heat flows and its reliability and accuracy was shown to be satisfactory for the purpose of its applications. The procedure adopted turned out to be fast and applicable to other SOFC stacks with different geometries and materials. The good results obtained and the limited calculation time make this model suitable for implementation in diagnostic tools. Another field of application is that of virtual sensors for stack temperature control. Black-box models for SOFC stack were also developed. In particular, a stationary Neural Network for the simulation of the HEXIS stack voltage was developed. The analyzed system was a 5-cells stack operated up to 10 thousand hours at constant load. The neural network exhibited very good prediction accuracy, even for systems with different technology from the one used for training the model. Beyond showing excellent prediction capabilities, the NN ensured high accuracy in well reproducing evolution of degradation in SOFC stacks, especially thanks to the inclusion of time among model inputs. Moreover, a Recurrent Neural Network for dynamic simulation of TOPSOE stack voltage and a similar one for a short stack built by HTc and tested by VTT were developed. The stacks analyzed were: a planar co-flow SOFC stack (TOPSOE) and a planar counter-flow SOFC stack (VTT-HTc). All models developed in this thesis have shown high accuracy and computation times that allow them to be implemented into diagnostic and control tool both for off-line (1-D model and grey-box) and for on-line (NN and RNNs) applications. It is important noting that the models were developed with reference to stacks produced by different companies. This allowed the evaluation of different SOFC technologies, thus obtaining useful information in the models development. The information underlined the critical aspects of these systems with regard to the measurements and control of some system variables, giving indications for the stack models development. The proposed modeling approaches are good candidates to address emerging needs in fuel cell development and on-field deployment, such as the opportunity of developing versatile model-based tools capable to be generic enough for real-time control and diagnosis of different fuel cell systems typologies, technologies and power scales. [edited by author]XI n.s