Optimizing circuit parameters of the biohydrogen real-time power generating system by genetic algorithm

This study performs optimal estimation of circuit parameters for a biohydrogen real-time power generating system by using a penalty-function genetic algorithm (GA). Circuit parameters of this system change with operating temperature and current density; some circuit parameters are nonlinear. To elucidate the circuit characteristics of the whole system, this study uses penalty-function GA to optimally estimate circuit parameters using data from a V–I$V–I$ characteristic experiment on novel biohydrogen real-time power generating system. This study then solves the circuit characteristic by the estimated circuit parameters formulated utilizing Kirchhoff's law. Then, the estimated V–I$V–I$ characteristic is then compared with actual measurements to verify the feasibility of this novel approach. In the same manner, the capacitor parameter of the biohydrogen real-time power generating system can be estimated to identify the alternating current (AC) equivalent circuit for this system.     

Parameter extraction of fuel cells using hybrid interior search algorithm

Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current‐voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.     

Metamodel based design optimization approach in promoting the performance of proton exchange membrane fuel cells

This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density.     

Electrolyzer-fuel cell combination for grid peak load management in a geothermal power plant: Power to hydrogen and hydrogen to power conversion

This study provides comprehensive energy, exergy, and economic evaluations and optimizations of a novel integrated fuel cell/geothermal-based energy system simultaneously generating cooling and electricity. The system is empowered by geothermal energy and the electricity is mainly produced by a dual organic cycle. A proton exchange membrane electrolyzer is employed to generate the oxygen and hydrogen consumed by a proton exchange membrane fuel cell utilized to support the network during consumption peak periods. This fuel cell can be also used for supplying the electricity demanded by the network to satisfy the loads at different times. All the simulations are conducted using Engineering Equation Solver software. To optimize the system, a multi-objective optimization method based on genetic algorithm is applied in MATLAB software. The objective functions are minimized cost rate and maximized exergy efficiency. The optimum values of exergy efficiency and cost rate are found to be 62.19% and 18.55$/h, respectively. Additionally, the results reveal that combining a fuel cell and an electrolyzer can be an effective solution when it comes to electricity consumption management during peak load and low load periods.     

An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells

The accurate mathematical model is the key issue to simulation and design of the fuel cell power systems. Aiming at estimating the proton exchange membrane fuel cell (PEMFC) model parameters, an adaptive RNA genetic algorithm (ARNA-GA) which is inspired by the mechanism of biological RNA is proposed. The ARNA-GA uses the RNA strands to represent the potential solutions and new genetic operators are designed for improving the global searching ability. In order to maintain the population diversity and avoid premature convergence, on the basis of the dissimilarity coefficient, the adaptive genetic strategy that allows the algorithm dynamically select crossover operation or mutation operation to execute is proposed. Numerical experiments have been conducted on some benchmark functions with high dimensions. The results indicate that ARNA-GA has better search capability and a higher quality of solutions. Finally, the proposed approach has been applied for the parameter estimation of PEMFC model and the satisfactory results are reached.     

Phosphosilicate nano-network (PPSN)-Polybenzimidazole (PBI) composite electrolyte membrane for enhanced proton conductivity,durability and power generation of HT-PEMFC

Here we report a composite electrolyte membrane of Polybenzimidazole (PBI) with Phosphosilicate nano-network (PPSN) for enhanced proton conductivity, durability and power generation of high temperature polymer electrolyte membrane fuel cell (HT-PEMFC). Solid state proton conductor three dimensional Phosphosilicate nano-network (average particle size <10 nm) is synthesized using easy and low-cost sol gel method followed by ball milling and composited with PBI at different loading employing methane sulfonic acid (MSA) as solvent. The electrolyte membrane is characterized using FESEM, XRD, FTIR, TGA; proton conductivity, ion exchange capacity, water uptake and acid doping level, chemical stability and mechanical yield strength are measured and the membrane is tested for HT-PEMFC application. Property and performance mapping reveals that with 10% PPSN loading, composite (PPSN-PBI-10) membrane offers the maximum enhancement of all properties and power generation of HT-PEMFC, while beyond a critical loading (～22%) properties and performance deteriorate below that of pristine PBI. Using optimum loading of PPSN, compared to pristine PBI, a remarkable rise in water uptake and acid doping level is achieved that facilitates proton conduction; also in spite of the presence of Phosphoric acid in the PPSN filler, the maximum 47.5% enhancement of ultimate strength is attained. The performance of HT-PEMFC using composite PPSN-PBI unveil that almost 2 times (100%) enhancement of peak power generation (～0.73 W cm^?2) is achieved using PPSN-PBI-10 at 170 °C operating temperature compared to pristine PBI. This may be attributed to the facilitated proton conduction through the extended tunnelling network offered by PPSN. Incorporation of PPSN improves the durability; over 48 h only 16% decay in voltage is noticed using PPSN-PBI-10 membrane which is remarkably lower than the 31% decay of pristine PBI membrane.     

Parameter identification of an SOFC model with an efficient,adaptive differential evolution algorithm

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as IJADE (Improved Jingqiao Adaptive DE). To verify the performance of IJADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, IJADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed.     

Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico

Renewable generating systems are alternative to produce electric energy in a clean manner. However, the high costs of the constituents limit their broad use. Thus, sizing is an important issue in the renewable generating systems design, in order to reach an efficient relationship between cost and benefit. Likewise, the random nature of the sources makes the sizing a complex task with regard to a conventional system. This paper is focused on calculate the optimal size of a wind-photovoltaic-fuel cell system to meet the power demand of an isolated residential load located in the south-east region of Mexico (Chetumal city 18°31′21.4″N 88°16′11.3″W), with a solar radiation range from 0 to 0.75 kW/m² and wind speed range from 5 to 7.8 m/s. Swarm intelligence techniques have been successfully applied in solving many combinatorial optimization problems in which the objective space possesses many local optimal solutions. This work employs the Particle Swarm Optimizer (PSO) algorithm to search the optimal sizing for the power plant minimizing the total costs of the system; as a metaheuristic procedure, the PSO was able to find the best configuration regardless the lack of a deep knowledge of the problem. Compared against the Differential Evolution (DE) technique, the PSO performance is faster and able to provide a configuration that saves around 10% of the total cost of the hybrid system.     

Hybrid artificial bee colony algorithm for parameter estimation of proton exchange membrane fuel cell

The accurate electrochemical model plays an important role in design and analysis of hydrogen fuel cell systems. For the purpose of estimating parameters of the proton exchange membrane fuel cell (PEMFC) model, and inspired by the foraging behavior of bacteria and bees, a hybrid artificial bee colony (HABC) algorithm is proposed. The HABC uses an improved solution search equation that mimics the chemotactic effect of bacteria to enhance the local search ability. To avoid premature convergence and improve search accuracy, the adaptive Boltzmann selection scheme is adopted, which adjusts selective probabilities in different stages. Performance testing has been conducted on some typical benchmark functions. The results demonstrate that the HABC outperforms other methods (BIPOA, PSOPS and two improved GAs) in both convergence speed and accuracy. The proposed approach is applied to estimate the PEMFC model parameters and the satisfactory model predictive curves are obtained. More experimental results in different search ranges and validate strategies indicate that HABC is an efficient technique for the parameter estimation problem of PEMFC.     

Ultra-low Pt loading for proton exchange membrane fuel cells by catalyst coating technique with ultrasonic spray coating machine

This paper reports use of an ultrasonic spray for producing ultra-low Pt load membrane electrode assemblies (MEAs) with the catalyst coated membrane (CCM) fabrication technique. Anode Pt loading optimization and rough cathode Pt loading were investigated in the first stage of this research. Accurate cathode Pt coating with catalyst ink using the ultrasonic spray method was investigated in the second stage. It was found that 0.272 mg_Pt/cm² showed the best observed performance for a 33 wt% Nafion CCM when it was ultrasonically spray coated with SGL 24BC, a Sigracet manufactured gas diffusion layer (GDL). Two different loadings (0.232 and 0.155 mg_Pt/cm²) exposed to 600 mA/cm² showed cathode power mass densities of 1.69 and 2.36 W/mg_Pt, respectively. This paper presents impressive cathode mass power density and high fuel cell performance using air as the oxidant and operated at ambient pressure.     

Circular genetic operators based RNA genetic algorithm for modeling proton exchange membrane fuel cells

Inspired by the biological RNA, a circular genetic operators based RNA genetic algorithm (cRNA-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the population diversity and avoid premature convergence, we design the novel genetic operator of the double-loop crossover operator. To allow the algorithm to jump out of local optima, the adaptive mutation probabilities are presented and the stem-loop mutation operator is adopted with the other mutation operators. The simulated annealing method is also incorporated into the cRNA-GA to improve local search ability. Performance tests conducted on some typical benchmark functions have witnessed the validity of cRNA-GA. The cRNA-GA is also applied to estimate the parameters of the PEMFC model and the satisfactory results have shown its effectiveness.     

A novel material fabrication method for the PEM fuel cell bipolar plate

Gelcasting, a near net-shape forming process, is suitable for manufacturing of structural ceramics with various shapes. In this study, the gelcasting process was adopted to obtain the material for PEM fuel cell bipolar plates. The mesocarbon microbead (MCMB) that has the unique self-sintering property was chosen as the starting material. In order to optimize the MCMB suspensions for gelcasting, the zeta potentials of the MCMB particles dispersing in water were investigated. A stable MCMB suspension with solid loading up to 66.7 wt.% was prepared from which the uniform green parts with complex structures were successfully molded. Finally, the physical properties of green parts and sintered samples were evaluated.     

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer’s objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies.To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.     

PEM fuel cell supported distribution static compensator for power quality enhancement in three-phase four-wire distribution system

One of the major problems in electrical power system is the lack of quality of power due to the rapid growth of nonlinear load and unbalanced load utilization in three-phase four-wire distribution system. In this paper, PEM (Proton Exchange Membrane) fuel cell supported four-leg Distribution Static Compensator (DSTATCOM) is modelled to mitigate harmonics, neutral current and load balancing under nonlinear load and unbalanced load conditions in three-phase four-wire distribution system. The instantaneous reactive power (IRP) theory control algorithm is proposed for four-leg DSTATCOM. The Real coded Genetic Algorithm (RGA) optimized Proportional Integral (PI) controller and Adaptive Neuro Fuzzy Inference System (ANFIS) controller are used for regulating the DC link voltage of DSTATCOM. This paper also investigates the performance of ANFIS based DSTATCOM with conventional method. The proposed system is modelled and its performance is analyzed in MATLAB/SIMULINK.     

An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)

To accurately model the PV module, it is crucial to include the effects of irradiance and temperature when computing the value of the model parameters. Considering the importance of this issue, this paper proposes an improved modeling approach using differential evolution (DE) method. Unlike other PV modeling techniques, this approach enables the computation of model parameters at any irradiance and temperature point using only the information provided by the manufacturer’s data sheet. The key to this improvement is the ability of DE to simultaneously compute all the model parameters at different irradiance and temperature. To validate the accuracy of the proposed model, three PV modules of different types (multi-crystalline, mono-crystalline and thin-film) are tested. The performance of the model is evaluated against the popular single diode model with series resistance R_s. It is found that the proposed model gives superior results for any irradiance and temperature variations. The modeling method is useful for PV simulator developers who require comprehensive and accurate model for the PV module.     

Water removal characteristics of proton exchange membrane fuel cells using a dry gas purging method

Water removal from proton exchange membrane fuel cells (PEMFC) is of great importance to improve start-up ability and mitigate cell degradation when the fuel cell operates at subfreezing temperatures. In this study, we report water removal characteristics under various shut down conditions including a dry gas-purging step. In order to estimate the dehydration level of the electrolyte membrane, the high frequency resistance of the fuel cell stack was observed. Also, a novel method for measuring the amount of residual water in the fuel cell was developed to determine the amount of water removal. The method used the phase change of liquid water and was successfully applied to examine the water removal characteristics. Based on these works, the effects of several parameters such as purging time, flow rate of purging gas, operation current, and stack temperature on the amount of residual water were investigated.     

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.     

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.     

Understanding and approaches for the durability issues of Pt-based catalysts for PEM fuel cell

The state-of-art understanding of durability issues (the degradation reasons and mechanisms, the influence of working conditions, etc.) of Pt-based catalysts for proton exchange membrane fuel cell (PEMFC) and the approaches for improving and studying catalyst durability are reviewed. Both carbon support and catalytic metals degrade under PEMFC conditions, respectively, through the oxidation of carbon and the agglomerate and the detachment from support materials of catalytic metals, especially under unnormal working conditions; furthermore, the degradation of carbon support and catalytic metals interact with and exacerbate one another. The working temperature, humidity, cell voltage (the electrode potential and the mode applied on the electrode), etc. can influence the catalyst durability. Carbons with high graphitization degree as support materials and alloying Pt with some other metals are proved to be effective ways to improve the catalyst durability. Time-effective and reliable methods for studying catalyst durability are indispensable for developing PEMFC catalysts.