Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

[EN] Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.This research was funded by COLCIENCIAS (Administrative department of science, technology and innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-ColombiaAriza-Chacón, HE.; Correcher Salvador, A.; Sánchez-Diaz, C.; Pérez-Navarro, Á.; García Moreno, E. (2018). Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm. Energies. 11(8):1-15. https://doi.org/10.3390/en11082099S115118Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6Wang, Y., Chen, K. S., Mishler, J., Cho, S. C., & Adroher, X. C. (2011). 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Adaptive online parameter estimation algorithm of PEM fuel cells

Since most of fuel cell models are generally nonlinearly parameterized functions, existing modeling techniques rely on the optimization approaches and impose heavy computational costs. In this paper, an adaptive online parameter estimation approach for PEM fuel cells is developed in order to directly estimate unknown parameters. The general framework of this approach is that the electrochemical model is first reformulated using Taylor series expansion. Then, one recently proposed adaptive parameter estimation method is further tailored to estimate the unknown parameters. In this method, the adaptive law is directly driven by the parameter estimation errors without using any predictors or observers. Moreover, parameter estimation errors can be guaranteed to achieve exponential convergence. Besides, the online validation of regressor matrix invertibility are avoided such that computation costs can be effectively reduced. Finally, comparative simulation results demonstrate that the proposed approach can achieve better performance than least square algorithm for estimating unknown parameters of fuel cells.Postprint (published version

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

Modelling and controlling of polymer electrolyte fuel cell systems

This thesis focuses on the modelling and controlling of polymer electrolyte fuelcell (PEFC) systems. A system level dynamic PEFC model has been developedto test the system performance (output voltage, reactants gas partial pressures,and stack temperature) for different operating conditions. The simulation resultsare in good agreement with the experimental data, which indicates that thePEFC model is well qualified to capture the dynamic performance of the PEFCsystem. Controlling strategies play a significant role in improving the fuel cellsystem’s reliability. Novel model predictive control (MPC) controllers and proportional–integral–derivative (PID) controllers are proposed and implemented indifferent PEFC systems to control voltage and regulate temperature to enhancesystem performance. MPC controllers show superior performance to PID controllers in tracking the reference value, with less overshoot and faster response. Anovel hydrogen selective membrane reactor (MR) is designed for methanol steamreforming (MSR) to produce fuel cell grade hydrogen for PEFC stack use. Thebackpropagation (BP) neural network algorithm is applied to find the mappingrelation between the MR’s operating parameters and the PEFC system’s outputperformance. Simulation results show that the BP neural network algorithm canwell predict the system behaviour and that the developed mapping relation modelcan be used for practical operation guidance and future control applications

Genetic Algorithm & Fuzzy Logic Based PEM Fuel Cells Power Conversion System for AC Integration

In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzzy logic controller is also used to control active power of PEM fuel cell system. Fuzzy logic controller will modify the hydrogen flow feedback from the terminal load. At the end, we will simulate DC-AC converter for checking its efficiency, stability and durability on the basis of the genetic algorithm and fuzzy logic controller to control power generation

A global integral terminal sliding mode control based on a novel reaching law for a proton exchange membrane fuel cell system

Proton exchange membrane fuel cells are devices with huge potential for renewable and clean industries due to their high efficiency and low emissions. Since the proton exchange membrane fuel cell employed in this research supplied a low output voltage, it was encouraged to use a boost converter with a designed non-linear controller to provide a suitable end-user voltage. In this paper, we proposed a novel control framework based on sliding mode control, which is a global integral sliding mode control linked with a quick reaching law that has been implemented in a commercial fuel cell system Heliocentris FC50 through a dSpace 1102 control board. We compared the strategy with a conventional sliding mode controller and an integral terminal sliding mode controller where we addressed a Lyapunov stability proof has for each structure. We contrasted the experimental outcomes where we proved the superiority of the proposed novel design in terms of robustness, convergence speed. Additionally, as the sliding mode controllers are well known by the energy consumption caused by the chattering effect, we analysed every framework in these terms. Finally, it was found that the proposed structure offered an enhancement in the energy consumption issues. Moreover, the applicability of the proposed control scheme has been demonstrated through the real time implementation over a commercial fuel cell.The authors wish to express their gratitude to the Basque Govern-ment, through the project EKOHEGAZ (ELKARTEK KK-2021/00092) , to the Diputacion Foral de alava (DFA) , through the project CONA-VANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work. The authors wish to express their gratitude to the Basque Govern-ment, through the project EKOHEGAZ (ELKARTEK KK-2021/00092) , to the Diputacion Foral de alava (DFA) , through the project CONA-VANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

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

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications

A Novel Adaptive PID Controller Design for a PEM Fuel Cell Using Stochastic Gradient Descent with Momentum Enhanced by Whale Optimizer

This paper presents an adaptive PID using stochastic gradient descent with momentum (SGDM) for a proton exchange membrane fuel cell (PEMFC) power system. PEMFC is a nonlinear system that encounters external disturbances such as inlet gas pressures and temperature variations, for which an adaptive control law should be designed. The SGDM algorithm is employed to minimize the cost function and adapt the PID parameters according to the perturbation changes. The whale optimization algorithm (WOA) was chosen to enhance the adaptive rates in the offline mode. The proposed controller is compared with PID stochastic gradient descent (PIDSGD) and PID Ziegler Nichols tuning (PID-ZN). The control strategies’ robustnesses are tested under a variety of temperatures and loads. Unlike the PIDSGD and PID-ZN controllers, the PIDSGDM controller can attain the required control performance, such as fast convergence and high robustness. Simulation results using Matlab/Simulink have been studied and illustrate the effectiveness of the proposed controller.The authors wish to express their gratitude to the Basque Government through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA) through the project CONAVANTER, and to the UPV/EHU through the project GIU20/063 for supporting this work