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An effective online fault diagnosis system is of great significance to improve the reliability of fuel cell vehicles. In this paper, a fault diagnosis model for proton exchange membrane fuel cells is proposed. Firstly, the tests of electrochemical impedance spectroscopy under different fault types (flooding, drying, air starvation) and fault degrees (minor, moderate, severe) are carried out, and each polarization loss of the fuel cell is denoted by an equivalent circuit model (ECM). Then, the parameters of the ECM are identified by the proposed random mutation differential evolution algorithm. Furthermore, the parameters identified under different fault conditions are used to train and test a probabilistic neural network-based fault diagnosis model. The fault diagnosis model achieves diagnosis accuracies of 100% for the fault type and 96.67% for the fault degree. By setting operating conditions with different fault degrees, the fault diagnosis model proposed in this paper can realize the fault type and fault degree diagnosis, effectively avoiding the misjudgment of fault types, and is effective for improving the reliability of the fuel cell system. [Display omitted] • A RMDE algorithm is proposed to realize the impedance parameter identification. • A fault diagnosis model based on the probabilistic neural network is developed. • The diagnosis accuracies for fault type and fault degree reach 100% and 96.67%. [ABSTRACT FROM AUTHOR]

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match. • A new signal processing-based IDM is presented for hydrogen energy-based DGs. • A UWT-based IDM with an improved pyramidal algorithm was implemented in real-time. • The proposed IDM provides both zero NDZ and short detection time. • Threshold values can be selected easily and noise sensitivity is more reliable. • Only PCC voltage signal is required, thus, loss of computation time is minimum. [ABSTRACT FROM AUTHOR]

Establishing a reasonable energy management strategy (EMS) is the key to improve the service durability, power performance and fuel economy of the fuel cell hybrid electric vehicle (FCHEV). This paper obtains energy distribution optimal solution for the fuel cell hybrid bus (FCHB) based on Pontryagin's minimum principle (PMP) algorithm, and the problems of inaccurate estimation of motor power and difficult real-time application are solved. Firstly, the driving feature recognition is completed by collecting the motor output power directly when the FCHB stops at the station. On the basis of it, the sub-optimal co-state value is chosen. Secondly, the sub-optimal co-state is used to complete the real-time application of PMP algorithm in the driving segment. The results are acquired through the simulation and the actual comparison experiment, compared with rule-based simulation and rule-based actual experiment, the hydrogen consumption of the proposed strategy decreases by 20.3% and 28.9% on average. Moreover, the online computation time per step of the proposed strategy is 3.64 ms averagely, less than sampling time interval 1s. It is shown that the proposed method has lower hydrogen consumption rate and excellent real-time performance. [Display omitted] • A method of driving feature recognition based on DBSCAN algorithm is proposed. • A real-time PMP EMS based on bus driving feature recognition is proposed. • The proposed EMS can realize real-time optimization without known road condition. • Hydrogen consumption is close to that of known road condition PMP strategy. [ABSTRACT FROM AUTHOR]

Energy management is a critical issue for the economical and optimum operation of grid integrated renewable energy systems. In conventional grid integrated systems, the control methods are operated according to the design criteria for grid connection modes and cause surplus energy for low-loading conditions under grid-failure situations. For this purpose, in this paper, a flexible control strategy is improved basis on perturb & observe MPPT, and it is purposed to improve the system efficiency under grid-connection and grid-failure modes of renewable energy systems. In this way, it is aimed to ensure optimized power delivering capability with optimum load voltages/currents at desired values, and it also prevents overloading under grid-failure mode. To show the proposed method's validity, the algorithm of control method is embedded in a function block and performed for grid connection & grid-failure modes under different load conditions. The performance results show that the flexible control strategy prevents surplus energy under the grid-failure mode and reduces the power losses compared to the conventional method. Also, the proposed method increases the load-side power quality. The results show that THD values are significantly reduced to fewer than the conventional perturb & observe method. • A flexible control strategy for grid-connection and grid-failure modes. • Optimum energy generation for local loads under grid-failure mode. • Comparison with conventional perturb & observe method based system. • Enhancement of electrical energy quality at load-side. [ABSTRACT FROM AUTHOR]

Microbial electrolysis cell (MEC) is a promising reactor. However, currently, the reactor cannot be adapted for industrial-scale biohydrogen production. Nevertheless, this drawback can be overcome by modeling studies based on mathematical equations. The limitation of analytical instrumentation to record the non-linearity of the dynamic behavior for biohydrogen processes in an MEC has led to the introduction of computational approach that has the potential to reduce time constraints and optimize experimental costs. Reviews of comparative studies on bioelectrochemical models are widely reported, but there is less emphasis on the MEC model. Therefore, in this paper, a comprehensive review of the MEC mathematical model will be further discussed. The classification of the model with respect to the assumptions, model improvement, and extensive studies based on the model application will be critically analyzed to establish a methodology algorithm flow chart as a guideline for future implementation. [Display omitted] • Advancement of MEC mathematical models. • Classification of MEC mathematical models. • Proposed guideline for the MEC mathematical model implementation. [ABSTRACT FROM AUTHOR]

Regarded as a promising technology, proton exchange membrane fuel cell (PEMFC) are not far from a large-scale deployment. However, some improvements are still needed to extend the lifetime of these systems. The discipline of PHM (Prognostic and health management) seems like a great solution to help against this problem. The objective is to predict the evolution of the behavior of a system using algorithms to estimate in advance when a fault occurs. This knowledge of the default before its occurrence allows to anticipate a decision, often by using a fault-tolerant control. Different methodologies exist to make a prognostic algorithm: model based, data based or a hybridization between these two previous methodologies. This paper will focus on the data based prognosis, mainly due to the fact that all of the phenomena involved in the degradation of a PEMFC are not yet fully known, thus not yet modeled. The first innovation of this paper concern the use of a new neural network paradigm, the Echo State Network, which is a part of Reservoir Computing methods. This new paradigm gives very interesting results, with a mean average percentage error less than 5% in our study case. The other contribution is the definition of a filtering method, regarding to the test bench, by evaluating the Hurst exponent of the signal filtered by wavelet. [ABSTRACT FROM AUTHOR]

In this paper, a method based on alternating direction method of multipliers (ADMM) algorithm is proposed to solve the energy management problem for fuel cell vehicles (FCVs). Taking the minimum hydrogen consumption as the objective function, a new fuel cell dynamical system and a whole vehicle model that conform to the property of convex function are constructed, and an augmented Lagrangian equation with its scaled dual form is established, which satisfies the standard normal form of ADMM algorithm. For the first time, the ADMM algorithm is applied to energy management system of FCVs. In the meantime, a cyclic constraint inspection strategy is introduced to have a further precise control of the power of the fuel cell system so that it can meet the dynamic change constraints of the power. Compared with dynamic programming (DP) algorithm off-line calculation results, the simulation results of the proposed method indicate that the calculation of this algorithm is approximately 20–200 times faster than the former in 9 standard driving cycles. And under the two energy management modes by power maintenance and consumption, the deviation results in terms of the hydrogen consumption derived from the proposed method and DP were 2.37% and 1.06% respectively. • The ADMM algorithm is applied to energy management system of FCVs. • A cyclic constraint inspection strategy is introduced. • Approximately 20 to 200 times faster than dynamic programming. • The results of hydrogen consumption calculated by ADMM have a difference of less than 5% with DP. [ABSTRACT FROM AUTHOR]

The Wavelet Transform (WT) is a mathematical method used to represent a given signal in different scales. This method is also used to extract the different frequency bands (or different frequency components) from a signal. In this paper, wavelet transform in the domain of energy management of Electrical Vehicles (EV) is considered. A method for choosing the mother wavelet and the decomposition level will be presented. Taking into account the damages caused by the extreme variations of the power demand on the power sources, this paper shows how Wavelet Transform can help eliminating this negative impact. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid metaheuristic optimizer called CHDJ to identify accurate and reliable values for the model parameters of solid oxide fuel cells (SOFC). CHDJ is a hybridizer of differential evolution (DE) and Jaya algorithm. The core of CHDJ lies in a developed competitive hybrid operation. In this operation, three vectors including the DE mutant vector, the Jaya mutant vector, and the crossover vector that constituted by the foregoing two mutant vectors form a tri-competitive mechanism together. This mechanism can contribute to exploring the global space extensively and exploiting the local region meticulously to generate promising candidate solutions. To verify the performance of CHDJ, two SOFC models including a cylindrical cell and a tubular stack operating at different pressures and temperatures are employed. Experimental results demonstrate that these three vectors interact and cooperate organically. They make CHDJ take full advantages of the exploration of DE and the exploitation of Jaya effectively. The optimal values of the mean square error (MSE) between the measured voltage and the calculated voltage obtained by CHDJ on the cylindrical cell at 1073K, 1173K, 1213K, and 1273K are 2.9215E-06, 1.4610E-06, 1.9093E-06, and 2.4682E-06, respectively. On the tubular stack, for the operating conditions of 3atm constant pressure at 873K, 923K, 973K, 1023K, and 1073K, the optimal MSE values are 2.7489E-05, 1.6169E-04, 6.9207E-04, 1.4833E-03, and 1.5876E-03, respectively. For the operating conditions of 1073K constant temperature under 1atm, 2atm, 3atm, 4atm, and 5atm, the results are 1.5293E-03, 1.6289E-03, 1.5876E-03, 1.5868E-03, and 1.5870E-03, respectively. Comparisons with other algorithms show that CHDJ achieves better overall performance. It can skip local optima and find optimal solutions, and thus can serve as a hopeful candidate method for parameter identification of SOFC models. • A hybrid algorithm (CHDJ) is proposed for parameter identification of SOFC models. • A competitive hybrid operation based on a tri-competitive mechanism is designed. • Exploration of differential evolution and exploitation of Jaya are integrated. • CHDJ is applied to one cylindrical cell and one tubular stack. • CHDJ is verified from parameter accuracy, robustness, convergence, and statistics. [ABSTRACT FROM AUTHOR]

Surrogate models that predict the behaviors of solid oxide cells (SOCs) accurately at low computational cost are crucial to the control and optimization of SOC plants. Lumped physical models of SOCs, while widely used in such applications, lack accuracy because of neglected physical details. Data-driven models are the other options of surrogate models, which are proved to be more accurate because these models are identified directly from experiments or numerical simulations. However, due to the time cost of experiments and numerical simulations of SOCs, it is hoped that data-driven models can be constructed from a minimum amount of data. Also, the trained data-driven models should be robust, in other words, insensitive to the data set as well as the initial settings. These requirements are hard to be achieved by existing data-driven models of SOCs, such as lookup tables and artificial neural networks (ANNs). Aiming to preserve robustness and reduce the required amount of data, this paper introduces an adaptive polynomial approximation (APA) method, which is derived from the latest findings of numerical computation science, to the surrogate modeling of SOCs. The obtained models by the APA method are validated by both experiments and simulations. By analyzing the models, the coupling relationship among operating parameters of SOCs is revealed. The physical interpretability makes the APA method distinctive from common data-driven modeling methods. Performance comparison shows that the APA method is more accurate and robust than the existing ones with similar sampling costs. Additionally, the APA method can control the accuracy of the model by setting an error criterion in the algorithm iteration, endowing the APA method with an error control ability as per different accuracy requirements for SOC modeling. • A data-driven method to construct surrogate models of SOC is introduced. • The models built by this method are validated by both experiments and simulations. • The models built by this method are physically interpretable. • The accuracy of this method is directly controlled by an error control parameter. • With similar time cost, this method is more robust and accurate than existing ones. [ABSTRACT FROM AUTHOR]

In this paper we have first reviewed operations of a hydrogen gas reformer and provided its linearized mathematical model. Then, we have simplified an existing algorithm for a two-stage design of feedback controllers for linear continuous-time time-invariant systems. The proposed design significantly reduces the computational requirements and provides flexibility of designing different type of controllers for different dynamic parts of the system. Since the hydrogen gas reformer (also known as a fuel processing system) possesses slow and fast modes (state variables), the newly proposed design is further simplified and specialized for this class of systems. The obtained algorithm is efficiently applied with very high accuracy to the hydrogen gas reformer. As a matter of fact, the eigenvalue placement problem is solved for the reformer dynamics for both slow and fast modes. The design is so flexible that combined hybrid controllers (optimal, robust, set-point, eigenvalue assignment controllers or any other linear controller) can be designed independently for particular subsystems of the hydrogen gas reformer. The hybrid linear feedback controller design for the hydrogen gas reformer that optimizes its slow subsystem and assigns the desired eigenvalues to its fast subsystem is also presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the energy efficiency of the Proton Exchange Membrane Fuel Cell (PEMFC) systems based on the fueling rates is systematically investigated. The PEMFC system under dynamic load must be operated close to the Maximum Efficiency Point (MEP) to obtain the highest energy efficiency. This is a difficult task because the MEP is dependent on the PEMFC parameters and the control PEMFC variables, besides the load profile. Thus, the MEP must be tracked dynamically with a safe search speed and funded accurately during the stationary regimes. Consequently, a real-time control is recommended to be used. The Extremum Seeking (ES) control scheme is proposed here to evaluate the FC net power at the MEP under different fueling rates and load profiles. Some interesting conclusions are obtained based on the comparative method proposed using as reference a base control technique or a PEMFC stack: 1) the MEP is different based on the control of the fuel or air flow rate; 2) the energy efficiency increases if both fueling flow rates are controlled; 3) the energy efficiency is less sensitive to power losses if the MEP is tracked by the ES controller based on air flow rate; 4) the strategy of load following control considering the fuel flow rate as an input variable is recommended based on the observation that the MEP is more sensitive to this in comparison to the air flow rate; 5) the design of an appropriate MEP tracking controller should equally focus on safe operation and the increase of the performances such as the search speed and tracking accuracy under dynamic load. All these remarks are based on an extensive numerical simulation, which are highlighted in this paper by the main results shown. [Copyright &y& Elsevier]

Abstract: Nowadays, renewable power system solutions are widely investigated for residential applications. Grid-connected systems including energy storage elements are designed. Advanced research is actually focused on improving the reliability and energy density of renewable systems reducing the whole utility cost. Source and load modeling, power architectures and algorithms are only a few topics to be addressed. Designers have to carefully deal with each subtopic prior to design efficient renewable energy systems. In the literature, each topic is separately discussed and the lack of a unique reference guide is clear to power electronics designers. In this paper, each design step including source and load modeling, hybrid supply architectures and power algorithms, is carefully addressed. A review of existing solutions is presented. The correlation between each topic is deeply analyzed. Guidelines for system design are given. This paper can be referenced as a detailed review of renewable energy system design issues and solutions. [Copyright &y& Elsevier]

Abstract: In this paper, a stochastic model is proposed for planning the location and operation of Fuel Cell Power Plants (FCPPs) as Combined Heat, power, and Hydrogen (CHPH) units. Total cost, emissions of FCPPs and substation, and voltage deviation are the objective functions to be minimized. Location and operation of FCPPs as CHPH are considered in this paper while their investment cost is not taken into account. In the proposed model, indeterminacy refers to electrical and thermal loads forecasting, pressure of oxygen and hydrogen, and the nominal temperature of FCPPs. In this method, scenarios are produced using roulette wheel mechanism and probability distribution function of input random variables. Using this method, the probabilistic problem is considered to be distributed as some scenarios and consequently probabilistic problem is considered as combination of some deterministic problems. Considering the nature of objective functions, the problem of locating and operating FCPPs as CHPH is considered as a mixed integer nonlinear problem. A Self Adaptive Charged System Search (SACSS) algorithm is employed for determining the best Pareto optimal set. Furthermore, a set of non-dominated solutions is saved in repository during simulation procedure. A 69-bus distributed system is used for verifying the beneficiary proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents a dynamic model for Solid Oxide Fuel Cell (SOFC) stack using Local Linear Model Tree (LOLIMOT) algorithm which is useful for both dynamic and steady-state studies. Most of conventional SOFC models require a large number of parameters and factors, which are difficult to be determined or estimated. In this paper, a LOLIMOT-based model, which does not require the parameters of fuel cell, is proposed for each operation mode of SOFC. In these models, decision tree-based feature selection approach is exploited to select inputs of the LOLIMOT. The proposed models are trained in a short time and they have little errors. In order to illustrate the effectiveness of the proposed models, they are applied to a simulated model of a 5-kW SOFC stack which show satisfactory results for both steady-state and dynamic studies. Furthermore, the proposed model demonstrates convincing results for real-time simulation studies. [Copyright &y& Elsevier]

Hydrogen/helium heat exchanger is a kind of heat exchanger dedicated to SABRE (Synergetic Air-breathing and Rocket Engine). Its special working environment and operating condition have brought a great challenge to its design. Hydrogen/helium heat exchanger with micro-channel/plate heat exchanger as its configuration is taken as the research object in this paper. Mathematical model of the heat exchanger is set up based on logarithmic mean temperature difference method, and the parameters that affect the heat transfer performance of heat exchanger is analyzed. The results showed that, reducing the thickness of fin or plate can help reduce the pressure loss of working fluids, the length and weight of hydrogen/helium heat exchanger. Reducing the width of micro-channel can reduce the length of heat exchanger and increase the height of heat exchanger. Reducing the height of micro-channel can reduce the length and height of heat exchanger. As a whole, the pressure loss of working fluids shows a trend of decreasing at first and then rising with the height or width of micro-channel rising. The pressure loss of helium is much greater than that of hydrogen, which means the pressure loss of helium is more sensitive to geometric parameters of heat exchanger and should be well controlled. In order to gain the best performance of the heat exchanger, the geometric parameters of heat exchanger is designed by Artificial Fish Swarm Algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the achievement of dynamic response of high gain DC to DC converter in solar application using BAT Algorithm (BATA) optimization tuned Fuzzy Sliding Mode Controller. The dynamic and transient response of isolated full bridge DC to DC converter are improved by using BATA optimization tuned Fuzzy Sliding Mode Controller under starting and load step change conditions. The aim of the control is to regulate the output voltage beneath the load change. The model of the BATA tuned Fuzzy Sliding Mode Controller is implemented using sim power systems toolbox of MATLAB SIMULINK. Fuzzy Sliding Mode Controller is tuned by using BATA. Results are compared. Performance of the proposed dynamic novel control under step load change condition is investigated. Performance of proposed control is assessed through prototype hardware design. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) method has got rapid development and application because of its advantages in solving problems of small sample regression. In this paper, support vector machine (SVM) method was applied to the engine test data regression analysis. Quadratic polynomial method, neural network and SVM method are respectively used to establish a mathematical model between operating & control parameters and performance parameters based on calibration experiment data for a Hydrogen enriched compressed natural gas (HCNG) engine. Through the comparison of the three methods, SVM method has a higher fitting accuracy than other ways, showing certain superiority in nonlinear system regression. As SVM method is a generic methodology, it may be a new direction for engine calibration algorithm study. [ABSTRACT FROM AUTHOR]

In Polymer Electrolyte Membrane Fuel Cells (PEMFCs) with Dead-Ended Anode (DEA) configuration, precise control of hydrogen pressure in anode is beneficial for better performance and durability of fuel cell systems. In most studies, mechanical pressure regulator is implemented to modulate the hydrogen pressure. But when load changes or purge operation activates, hydrogen pressure downstream the regulator varies with hydrogen flow fluctuation. In this study, a hydrogen injection system is developed utilizing gaseous fuel injectors. Based on a fuel cell simulation test bench, the performance of the hydrogen injection system is evaluated. A physics-based nonlinear model of the simulation test bench is presented in this paper. Experimental results validate the model. The model is further linearized and integrated in a Robust Predictive Controller (RPC), a Model-Based Controller (MBC), and an H∞ controller, respectively, in order to compare the control effects on the hydrogen pressure, especially the mitigation of the pressure swing during the purge operation. A comparative study is carried out under different scenarios, i.e. target pressure step response, hydrogen consumption mass flow step response, and periodical pulse response during the purge operation. According to the results, MBC is chosen for injection control, since the system demonstrates preferable performance in comparison with other controllers. Finally, pressure fluctuation data of both simulation test bench with hydrogen injection and conventional mechanical pressure regulator is compared to elucidate the effectiveness. [ABSTRACT FROM AUTHOR]

Infrastructure for fuel-cell and other alternative-fuel vehicles is lacking not only in the paucity of fuel stations, but also in inadequate web-based support to help drivers complete their trips via the few stations that do exist. In this paper, we present an online mapping tool for finding the shortest feasible path in a road network given the vehicle's driving range and station locations. Users input their origin, destination, type of fuel, and driving range, and the algorithm generates a new reduced feasible network in which the vertices are the origin and destination nodes and reachable fuel stations and the edges represent feasible paths between them. Dijkstra's shortest path algorithm is applied to this reduced network to find the shortest feasible path. Efficiency is substantially improved by preprocessing and storing the shortest-path distances between stations. We present a web-mapping prototype ( www.afvrouting.com ) for hydrogen and compressed natural gas stations in the United States. Sample results illustrate the need for this kind of globally optimal solution method by showing that the optimal feasible path and refueling stops can vary tremendously as a result of user inputs for driving range, initial tank level, and one-way or round-trip. [ABSTRACT FROM AUTHOR]

Abstract: Polarization curves of proton exchange membrane fuel cells (PEMFCs) are affected by various parameters. The relative importance and effect of each parameter on the polarization curve is different. This paper studies estimation of parameters with the most influence on the electrochemical model. In order to evaluate the obtained results, the model accuracy is compared with that model in which all the parameters are estimated. Because PEMFCs parameter estimation is a complex optimization problem, a recently invented nature-inspired algorithm, bird mating optimizer (BMO), is proposed. For this aim, two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, are considered. The obtained results show that when the whole parameters are estimated, the accuracy of the model increases. Also, BMO algorithm yields better results than the other studied methods in terms of precision and robustness. [Copyright &y& Elsevier]

Abstract: This paper studies the air flow control for preventing the starvation and/or obtaining the maximum net power of a Polymer Electrolyte Membrane (PEM) fuel cell system using time delay control (TDC). Feedforward and feedback controls are utilized simultaneously to prevent air shortage during the transient response of the fuel cell operation. The TDC algorithm design is created with a low-order dynamic model, and its superior performances are proven using a real-time control experiment. The optimal air excess ratio is calculated experimentally given the variation of the external load, and the net power increase is discussed by comparison with the results obtained from fixed air excess ratio. The Ballard 1.2 kW PEM fuel cell system is used for the experiments as a test rig, and the LabView system is used for the real-time air flow control. The superiority of the TDC performance is proven by comparison with other control algorithms such as the proportional–integral control (PIC), feedforward control, and the original manufacturer's control. The proposed control algorithm can improve PEM fuel cell system performance by preventing air shortage and/or by obtaining higher performance. [Copyright &y& Elsevier]

Abstract: Nature inspired flow field designs for proton exchange membrane fuel cells (PEMFCs) are a relatively recent development in the technology evolution. These novel designs have the potential to show dramatic performance improvements by effective distribution of reactant gases without water flooding. Optimization of a flow field requires balancing gas distribution, water management, electron transport, pressure drop and manufacturing simplicity. Computational fluid dynamics (CFD) simulation studies are a useful tool for evaluating nature inspired flow field designs; however, the predictions should be used with caution until validated by an experimental study. Nature inspired flow field designs can be generated using formal mathematical algorithms or by making heuristic modifications to existing natural structures. This paper reviews the current state of nature inspired PEMFC flow field designs and discusses the challenges in evaluating these designs. [Copyright &y& Elsevier]

Abstract: Pressure difference inside the Polymer Electrolyte Membrane Fuel Cells (PEMFC) arises due to load variations, during which the pressure difference between anode and cathode rises. Practically, this problem can be avoided by equalizing anode and cathode pressures, to protect the fuel cell from permanent damage. This paper focuses on pressure regulation in the anode and cathode sides of the PEMFC. The control objective is achieved using second order sliding mode multi-input multi-output (MIMO) controller based on “Twisting algorithm”. Parametric uncertainty is formally presented and included in a nonlinear dynamic fuel cell model. The resultant nonlinear controller is robust and is proved to guarantee performance around any equilibrium point and under parametric uncertainty. Simulation results show that the proposed controller has a good transient response under load variations. [Copyright &y& Elsevier]

Abstract: For the sustainable production of clean H2, it is essential to optimize the capture and conversion of renewable energy. In this context, this paper presents a new technique to maximize the efficiency of a photovoltaic conversion system supplying an electrolyzer. Simple, efficient and low cost maximum power point tracking algorithms to optimize energy production are proposed for the three DC/DC electronic converter topologies typically used in photovoltaic and H2 applications. The method is based on energy balance concepts and exhibits certain analogies with maximum power tracking algorithms extensively used in wind turbines that do not require measurement of the primary energy source. The algorithm provides a reference to the current controller only requiring measurement of electrical variables and solar array temperature. The current controller is implemented using sliding mode strategies. Experimental and simulation results are provided showing the effectiveness and simplicity of the proposed method. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In recent years, accurate identification of voltage versus current (V–I) characteristics of proton exchange membrane fuel cell (PEMFC) has attracted significant attention in the literature. However, the main drawback in accurate modeling is the lack of information about the precise values of the model parameters. In this paper, in order to overcome this drawback a grouping-based global harmony search algorithm, named GGHS, is proposed for parameter identification issue. The proposed algorithm attempts to provide an efficient way in which a new harmony can be properly improvised. In order to study the capability of the proposed algorithm, the results obtained by GGHS are compared with those obtained by two versions of harmony search (HS) algorithms, three versions of particle swarm optimization (PSO) algorithms, as well as seeker optimization algorithm (SOA). Simulation results accentuate the superiority of the GGHS over the other methods. [Copyright &y& Elsevier]

Abstract: With ever growing concerns on energy crises and environmental issues, Proton Exchange Membrane Fuel Cell is favored in automotive applications because it is clean, efficient and low noise. A fuel cell hybrid powertrain is composed of a fuel cell system as the primary power source, a battery as the secondary power source and an electric motor. In order to improve the reliability, a distributed control system that can be preserved even under faulty cases is necessary. This paper presents an active fault tolerance control system (AFTCS) for a fuel cell/battery hybrid powertrain applied to a city bus. The AFTCS consists of a system for fault detection and diagnose and a reconfigurable controller. Algorithms to detect and isolated three kinds of important faults are introduced. The real-time applicable reconfigurable controller is exploited to recover the pre-fault system performance as much as possible. Experimental results show the effectiveness of the proposed system. [Copyright &y& Elsevier]

Abstract: A well-designed control system with a high utilization ratio of hydrogen and a fast filling speed are two critical objectives to ensure the reduction of cost and time required in the refueling process. In this paper, the popular three-stage refueling process is modeled with the aim to address both objectives. Using the real gas law of hydrogen, the utilization ratio of hydrogen filling is analyzed and the filling flowrate and time of each stage are evaluated. A multi-objective iterative optimization model is established and an optimization algorithm for the filling process is proposed to achieve both fast refueling and high utilization. Numerical results can be applied to the optimization of an actual hydrogen filling process. Besides, the tests show that an optimized control method can significantly improve the utilization ratio and allow refueling in a widely acceptable time. [Copyright &y& Elsevier]