Modeling and Simulation of a Proton Exchange Membrane Fuel Cell Alongside a Waste Heat Recovery System Based on the Organic Rankine Cycle in MATLAB/SIMULINK Environment

Modelling of Fuel Cells and Related Energy Conversion Systems

Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Various fuel cells have been proposed, of which proton-exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC) are the most frequently used. However, experimental testing rigs are expensive, and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of the operation of fuel cells is compulsory to avoid damage, and such control must be based on accurate models, able to predict cell behaviour and prevent stresses and shutdown. Additionally, when used for mobile applications, intrinsically dynamic operation is needed. Some selected examples of steady-state, dynamic and fluid-dynamic modelling of different types of fuel cells are here proposed, mainly dealing with PEMFC and SOFC types. The general ideas behind the thermodynamic, kinetic and transport description are discussed, with some examples of models derived for single cells, stacks and integrated power cogeneration units. This review can be considered an introductory picture of the modelling methods for these devices, to underline the different approaches and the key aspects to be taken into account. Examples of different scales and multi-scale modelling are also provided.

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.

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.