An integrated simulation model for PEM fuel cell power systems with a buck DC-DC converter

Fuel cell powered systems generally have a high current and a low voltage. Therefore, the output voltage of the fuel cell must be stepped-down using a DC-DC buck converter. However, since the fuel cell and converter have different dynamics, they must be suitably coordinated in order to satisfy the demanded load. Accordingly, this study commences by constructing a MATLAB/Simulink model of a proton exchange membrane fuel cell (PEMFC) system comprising a PEMFC stack, an air/fuel supply system, and a temperature control system. The validity of the PEMFC model is demonstrated by comparing the simulation results obtained for the polarzation curves of a single fuel cell with the corresponding experimental curves. A model is then constructed of the DC-DC buck converter used to step-down the PEMFC output voltage. In addition, a sliding mode control (SMC) scheme is proposed for the DC-DC buck converter which guarantees a low and stable output voltage given transient variations in the output voltage of the PEMFC. Finally, a model is constructed of a DC-AC inverter with a pulse width modulated (PWM) control scheme which enables the PEMFC stack to supply the grid or power AC applications directly. Overall, the combined PEMFC/DC-DC buck converter/DC-AC inverter model provides a powerful and versatile tool for the design and development of a wide range of PEMFC power systems.     

Modelling,simulation and control of a proton exchange membrane fuel cell (PEMFC) power system

Fuel cell power systems are emerging as promising means of electrical power generation on account of the associated clean electricity generation process, as well as their suitability for use in a wide range of applications. During the design stage, the development of a computer model for simulating the behaviour of a system under development can facilitate the experimentation and testing of that system's performance. Since the electrical power output of a fuel cell stack is seldom at a suitable fixed voltage, conditioning circuits and their associated controllers must be incorporated in the design of the fuel cell power system. This paper presents a MATLAB/Simulink model that simulates the behaviour of a Proton Exchange Membrane Fuel Cell (PEMFC), conditioning circuits and their controllers. The computer modelling of the PEMFC was based on adopted mathematical models that describe the fuel cell's operational voltage, while accounting for the irreversibilities associated with the fuel cell stack. The conditioning circuits that are included in the Simulink model are a DC–DC converter and DC–AC inverter circuits. These circuits are the commonly utilized power electronics circuits for regulating and conditioning the output voltage from a fuel cell stack. The modelling of the circuits is based on relationships that govern the output voltage behaviour with respect to their input voltages, switching duty cycle and efficiency. In addition, this paper describes a Fuzzy Logic Controller (FLC) design that is aimed at regulating the conditioning circuits to provide and maintain suitable electrical power for a wide range of applications. The model presented demonstrates the use of the FLC in conjunction with the PEMFC Simulink model and that it is the basis for more in-depth analytical models.     

A metaheuristic-based methodology for efficient system identification of the Proton Exchange Membrane Fuel Cell stacks

In this study, a new procedure for optimum and effective system identification of Proton Exchange Membrane Fuel Cell (PEMFC) stacks is introduced. The present study proposes a method to obtain the optimum amount of the uncertain parameters of the PEMFC design to deliver the uppermost confirmation with the real output voltage value. To do so, the integral of the absolute error (IAE) between the real and model outputted voltage values is considered an objective function. To minimize this function, a modified design of the emperor penguin optimizer has been suggested. The method has been then investigated by performing two real case study models and its achievements are put in comparison with several latest techniques to show the method's productivity. The achievements show that the suggested technique has a higher ability for parameter estimation of the PEMFC models.     

基于PEMFC分布式发电系统的动态特性分析

分析了质子交换膜燃料电池(PEMFC)的机理模型,在此基础上运用MATLAB的Simulink仿真工具,建立了PEMFC发电系统带负载模型。通过仿真,分析了负载对PEMFC电堆的各项动态特性(燃料的流量、效率、输出电压等)的影响,以及DC/DC、负载端的电压响应。仿真结果中负载电压呈三相交流正弦波形,表明搭建的整个PEMFC发电系统是基本正确的,为实现PEMFC并网的实时分析和动态优化提供了理论依据和参考方法。     

Improved fish migration optimization method to identify PEMFC parameters

In a worldwide environment context where the emergency need to decrease pollutant emissions is an important issue, the research for solutions is increasing. Fuel cell technology is anticipated to become a practicable approach for solving the problem of pollution due to its environmentally friendly characteristics. In this work, a novel problem formulation is suggested for the efficient recognition of PEMFC parameters which the solving is by using the Improved Fish Migration Optimizer (IFMO) technique. After coding the steps of the algorithm in MATLAB, the objective function is resolved for the fuel cell. Comprehensive simulations evaluate the formulation performance with the suggested and traditional objective functions; then, the outcomes are compared. To confirm the suggested formulation ascendancy compared to the traditional curve-fitting method, a complete assessment based on convergence rate, the value of the objective function, and the value of absolute voltage error are performed. The achieved value of the objective function, absolute voltage error, and average time of computation is 0.005, 0.4, and 1.63, respectively. Environmentally, the combustion of hydrogen and its use in PEMFC produce no carbon dioxide.     

Real-time AC voltage control and power-following of a combined proton exchange membrane fuel cell,and ultracapacitor bank with nonlinear loads

The nonlinear loads create a wide range of current harmonics in the system. Such loads can make distortions on the output voltage profile, influence on the fuel cell (FC) performance, and endanger safe operation of the FC unit. In this paper, new strategies for power-following and AC voltage control have been developed. The proposed system consists of the ultracapacitor (UC) bank and proton exchange membrane fuel cell (PEMFC) supplying nonlinear AC loads. The power tracking strategy is based on the Fourier analysis of total load demand. The Fourier analysis is used as an effective tool to eliminate destructive effect of current harmonics on the PEMFC output current. To supply the nonlinear AC loads under sinusoidal voltage with the fast response, a dynamic model for the inverter control loop is also presented. This model is used to enhance the input reference tracking and reject input/output disturbances. The simulation outcomes confirm the desirable PEMFC performance against nonlinear load disturbances. In addition, the output AC voltage is kept sinusoidal and has low deviations under nonlinear load variations.     

Collaborative simulation for dynamical PEMFC power systems

In this paper a collaborative simulation platform for proton exchange membrane fuel cell (PEMFC) power systems is presented, where the stack is simulated by a two-phase distributed parameter model and the auxiliary units by lumped parameter models. By exchanging the dynamic data between the external load/auxiliary units and PEMFC stack, dynamic simulation of PEMFC stack has been carried out during the load changes for various states associated with different characteristic variables. The internal states of the stack can be observed due to variation of external load/auxiliary units. Numerical experiments are provided for a special case with multiple cycles of load changes derived from an acceleration mode of a fuel cell vehicle. The numerical results demonstrate that the “undershoot” of output voltage is due to the response lag of the auxiliary units and liquid water accumulation in the fuel cell stack.     

The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks

This paper has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a genetic algorithm neural networks (GANN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the GANN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in GANN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this paper will present an approach for constructing a GANN model with precise accuracy for the output voltage of PEMFC. For achieving the GANN model with high precision, a troublesome work has to be taken care of, that is, to determine all the parameters required in GANN. We will introduce Taguchi method to solve this problem as well. Finally, to show the superiority of proposed model, this approach has compared the estimation values of output voltage for PEMFC from GANN and BPNN models without using Taguchi method. One can easily find that the error of the proposed method is much smaller than that of the GANN model without Taguchi method and of the BPNN model; that is, the proposed approach has better performance on estimation for PEMFC output voltages.     

Performance prediction of a proton exchange membrane fuel cell using the ANFIS model

In this study, the performance (current–voltage curve) prediction of a Proton Exchange Membrane Fuel Cell (PEMFC) is performed for different operational conditions using an Adaptive Neuro-Fuzzy Inference System (ANFIS). First, ANFIS is trained with a set of input and output data. The trained model is then tested with an independent set of experimental data. The trained and tested model is then used to predict the performance curve of the PEMFC under various operational conditions. The model shows very good agreement with the experimental data and this indicates that ANFIS is capable of predicting fuel cell performance (in terms of cell voltage) with a high accuracy in an easy, rapid and cost effective way for the case presented. Finally, the capabilities and the limitations of the model for the application in fuel cells have been discussed.     

A family of high gain fuel cell front-end converters with low input current ripple for PEMFC power conditioning systems

Since the output voltage of the proton exchange membrane fuel cell (PEMFC) is relatively low and load-dependent, a high-performance fuel cell front-end converter is required to achieve boost and power regulation in PEMFC systems. In response, a novel family of high gain fuel cell front-end converters with low input current ripple is proposed. The proposed topologies can substantially improve the voltage gain through the expansion and combination of active switched-inductor networks and passive switched-capacitor units. The introduced interleaved parallel structure is convenient to limit the current ripple on the input side to prevent accelerated aging of fuel cells, which is another prominent advantage. Meanwhile, the converters can achieve the automatic current sharing between parallel inductors and the low voltage stress on active switches and diodes. In this paper, the fuel cell model and topology derivation of the high gain fuel cell front-end converters are first analyzed. Then, it further describes the operating mode and steady-state performance of converters under the inductor current continuous conduction mode. The comparison with other converters shows that this converter is suitable for connecting the PEMFC to the high voltage DC bus. Finally, a 200 W, 20/180 V converter prototype is implemented, and the simulation and experiment prove the theoretical correctness and validate the superior performances of the proposed converters.     

Fractional order modeling and two loop control of PEM fuel cell for voltage regulation considering both source and load perturbations

The growing demand for renewable energy sources has favored attention towards fuel cell and in particular towards Polymer Electrolyte Membrane Fuel Cell (PEMFC) as an alternative energy source. Despite the advantage of possessing high current density, standalone isolated fuel cell operate at low voltage and the output is heavily dependent on the operating condition. This demands the integration of fuel cells with suitable power conditioning units. The present work aims at designing a controller which achieves the objective of regulated output voltage irrespective of variation in both load and source operating condition. The design and integration of the converter with PEMFC necessitates the development of a mathematical model, which can represent the PEMFC dynamics under different operating conditions. PEMFCs are known to exhibit distributed dynamics and possess long term memory, which are more accurately represented by fractional calculus. In this regard, a hybrid optimization based approach for fractional order modeling of PEMFC has been proposed. Further using the model, a fractional order Proportional Integral (FOPI) controller has been designed for regulating the load voltage. The presence of an extra tuning parameter in FOPI allows greater flexibility in achieving the system specification as compared to the classical Integer Order Proportional Integral (IOPI) controller. The effectiveness of the proposed FOPI controller for PEMFC fed PWM DC/DC converter has been validated under varying operating condition of the PEMFC and load perturbations in real time environment.     

Energy processing from fuel-cell systems using a high-gain power dc-dc converter: Analysis,design, and implementation

The electrolyte membrane fuel cell (PEMFC) is characterized by a low and unregulated output voltage; thus, an interface between source and load is required for processing the generated energy by the PEMFC. In this paper, a solution for processing the energy generated by a PEMFC is given. A switching regulator is developed by using a quadratic boost converter with a single switch (QBC-SS). The controller for the QBC-SS is designed using average current-mode (ACM) control, which is easy to implement using analog circuits. The proposed switching regulator ensures high conversion ratios, output voltage regulation, adequate dynamic performance, and stability. On the other hand, a model with static characteristics for the PEMFC electrical behavior is proposed, which can be used for modeling and control purposes. This model consists of three parameters, which are computed using experimental data of the PEMFC stack. A laboratory prototype of 400 W is used to verify the analytical results. As an input source, a PEMFC system is used. The output voltage of the PEMFC stack ranges from 41 V to 24 V, which depends on the generated current. Experimental results applying load step changes and frequency response analysis are shown.     

Dynamic modeling and simulation of a small wind–fuel cell hybrid energy system

This paper describes dynamic modeling and simulation results of a small wind–fuel cell hybrid energy system. The system consists of a 400 W wind turbine, a proton exchange membrane fuel cell (PEMFC), ultracapacitors, an electrolyzer, and a power converter. The output fluctuation of the wind turbine due to wind speed variation is reduced using a fuel cell stack. The load is supplied from the wind turbine with a fuel cell working in parallel. Excess wind energy when available is converted to hydrogen using an electrolyzer for later use in the fuel cell. Ultracapacitors and a power converter unit are proposed to minimize voltage fluctuations in the system and generate AC voltage. Dynamic modeling of various components of this small isolated system is presented. Dynamic aspects of temperature variation and double layer capacitance of the fuel cell are also included. PID type controllers are used to control the fuel cell system. SIMULINK^TM is used for the simulation of this highly nonlinear hybrid energy system. System dynamics are studied to determine the voltage variation throughout the system. Transient responses of the system to step changes in the load current and wind speed in a number of possible situations are presented. Analysis of simulation results and limitations of the wind–fuel cell hybrid energy system are discussed. The voltage variation at the output was found to be within the acceptable range. The proposed system does not need conventional battery storage. It may be used for off-grid power generation in remote communities.     

A new method for optimal parameters identification of a PEMFC using an improved version of Monarch Butterfly Optimization Algorithm

In this paper, a circuit-based model of proton exchange membrane fuel cell (PEMFC) is developed for optimal selection of the model parameters. The optimization is based on using an improved version of Monarch Butterfly Optimization (IMBO) algorithm for minimizing the Integral Time Absolute Error between the measured output voltage and the output voltage of the achieved model. For validation of the proposed method, two different case studies including 6 kW NedSstack PS6 and 2 kW Nexa FC PEMFC stacks have been employed and the results have been compared with the experimental data and some well-known metaheuristics including Chaotic Grasshopper Optimization Algorithm (CGOA), Grass Fibrous Root Optimization Algorithm (GRA), and basic Monarch Butterfly Optimization (MBO) to indicate the superiority of the proposed method against the compared methods. Final results show a satisfying agreement between the proposed IMBO and the experimental data.     

Dynamic modeling and dynamic responses of grid-connected fuel cell

The active distribution network (ADN) is a new effective approach to facilitate connecting distributed generation (DG) to the network, where the DG is controlled to support the system stability during various kinds of disturbances. Fuel cell is one of the most important DGs, however there are still many issues left to be solved in order to meet the requirements of the ADN, such as dynamic modeling, dynamic responses to power systems, especially during voltage dip, system fault, etc. In the existing grid-connected fuel cell researches, most of the dynamic models did not consider air compressor and its parasitic power consumption. Hence, a dynamic model of grid-connected proton exchange membrane fuel cell (PEMFC) is presented by considering dynamic modeling of the air compressor and its parasitic power consumption. Based on the model, the mutual influences between power system and fuel cell are analyzed when the fuel cell is synchronously grid-connected. The dynamic responses of the fuel cell and its low voltage and fault ride-through capability are studied when the power system fault or voltage dip occurs. Finally, based on the dynamic simulation of the typical power systems with a PEMFC, the theoretical basis and guiding suggestions are presented for grid-connection, dynamic operation, and off-grid of fuel cells.     

Parameter optimization for a PEMFC model with a hybrid genetic algorithm

Many steady‐state models of polymer electrolyte membrane fuel cells (PEMFC) have been developed and published in recent years. However, models which are easy to be solved and feasible for engineering applications are few. Moreover, rarely the methods for parameter optimization of PEMFC stack models were discussed. In this paper, an electrochemical‐based fuel cell model suitable for engineering optimization is presented. Parameters of this PEMFC model are determined and optimized by means of a niche hybrid genetic algorithm (HGA) by using stack output‐voltage, stack demand current, anode pressure and cathode pressure as input–output data. This genetic algorithm is a modified method for global optimization. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder–Mead's simplex method into genetic algorithms (GAs). Calculation results of this PEMFC model with optimized parameters agreed with experimental data well and show that this model can be used for the study on the PEMFC steady‐state performance, is broader in applicability than the earlier steady‐state models. HGA is an effective and reliable technique for optimizing the model parameters of PEMFC stack. Copyright © 2005 John Wiley & Sons, Ltd.     

A linear quadratic regulator control of a stand-alone PEM fuel cell power plant

This paper introduces a technique based on linear quadratic regulator (LQR) to control the output voltage at the load point versus load variation from a standalone proton exchange membrane (PEM) fuel cell power plant (FCPP) for a group housing use. The controller modifies the optimal gains k _i by minimizing a cost function, and the phase angle of the AC output voltage to control the active and reactive power output from an FCPP to match the terminal load. The control actions are based on feedback signals from the terminal load, output voltage and fuel cell feedback current. The topology chosen for the simulation consists of a 45 kW proton exchange membrane fuel cell (PEMFC), boost type DC/DC converter, a three-phase DC/AC inverter followed by an LC filter. Simulation results show that the proposed control strategy operated at low commutation frequency (2 kHz) offers good performances versus load variations with low total harmonic distortions (THD), which is very useful for high power applications.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Channel geometry optimization using a 2D fuel cell model and its verification for a polymer electrolyte membrane fuel cell

This paper studies the optimization method of channel geometries for a proton exchange membrane fuel cell (PEMFC) using a genetic algorithm (GA). The channel and rib widths and channel height are selected as geometry variables. The fuel cell output power is chosen as the cost function for the optimization. In this paper, an in-house genetic algorithm is constructed, and the fuel cell output power is obtained using an interfacing program connected to a commercial computational fluid dynamics (CFD) tool, COMSOL, in a Matlab environment. The 2D PEMFC is used to calculate the performance cost function for computational time and cost. The calculated output power of the PEMFC is delivered to the in-house GA program to check for optimality. After the optimality is checked, the new geometry data is fed back to the COMSOL to calculate the PEMFC output power until the optimization process is finished. Experiments are conducted to support the optimized results using three different channel geometries: channel-to-rib width ratios of 0.5:1, 1:1, and 2:1. A full 3D PEMFC CFD model is constructed using COMSOL to support the 2D CFD optimization results. This paper shows the possibility of applying the geometry optimization process to sophisticated electrochemical reaction systems, such as a PEMFC, using a GA and a commercial CFD tool on the Matlab platform. The geometries and materials can be optimized using this approach to obtain the most efficient performance of an electrochemical system.     

Load Transient Mitigation for Stand-Alone Fuel Cell Power Generation Systems

In this paper, a load transient mitigation technique for stand-alone fuel cell (FC)-battery power generation systems is proposed. The technique can be used not only to improve the output power quality of the overall system, but also to mitigate or eliminate the electrical feedback stresses that are caused by load transients upon fuel cells. As a result, the durability of the fuel cell can also be improved. System analysis and controller design procedure for the proposed technique are given in this paper. Simulation studies have been carried out on FC-battery power generation systems using the dynamic models developed for proton exchange membrane fuel cell (PEMFC) and solid-oxide fuel cell (SOFC). Simulation results show the effectiveness of the proposed technique in preventing load transients from affecting the fuel cell performance.