A new RBFN based MPPT controller for grid-connected PEMFC system with high step-up three-phase IBC

Extraction of maximum power from a proton exchange membrane fuel cell (PEMFC) power source is necessary for its economical and optimal utilization. In this paper, a neural network based maximum power point tracking (MPPT) controller is proposed for the grid-connected PEMFC system. Radial basis function network (RBFN) algorithm is implemented in the neural network controller to extract the maximum power from PEMFC. A high step-up three-phase interleaved boost converter (IBC) is also designed in order to reduce the current ripples coming out from the PEMFC. Interleaving technique provides high power capability and reduces the voltage stress on the power semiconductor devices. The performance analysis of the proposed RBFN MPPT controller is analyzed in MATLAB/Simulink platform for both standalone as well as for the grid-connected PEMFC system.     

Performance improvement of PEM fuel cell power system using fuzzy logic controller-based MPPT technique to extract the maximum power under various conditions

Power generation of a fuel cell (FC) is mostly dependent upon operational variables such as cell temperature and membrane water content. There is an individual maximum power point (MPP) on the P-I curve of the FC. The location of the MPP varies with respect to the MPP position. Thus, an MPP tracking (MPPT) system should exist to guarantee that the FC works at the MPP in order to maximize the functionality. Due to their straightforward structure, prevalent MPPT methods had strong functionality. However, their primary limitations include fluctuations around the MPP and inefficiency under abrupt variations of operating conditions. The primary objective of this paper is to maintain the PEMFCs operation at an efficient power point. To this purpose, the efficiency of PEM-FC is tested and enhanced using a variety of MPPT-based smart controller techniques. To determine the appropriate MPPT controller parameters, the modified fluid search optimization (MFSO) approach and fuzzy logic controller (FLC) are employed. Furthermore, the MFSO method is deployed to adjust the membership functions (MFs) of the FLC. The MFSO is an excellent approach for coping with the stochastic behavior of the PEM-FC system when the temperature and water content of the membrane change. In terms of improved dynamic behavior, better convergence rate, reduced oscillations, and better tracking of the MPP, the results obtained by employing the suggested strategy demonstrate the superior functionality of the system compared to case using other methods. Moreover, the power generated by the PEMFC system is less than the nominal capacity for the temperature's rated capacity. Therefore, the deficit in power would be covered by transacting power with the grid.     

Fuzzy multi-objective technique combined with VCS algorithm for unified power quality conditioner based on hybrid power source PEMFC/SC

This paper is proposed to establish an optimal control method for UPQC (Unified Power Quality Conditioner) to improve power quality and manage effectively equal power sharing between shunt and series inverter of UPQC under electrical faults condition. The UPQC is modeled to protect sensitive load from source side voltage disturbances under nonlinear load conditions. A hybrid power generator that integrates a proton exchange membrane fuel cell (PEMFC) as the main energy source and a super capacitor (SC) as secondary source is proposed to feed the FACT device. In this work, a new control strategy is presented for considering the voltage sag, power factor and total harmonic distortion (THD) as multi-objective of UPQC controller. For this purpose, a new powerful algorithm named virus colony search (VCS) is used for determining the coefficients of the PI controller of UPQC. By using the fuzzification process for the objectives function, a suitable fitness function is established for the optimization method. From the simulations, it can be seen that the results obtained by the proposed algorithm are best and attractive compared to other method. Consequently, the proposed strategy is effective and outstrips other strategies.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

Model parameters identification of the PEMFCs using an improved design of Crow Search Algorithm

There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no environmental pollution, desirable dynamic response, and reliability. Due to this reason, herein, a new method has been presented for optimum identification of the model of the proton exchange membrane fuel cell (PEMFC) model. The major concept is to lessen the sum of squared error (SSE) amount of the observed output voltage and the output voltage of the PEMFC stack by an improved version of Crow Search optimizer (ICSO). To validate the suggested technique, it is applied to two studied cases and the achievements are put in comparison with several newest optimizers, which are Genetic algorithm (GA), Grasshopper Optimizer (GHO), and Salp Swarm Optimizer (SSO). The achievements show that the suggested ICSO gives a better superiority to the other comparative algorithms for optimum estimation of the PEMFC model.     

Dynamic modeling and adaptive control of voltage in proton exchange membrane fuel cell using water management

Maintaining a constant voltage in polymer electrolyte membrane fuel cells (PEMFCs) has always attracted the attention of many researchers, and many articles have been published on this issue. Furthermore, water management in PEMFC has become an important challenge because it can improve cell efficiency and lifetime. This paper will develop a one‐dimensional dynamic model for a single PEMFC, which correlates changes in the cell voltage to changes in the cell current density and humidification rate. Subsequently, a recurrent neural network controller based on the approximation of nonlinear autoregressive moving average model is proposed. The controller manipulates the anode and the cathode water mole fractions in order to fix cell voltage and preserve cell water content within a satisfactory interval regardless of the varying cell current. The model and the controller are simulated in matlab /Simulink (Mathworks Inc., Natick, MA) software, and the results are compared with a PID controller from different viewpoints such as current disturbance and plant parameter variation. Copyright © 2011 John Wiley & Sons, Ltd.     

Hydrogen consumption minimization method based on the online identification for multi-stack PEMFCs system

The proton exchange membrane fuel cell (PEMFC) stacks are not widely used in the field of transportation industry, due to their limited power. Thus, the PEMFC stacks usually connected in parallel or series to meet the load demand power in high-power applications. The hydrogen consumption of multi-stack fuel cells (MFCs) system is related to the efficiency and output power. In addition, the efficiency of PEMFC depends on the applied voltage and other parameters. Consequently, the hydrogen consumption of system changes with varying load, because the system parameters are also varying. It makes reducing the fuel consumption of system a challenging assignment. In order to achieve the goal of minimizing fuel consumption of parallel-connected PEMFCs system, this paper proposes a novel power distribution strategy based on forgetting factor recursive least square (FFRLS) online identification. The FFRLS algorithm is based on data-driven and uses real-time data of the system to improve the estimation accuracy of PEMFC system parameters. On the test bench of parallel-connected PEMFCs system consists of two 300 W PEMFC stacks, PEMFC stack controller, DC/DC converters, and DSP controller etc., a multi-index performance test and comparative analysis are carried out. The results showed that, the performance of proposed power allocation strategy has been successfully validated. In addition, compared with the power average and daisy chain algorithms, the proposed online identification power distribution method can get more satisfactory results. Such as, reducing the hydrogen consumption and improving efficiency.     

Adaptive maximum power point tracking control of fuel cell power plants

A fuel cell's output power depends nonlinearly on the applied current or voltage, and there exists a unique maximum power point (MPP). This paper reports a first attempt to trace MPPs by an extremum seeking controller. The locus of MPPs varies nonlinearly with the unpredictable variations in the fuel cell's operation conditions. Thus, a maximum power point tracking (MPPT) controller is needed to continuously deliver the highest possible power to the load when variations in operation conditions occur. A two-loop cascade controller with an intermediate converter is designed to operate fuel cell power plants at their MPPs. The outer loop uses an adaptive extremum seeking algorithm to estimate the real-time MPP, and then gives the estimated value to the inner loop as the set-point, at which the inner loop forces the fuel cell to operate. The proposed MPPT control system provides a simple and robust control law that can keep the fuel cell working at MPPs in real time. Simulation shows that this control approach can yield satisfactory results in terms of robustness toward variations in fuel cell operation conditions.     

Fuel cell power conditioning system design for residential application

This paper presents a design of a high performance proton exchange membrane fuel cell (PEMFC) power conditioning system (PCS) for residential application. Firstly, a high efficiency PCS topology is described which can improve the PCS maximum efficiency up to 92.9%. Furthermore, a novel PCS controller is presented, which succeeds in suppressing the low frequency current ripple, controlling the dc link voltage and inverter output current. The controller also achieves reliable power grid integration. The experimental results show that a residential fuel cell PCS with high performance can be achieved.     

Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm

In this paper, a new optimization algorithm called Adaptive Sparrow Search Algorithm (ASSA) is proposed for optimal model parameters identification of the proton exchange membrane fuel cell (PEMFC) stacks. The proposed ASSA is utilized for minimizing the sum of squared error (SSE) between the empirical stack voltage and the calculated stack voltage by optimal selection of the mentioned parameters in the PEMFC stack. The method is then performed to three case studies including Ballard Mark V, Horizon H-12, and NedStack PS6 under different operating conditions and give 0.82, 5.14, and 0.097 of SEE which is the least value for all three case studies. The results of the algorithm are compared with some reported works in the literature including CGOA, GRA, and basic SSA to show the method prominence. The final results indicated that the proposed ASSA has the best efficiency toward the others.     

A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.     

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.     

Power management and nonlinear control of a fuel cell–supercapacitor hybrid automotive vehicle with working condition algorithm

This paper deals with the problem of controlling a multi-source system applied in hybrid electrical vehicles. The system consists of a proton exchange membrane fuel cell (PEMFC) and a super capacitor (SC). Fuel cell (FC) provides energy for load as a main power source, and SC helps the system in a load peak or in fast transients. The system is modeled as Port controlled Hamiltonian (PCH), and interconnection and damping assignment passivity based controller (IDA-PBC) is used for a typical hybrid vehicle. The aim is first to support the load power in all circumstances without interruption by combination of FC and SC production, and second to control the DC bus voltage. The purposed system analyzed under standard driving cycle consists of off-load, over-load, and charging conditions of SC. Simulations are accomplished in MATLAB/Simulink software for validation of control strategy and new represented algorithm. The results illustrate that both control method and algorithm can manage power among PEMFC, SC, and the load whereas the DC bus voltage remains near its reference.     

Parallel serpentine-baffle flow field design for water management in a proton exchange membrane fuel cell

In a proton exchange membrane fuel cell (PEMFC) water management is one of the critical issues to be addressed. Although the membrane requires humidification for high proton conductivity, water in excess decreases the cell performance by flooding. In this paper an improved strategy for water management in a fuel cell operating with low water content is proposed using a parallel serpentine-baffle flow field plate (PSBFFP) design compared to the parallel serpentine flow field plate (PSFFP). The water management in a fuel cell is closely connected to the temperature control in the fuel cell and gases humidifier. The PSBFFP and the PSFFP were evaluated comparatively under three different humidity conditions and their influence on the PEMFC prototype performance was monitored by determining the current density–voltage and current density–power curves. Under low humidification conditions the PEMFC prototype presented better performance when fitted with the PSBFFP since it retains water in the flow field channels.     

Modeling and control of a PEM fuel cell based hybrid multilevel inverter

Multilevel inverter is an effective and practical solution for increasing power demand and reducing harmonics of AC waveforms. It is mainly employed in the distributed energy sources area because several batteries, fuel cell and solar cell can be connected through multilevel inverter to feed a load. This paper investigates the potentials of a single-phase Hybrid Cascaded Multilevel Inverter (HCMLI) fed from Proton Exchange Membrane Fuel Cell (PEMFC). A mathematical model of the PEMFC supplying HCMLI has been developed. This paper also presents the effect of a novel hybrid modulation on the device switching losses and harmonics of HCMLI. The proposed hybrid modulation technique combines the fundamental frequency switching scheme and Variable Frequency Inverted Sine Pulse Width Modulation (VFISPWM) technique. A comparison between the hybrid modulation strategy and the conventional Phase Disposition (PD) PWM method is also presented in terms of THD and switching losses. A suitable PID controller has been designed to control the output voltage of fuel cell based HCMLI, so that it can provide constant AC voltage with minimum THD up to the rated conditions. The inverter circuit topology and its control scheme are described in detail and their performance is verified based on simulation and experimental results.     

Optimal design of baffles locations with interdigitated flow channels of a centimeter-scale proton exchange membrane fuel cell

In the present study, the simplified conjugate-gradient method (SCGM) is combined with commercial CFD code to build an optimizer for designing the baffles locations with interdigitated channels of a centimeter-scale proton exchange membrane fuel cell (PEMFC). Using the optimizer, the locations of the baffles are adjusted toward the maximization of the average current density of the flow field. The approach is developed by using the commercial CFD code as the direct problem solver, which is able to provide the numerical solutions for the three-dimensional mass, momentum and species transport equations as well as to predict the electron conduction and proton migration taking place in a PEMFC. Results show that the optimal design process of the locations of the baffles can be completed by using the present optimization approach in just a finite number of iterations. The optimization process may lead to an appreciable increase by 14% in the power output from the fuel cell.     

Whale optimization algorithm based MPPT control of a fuel cell system

Renewable energy sources have provided a great contribution to global energy demand; However, their intermittent characteristics can cause sustainability and efficiency problems. To handle these, alternative systems are utilized. Among these, proton exchange membrane fuel cells (PEMFCs) stand out with their longer lifecycle, efficient, and cost-effective features. However, their performance depends on operating conditions such as temperature, gas pressure, and membrane water content. These nonlinear features require instant and proper control for maximizing efficiency and longer working life. In this study, a whale optimization algorithm (WOA) based maximum power point tracking (MPPT) controller is utilized for a PEMFC system. To validate the proposed controller, the PEMFC system has been analyzed under changing conditions in the MATLAB/Simulink environment. The proposed method has been compared with the other MPPT methods. The results indicate that the proposed controller can provide accurate and fast MPPT performance, less power fluctuations, and higher production efficiency.     

On-line implementation of model free controller for oxygen stoichiometry and pressure difference control of polymer electrolyte fuel cell

This paper proposes and validates a model free controller to improve the real time operating conditions of Proton Exchange Membrane Fuel Cells (PEMFC). This approach is based on an ultra-local model that does not depend on a precise knowledge of the system. It is perfectly adapted to a complex system such as the fuel cell, while benefiting from the ease of online implementation and low computational cost. The designed controller is used to regulate both the oxygen stoichiometry and the membrane inlet pressure, which are crucial operating conditions for the fuel cell's lifetime. The objectives of the proposed control strategy are twofold: preventing the starvation failure, and limiting the potential for mechanical degradation of the membrane during a large pressure difference. The performance of the proposed control strategy is initially evaluated by a simulation environment for both oxygen stoichiometry and inlet pressure difference control of fuel cell stack. An online validation on 1.2 KW fuel cell stack is conducted to control the membrane pressure drop. Two case studies are comprehensively investigated in relation to stoichiometry control: set point tracking and rejection of unmeasured disturbances caused by current variations. Simulations and experimental results reveal that the proposed controller provides significantly better performance in terms of fast trajectory tracking, and ensures less overshoot compared to the Fuzzy PID and PID controller. This efficiency is proven using the Integral Absolute Error (IAE), Integral Squared Error (ISE) and Integral of the Square input (ISU) performance indexes.     

Modeling and control of photovoltaic and fuel cell based alternative power systems

Photovoltaic (PV) systems and fuel cells (FCs) represent interesting solutions as being alternative power sources with high performance and low emission. This work presents a modeling and control study of two power generators; photovoltaic array and fuel cell based systems. An MPPT approach to optimize the PV system performances is proposed. The PV system consists of a PV array connected to a DC-DC buck converter and a resistive load. A maximum power point tracker controller is required to extract the maximum generated power. Based on Incremental Conductance (INC) principle, the idea of the proposed control is to use a Fuzzy Logic Controller (FLC) that allows the choice of the duty cycle step size which is used to be fixed in conventional MPPT algorithms. The variable step is computed according to the value of the PV power-voltage characteristic slope. The second working system comprises a controlled DC-DC converter fed by a proton exchange membrane fuel cell (PEMFC) and supplies a DC bus. The mathematical model of the PEMFC system is given. The converter duty cycle is adjusted in order to regulate the DC bus voltage. Obtained simulation results validate the control algorithms for both of studied power systems.     

Neural network model of 100 W portable PEM fuel cell and experimental verification

The inherent properties of artificial neural networks (ANNs) such as low sensitivity to noise and incomplete information make the ANN a promising candidate to model the fuel cell system. In this paper, an ANN-based model of 100 W portable direct hydrogen fed proton exchange membrane fuel cell (PEMFC) is presented. The model is built based on experimentally collected data from a portable 100 W direct hydrogen fed PEMFC in the authors’ laboratory. A multilayer feedforward ANN with back-propagation training algorithm is used to model the portable PEMFC. The ANN consists of fully connected four layers network with two hidden layers. The PEMFC ANN model is trained using extracted data from experimentally measured and calculated parameters. To validate the model, the outputs of the PEMFC ANN are compared against experimental data and results from a dynamic model of portable direct hydrogen fed PEMFC. In addition, three statistical indices to measure variations, unbiasedness (precision), and accuracy in voltage, power, and hydrogen flow are used to evaluate the PEMFC ANN model performance. The indices indicate that the maximum variations, unbiasedness, and accuracy of the voltage, power, and hydrogen flow are 1.45%, 2.04%, and 1.90%, respectively, which shows a close agreement between the outputs of the PEMFC ANN and the experimental results.