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.     

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.     

A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model

Polarization curves are vital tools for simulation, evaluation, management, and optimization of proton exchange membrane fuel cells (PEMFCs). They are affected by various parameters related to the nonlinearities associated with the electrochemical processes governing fuel cells (FCs). However, the relative importance and effect of each parameter on the polarization curve is different. In order to efficiently estimate the unknown parameters of the PEMFC electrochemical-based model and obtain accurate solutions, in this paper, a backtracking search algorithm combined with Burger's chaotic map (BSABCM) is proposed and investigated. The results related to two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, illustrate the effectiveness of the proposed methodology. Furthermore, the BSABCM presented competitive results when compared with the results of the other metaheuristics.     

Numerical investigation of the leakage and explosion scenarios in China's first liquid hydrogen refueling station

Promoting fuel cells has been one of China's ambitious hydrogen policies in the past few years. Currently, several hydrogen fueling stations (HRSs) are under construction in China to fuel hydrogen-driven vehicles. In this regard, it is necessary to assess the risks of hydrogen leakage in HRSs. Aiming at conducting a comprehensive consequence assessment of liquid hydrogen (LH2) leakage on China's first liquid hydrogen refueling station (LHRS) in Pinghu, a pseudo-source model is established in the present study to simulate the LH2 leakage using a commercial CFD tool, FLACS. The effects of the layout of the LHRS, leakage parameters, and local meteorological conditions on the LH2 leakage consequence has been assessed from the perspectives of low-temperature hazards and explosion hazards. The obtained results reveal that considering the prevailing southeast wind in Pinghu city, the farthest low-temperature hazard distance and lower flammable limit (LFL) -distance occurs in the leakage scenario along the north direction. It is found that the trailer parking location in the current layout of the LHRS will worsen the explosion consequences of the LH2 leakage. Moreover, the explosion will completely destroy the control room and endanger people on the adjacent road when the leakage equivalent diameter is 25.4 mm. The performed analyses reveal that as the wind speed increases, the explosion hazard decreases.     

A parameter optimized model of a Proton Exchange Membrane fuel cell including temperature effects

An electrical equivalent circuit model of the proton exchange membrane (PEM) fuel cell system with parameters extracted through optimization is presented in this paper. The analytical formulation of the fuel cell behavior is based on a set of equations which enables to estimate his overall performance in terms of operation conditions without extensive calculations. The approach uses a set of parametrical equations and related parameters in order to characterize and predict the voltage–current characteristics of the fuel cell operation without examining in depth all physical/chemical phenomena, but including within the model different components and forms of energy actuating in the generation process. Although many models have been reported in the literature, the parameter extraction issue has been neglected. However, model parameters must be precisely identified in order to obtain accurate simulation results. The main contribution of this work is the application of Simulated Annealing (SA) as optimization method focused on the extraction of the PEM model parameters. Model validation is carried out comparing experimental and simulated results. The good agreement between the simulation and experimental results shows that the proposed model provides an accurate representation of the static and dynamic behavior for the PEM fuel cell. Therefore, the approach allows at getting the set of parameters within analytical formulation of any fuel cell. In consequence, fuel cell performance characteristics are well described as they are carried out through a methodology that simultaneously calibrates the model.     

Intensifying vehicular proton exchange membrane fuel cells for safer and durable,design and operation

The explosion in a proton exchange membrane fuel cell (PEMFC) powered forklift in Louisiana, USA in May 2018 and the resulting fatality highlights the need for the improved safety of this technology. Apart from the safety concerns, PEMFC durability has been an important issue towards its further commercialization. Both the safety and durability concerns associated with this technology can be attributed to the temporal degradation of its components. In this study, we have developed a mathematical model that relates the microscale PEMFC degradation to the probability of a macroscale explosion in a Fuel Cell Electric Vehicle (FCEV). Using the model and the inherent safety principle of intensification, it was observed that increasing the operating temperature of the PEMFC system can significantly improve both its safety and durability while intensifying membrane design parameters i.e. membrane thickness and membrane conductivity do not provide any significant improvements. A key inference from this study is that the durability (expressed in voltage loss) and safety (expressed in explosion probability) of a PEMFC system are not perfectly correlated.     

A simple model for calculating peak pressure in vented explosions of hydrogen and hydrocarbons

The authors presented a basic mathematical model for estimating peak overpressure attained in vented explosions of hydrogen in a previous study (Sinha et al. [1]). The model focussed on idealized cases of hydrogen, and was not applicable for realistic accidental scenarios like presence of obstacles, initial turbulent mixture, etc. In the present study, the underlying framework of the model is reformulated to overcome these limitations. The flame shape computations are simplified. A more accurate and simpler formulation for venting is also introduced. Further, by using simplifying assumptions and algebraic manipulations, the detailed model consisting of several equations is reduced to a single equation with only four parameters. Two of these parameters depend only on fuel properties and a standard table provided in the Appendix can be used. Therefore, to compute the overpressure, only the two parameters based on enclosure geometry need to be evaluated. This greatly simplifies the model and calculation effort. Also, since the focus of previous investigation was hydrogen, properties of hydrocarbon fuels, which are much more widely used, were not accounted for. The present model also accounts for thermo-physical properties of hydrocarbons and provides table for fuel parameters to be used in the final equation for propane and methane. The model is also improved by addition of different sub-models to account for various realistic accidental scenarios. Moreover, no adjustable parameters are used; the same equation is used for all conditions and all gases. Predictions from this simplified model are compared with experimentally measured values of overpressure for hydrogen and hydrocarbons and found to be in good agreement. First the results from experiments focussing on idealized conditions of uniformly mixed fuel in an empty enclosure under quiescent conditions are considered. Further the model applicability is also tested for realistic conditions of accidental explosion consisting of obstacles inside the enclosure, non-uniform fuel distribution, initial turbulent mixture, etc. For all the cases tested, the new simple model is found to produce reasonably good predictions.     

A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model

Accurate kinetic models are of great significance for the simulation and analysis for hydrogen fuel cells. The proton exchange membrane (PEM) fuel cell is a complex nonlinear, multi-variable system. The mathematical modeling of PEM fuel cell usually leads to nonlinear parameter estimation problems which often contain more than one minimum. In this paper, a novel bio-inspired P systems based optimization algorithm, named BIPOA, is proposed to solve PEM fuel cell model parameter estimation problems. In BIPOA, the nested membrane structure and new rules such as adaptive mutation rule, partial migration rule and autophagy rule are combined to improve the algorithm's global search capacities and convergence accuracy. Studies on some benchmark test functions indicate that the BIPOA outperforms the other two methods (PSOPS and GAs) in both convergence speed and accuracy. In addition, experimental results reveal that the model predictive outputs are in better agreement with the actual experimental data. Therefore, the BIPOA is a helpful and reliable technique for estimating the PEM fuel cell model parameters and is available to other complex parameter estimation problems of fuel cell models.     

Effect of nitrogen on deflagration characteristics of hydrogen / methane mixture

In order to study the influence of nitrogen on the deflagration characteristics of premixed hydrogen/methane, the explosion parameters of premixed hydrogen/methane within various volume ratios and different dilution ratios were studied by using a spherical flame method at room temperature and pressure. The results are as follows: The addition of nitrogen makes the upper limit of explosion of hydrogen/methane premixed gas drop, and the lower limit rises. For explosion hazard (F-number), hydrogen/methane premixed fuel with a hydrogen addition ratio of 10% has the lowest risk, and nitrogen has a greater impact on the dangerous degree of hydrogen and methane premixed gas whose hydrogen addition ratio does not exceed 30%. In terms of flame structure, the spherical flame was affected by buoyancy instability as the percentage of nitrogen dilution increased, but the buoyancy instability gradually decreased as the percentage of hydrogen addition increased. The addition of diluent gas reduces the spreading speed of the stretching flame and reduces the stretching rate in the initial stage of flame development. The laminar flame propagation velocity calculated by the experiment in this paper is consistent with the laminar flow velocity of the hydrogen/methane premixed gas calculated by GRI Mech 3.0. Considering the explosion parameters such as flammability limit, laminar combustion rate and deflagration index, when hydrogen is added to 70%, it is the turning point of hydrogen/methane premixed fuel.     

Methanol crossover in direct methanol fuel cells: a link between power and energy density

Direct methanol fuel cell performance curves were obtained as a function of three parameters, (1) temperature, (2) fuel flow-rate and (3) concentration. Methanol crossover was measured by gas chromatography as a function of these three parameters at 100 mA/cm² in the single-pass fuel delivery mode. The data was used to model a continuous loop mode where pure methanol is injected into a loop that circulates through the flow-field and recovers water from the cathode. The modeled loop composition is identical to the fuel stream used in the single-pass experiments (dilute aqueous methanol). The model results, presented in three-dimensional surfaces, elucidate the impact of parameter variations on the energy and power density of the direct methanol fuel cell (DMFC) and the link between those two figures of merit. In addition, a reasonable estimate of the contribution of mass transport effects due to the carbon fabric current collectors is made along with in situ CO stripping experiments on membrane electrode assembly (MEA) anode surfaces. The analysis shows that, at present, serious compromises are required if reasonable energy and power densities are to be simultaneously maintained in DMFCs using Nafion™ 117 as an electrolyte.     

The optimal performance estimation for an unknown PEMFC based on the Taguchi method and a generic numerical PEMFC model

In this paper, a new approach to estimate the optimal performance of an unknown proton exchange membrane fuel cell (PEMFC) has been proposed. This proposed approach combines the Taguchi method and the numerical PEMFC model. Simulation results obtained using the Taguchi method help to determine the value of control factors that represent the tested unknown PEMFC. The objective of reducing both fuel consumption and operation cost can be achieved by determining the parameters for the unknown PEMFC. In addition, the optimal operation power for the tested unknown PEMFC can also be predicted. Experimental results on the test equipment show that the proposed approach is effective in optimal performance estimation for the tested unknown PEMFC, thus demonstrating the success achieved by combining the Taguchi method and the numerical PEMFC model.     

Nonlinear D-optimal design of experiments for polymer–electrolyte–membrane fuel cells

Using empirical models, parameters have to be estimated from experimental data. Experimental characterization of fuel cell stacks is an expensive and time-consuming task. Therefore it is very important to choose an experimental design, which maximizes the statistical quality of the resulting information. Box and Lucas (Biometrika 46 (1959)) showed that it is possible to optimize nonlinear experimental designs by the minimization of the covariance matrix of the least squares estimate. The aim of this work is to adopt this general method in order to investigate its ability for application in polymer–electrolyte–membrane fuel cell (PEMFC) characterization. Based on an empirical PEMFC model a D-optimal design criterion has been developed and validated. Numerical methods, evolutionary and heuristic are investigated with respect to fast and robust evaluation of the design criterion. For a given set of experimental data best results are achieved using a heuristic approach, a so-called sequential search. Based on that result an algorithm to obtain an optimal design of experiments (DoE) in a nondeterministic operating area is introduced. The proposed algorithm is able to take into account experimental limitations due to test facilities or examinee. The algorithm further allows to include existing and for reference needed experiments.     

Shock waves in multiphase flow of fuel–coolant interaction

The interaction of fuel and coolant (FCI) is a complex multiphase process due to the fact that the fragmentation and heat transfer process are not easy to cast in simple mathematical formulas. This paper, a theoretical model has been developed by considering multiphase flow shock wave propagating of fuel–coolant interaction. Analysis of a steady-state vapour explosion in one dimension has been carried out by applying the conservation laws of mass, momentum, energy and the appropriate equation of state for an interaction of molten dioxide uranium and water. Using the model, we predicted the pressure magnitudes behind shock wave of vapour explosion varied with the initial volume fraction of vapour, melt mass concentrations, liquid entrained fraction and when they were considered as dangerous.     

Determination of household energy using ‘fingerprints’ from energy billing data

Electric and gas utilities (in the U.S.A.) bill their customers on a regular basis, usually monthly or bimonthly. These data provide a truly valuable information resource for energy conservation programme analysts and evaluators. This paper discusses ways to analyse such billing data. The starting point is the Princeton University score-keeping model, which permits decomposition of total household energy use into its weather-and non-weather-sensitive elements; the weather-sensitive portion is assumed to be proportional to heating degree days. The score-keeping model also allows one to compute weather-adjusted energy consumption for each household based on its billing data and model parameters; this is the model's estimate of annual consumption under long-run weather conditions. The methods discussed here extend the score-keeping results to identify additional characteristics of household energy use. The methods classify households in terms of the intensity with which the particular fuel is used for space heating (primary heating fuel vs. supplemental heating fuel vs. no heating at all with the fuel). In addition, households that use the particular fuel for air conditioning are identified. In essence, the billing data and model results define household energy use ‘fingerprints’. The billing data and model results can also be used to identify and correct anomalous bills. Finally, the methods permit careful examination and analysis of changes in energy use from one year to another. They help explain why some households show anomalously large energy savings (e.g. they began using wood as a heating fuel during the second year) or negative energy savings (e.g. very high air conditioning energy use during the second year).     

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.     

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.     

Control of a fuel cell based on the SIRMs fuzzy inference model

A typical working mode for the fuel cell hybrid system is that the fuel cell produces constant power output while the auxiliary storage energy device such as ultracapacitor or battery provides the deviation between the desired power demand and the value of the actual operation. This paper concentrates on the control of the fuel cell flow system. The system which is like an under-actuated mechanical system needs to control two objects (the cathode pressure and the air flow) with one manipulated variable (the set voltage of the air mass flow controller). A SIRMs-based fuzzy inference model is successfully implemented in the system. Online random search optimization algorithm based on Simulink C-S functions is developed to adjust the parameters in the model. By alternative control of the two objects, experimental results demonstrate the realization of the control strategy with one adjustment means.     

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.     

Hydrodynamics of the initial phase of explosion

Physical and hydrodynamic processes accompanying explosions of condensed explosives and fuel–air mixtures have been considered. Wide-range equations of the state of explosion products and air have been derived. A physical model and a program code based on the equations of gas dynamics in Lagrangian representation have been developed for modeling one-dimensional hydrodynamic processes in the near zone of explosion. The described model has shown its working efficiency within a wide range of explosion energies and environmental conditions.     

Novel dynamic semiempirical proton exchange membrane fuel cell model incorporating component voltages

This paper introduces a novel dynamic semiempirical model for the proton exchange membrane fuel cell (PEMFC). The proposed model not only considers the stack output voltage but also provides valid waveforms of component voltages, such as the no‐load, activation, ohmic, and concentration voltages of the PEMFC stack system. Experiments under no‐load, ramping load, and dynamic load conditions are performed to obtain various voltage components. According to experimental results, model parameters are optimised using the lightning search algorithm by providing valid theoretical ranges of parameters to the lightning search algorithm code. In addition, the correlation between the vapour and water pressures of the PEMFC is obtained to model the component voltages. Finally, all component voltages and the stack output voltage are validated by using the experimental/theoretical waveforms mentioned in previous research. The proposed model is also compared with a recently developed semiempirical model of PEMFC through particle swarm optimisation. The proposed dynamic model may be used in future in‐depth studies on PEMFC behaviour and in dynamic applications for health monitoring and fault diagnosis.