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In high-power applications, the use of fuel cell system cluster (FCSC) is increasingly valued. However, most of the existing strategies emphasis in optimizing a certain control objective (e.g., efficiency or consumption), but do not fully consider the impact of other factors such as stack degradation. To address this research gap, this article proposes a power allocation method related to the system economy, which considers multiple factors that affect the operating cost of FCSC. The efficiency and hydrogen consumption are analyzed, and two high-order polynomials are used to represent these two curves. Besides, considering that the fuel cell (FC) output characteristics are variable, an online identification algorithm is adopted to update the system parameters in real-time. Moreover, in the formulated cost function, stack degradation is also considered. What is more, to ensure that the FCs can operate in the high efficiency range as much as possible, we also add constraints to limit the FCs output power. The effectiveness of the proposed method has been verified on a hardware-in-the-loop simulation platform. Compared with the equal distribution strategy, the proposed method can effectively ameliorate the economy of the system. Furthermore, the proposed method has been experimentally validated and its real-time operation ability has been highlighted on an actual FCSC. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical power allocation method (HPAM) for dual-stack fuel cell (FC) hybrid locomotive powertrain. In a high-power FC application such as rail transportation, the use of multi-stack system is able to provide the primary and stable demanded power. The battery is adopted as the energy storage source (ESS) to fulfill the transient power demand fluctuations. Besides, considering that the performance of the stack will change with the operating conditions, this study also searches an online identification method based on forgetting factor recursive least square (FFRLS) algorithm to update the system parameters of the dual-PEMFC system in real time. In addition, sequential quadratic programming (SQP) scheme is employed in this study to search for the optimal solution of nonlinear constraint problems. Furthermore, it is also to ensure that the proton exchange membrane fuel cell (PEMFC) systems can operate as much as possible in the high efficiency range, and the final battery state of charge (SOC) can be close to the initial SOC. Moreover, this study also adopts the equivalent consumption minimization strategy (ECMS) as benchmark to foreground the superiority of the presented method. A reduced-scale hybrid locomotive powertrain test bench is developed to demonstrate the practicability of the developed HPAM. It can be seen from the experimental results that, the proposed HPAM can optimize PEMFC systems efficiency and performances, and reduce system fuel consumption. [ABSTRACT FROM AUTHOR]

The device-level description of power switches is an emerging topic that enhances the real-time simulation (RTS) accuracy of power electronic converters. However, device-level RTS (DLRTS) is challenging due to contradictions between the additional computing amounts introduced by nonlinear switch models and the nanosecond-level simulation time step required by fast switching transients. Therefore, much research has sought the tradeoff between the accuracy and speed in DLRTSs. In this article, we review state-of-the-art DLRTS technologies, in particular for device-level switch models and efficient network solvers. Moreover, we summarize different applications of DLRTSs and crosswise compare their performances. We also give some outlooks on future research. This review not only provides a comprehensive overview of DLRTSs for general audiences but also provides a technical reference for practitioners. See "A List of Acronyms Used in This Article" for relevant acronyms and their definitions. [ABSTRACT FROM AUTHOR]

The purpose of this work is to design a power allocation method (PAM) for multistack fuel cell system (MFCS). Due to the limitations of technology, large-scale application of proton exchange membrane fuel cell (PEMFC) in the field of rail transportation has not been implemented. Therefore, this study presents an MFCS to promote the use of PEMFCs in the high-power energy market. During the operation of the MFCS, since the output power of the FC varies dynamically with different operation conditions, the performance of each stack is usually inconsistent. A method for evaluating the degree of performance degradation (DOPD) of stacks is presented in this study. In addition, a fuel cell (FC) semiempirical model is also adopted to simulate the effects of aging on the stacks’ performance. In order to maintain the uniform performance of stacks and enhance the lifespan of the MFCS, a PAM that considers the DOPD of each stack is presented. Besides, in order to facilitate system expansion, this article proposes a virtual droop control method to realize power splitting among the stacks. Also, a voltage control strategy is presented to compensate for the bus voltage drop caused by the droop control. The effectiveness and practicability of the presented PAM and MFCS are verified on the hardware-in-the-loop test bench constructed by RT-LAB. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under various fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications. [ABSTRACT FROM AUTHOR]

Real-time simulation is important for the fuel cell online diagnostics and hardware-in-the-loop tests before industrial applications. However, it is hard to implement real-time multidimensional, multiphysical fuel cell models due to the model numerical stiffness issues. In this paper, the numerical stiffness of a tubular solid oxide fuel cell real-time model is first analyzed to identify the perturbation ranges related to the fuel cell electrochemical, fluidic, and thermal domains. Some of the commonly used ordinary differential equation (ODE) solvers are then tested for the real-time simulation purpose. At last, a novel two-stage third-order parallel stiff ODE solver is proposed to improve the stability and reduce the multidimensional real-time fuel cell model execution time. To verify the proposed model and the ODE solver, real-time simulation experiments are carried out in a common embedded real-time platform. The experimental results show that the execution speed satisfies the requirement of real-time simulation. The solver stability under strong stiffness and the high model accuracy are also validated. The proposed real-time fuel cell model and the stiff ODE solver can also help to design the online diagnostic control method. [ABSTRACT FROM AUTHOR]

This paper presents a multiphysical model for a microtubular solid-oxide fuel cell (SOFC) using syngas as fuel. The proposed control-oriented syngas fuel cell model includes both hydrogen and carbon monoxide co-oxidation phenomena and is demonstrated through the electrochemical, fluidic, and thermal dynamic characteristics of the fuel cell. The developed model is validated experimentally under different operating conditions regarding different reaction temperatures, species partial pressures, and entire working range of current densities. As an important part of the model, the co-oxidation phenomenon of hydrogen and carbon monoxide is discussed in details. The developed model can be used in embedded applications such as real-time simulation, which can help to design and test the control and online diagnostic strategy for fuel cell power generation system in the industrial applications. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient two-dimensional (2-D) steady-state model for fuel cell real-time control implementation. Both the fluid and electrochemical physical domains are considered in the proposed real-time model. The fuel cell under-rib convection is fully described by considering the geometry of serpentine channel. In addition, in order to solve the implicit activation voltage loss and further explore the computational performance, three numerical root-searching algorithms: bisection, secant, and Newton–Raphson methods are applied to the proposed implicit iterative solver and compared. The preferred secant method has been proven to effectively improve both the efficiency and robustness performance of the proposed real-time fuel cell model. Moreover, a computational fluid dynamic based COMSOL fuel cell model is used to validate the calculation accuracy. Furthermore, the practical feasibility of the presented real-time model has been verified using an RT-LAB simulator platform from Opal-RT. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator. [ABSTRACT FROM PUBLISHER]

This paper presents a multiphysical modeling of a two-dimensional (2-D) reversible tubular solid oxide cell. The developed model can represent both a solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOFC) operations. By taking into account of the electrochemical, fluidic, and thermal physical phenomena, the presented model can accurately describe the multiphysical effects inside a cell for both fuel cell and electrolysis cell operation under entire working range of cell current and temperature. In addition, an iterative solver is proposed which is used to solve the 2-D distribution of physical quantities along the tubular cell. The proposed model is suitable for embedded applications, such as real-time simulation or online diagnostic control. The reversible solid oxide cell model is then validated experimentally in both SOEC and SOFC configurations under different species partial pressures, operating temperatures, and current densities conditions. [ABSTRACT FROM AUTHOR]

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler–Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method. [ABSTRACT FROM AUTHOR]

A current-fed full-bridge bidirectional voltage doubler with secondary-assisted device voltage clamping and zero-current commutation (ZCC) is proposed for fuel cell vehicles (FCVs). The proposed topology is suitable for interfacing energy storage and/or fuel cell stack with a dc bus in FCVs. A voltage doubler on the secondary side is selected to enhance the gain by 2 $\times$, reduce transformer size, and efficiently reduce the low-frequency dc current harmonics. Parasitics-based zero-voltage switching (ZVS), secondary-based zero-current switching of low-voltage-side devices, and ZVS of secondary devices are achieved. The proposed secondary modulation technique naturally clamps the voltage across the primary-side devices with ZCC, thus eliminating the necessity for traditional active-clamp or passive snubbers. Switching losses are reduced, owing to the soft-switching of all semiconductor devices. Steady-state analysis and design are studied and explained. The experimental results of a 1-kW proof-of-concept hardware prototype are shown to demonstrate the performance and confirm the proposed claims. [ABSTRACT FROM PUBLISHER]

Dynamic variable coupling analysis is an important method to properly design a control structure for complex multivariable and multi-physical dynamic systems, such as fuel cells. The fuel cell is an electrochemical energy conversion device which includes different inter-coupled dynamic phenomena in electrical, fluidic, and thermal domains. In order to achieve optimized fuel cell performance, different operation variables, such as fuel cell temperature, inlet air flow rate, hydrogen pressure, and membrane water content, need to be properly controlled. In this paper, variable coupling analyses of fuel cell dynamic behaviors are presented and discussed based on a proton exchange membrane fuel cell (PEMFC) dynamic model, which considers in particular the transient phenomena in both fluidic and thermal domain. The analyses of dynamic phenomena step responses are conducted using the relative gain array for various control input variables. Quantitative analyses of coupling effects in different physical domains are shown and discussed. The analysis results can be used to optimize the controller design for fuel cell system. [ABSTRACT FROM PUBLISHER]

Environmental issues, especially global warming due to the greenhouse effect, have become more and more critical in recent decades. As one potential candidate among different alternative “green energy” solutions for sustainable development, the proton exchange membrane fuel cell (PEMFC) has received extensive research attention for many years for energy and transportation applications. However, the relatively short lifespan of PEMFCs operating under non-steady-state conditions (for vehicles, for example) impedes its massive use. The accurate prediction of their aging mechanisms can thus help to design proper maintenance patterns of PEMFCs by providing foreseeable performance degradation information. In addition, the prediction could also help to avoid or mitigate the unwanted degradation of PEMFC systems during operation. In this paper, an advanced self-adaptive relevance vector machine (RVM) has been developed and demonstrated to predict the performance degradation of PEMFCs. In order to prove the versatility of proposed RVM method, the predictive results are experimentally validated using two different PEMFC stacks aging data under different operating patterns. Furthermore, the obtained results are compared with results from both classic support vector machine and original RVM methods in order to highlight the effectiveness of the proposed self-adaptive RVM method with a modified design matrix. A comparison between single-step-ahead and multiple-step-ahead predictions of the proposed method is also given and discussed. The results show that the proposed novel RVM method is powerful and effective for PEMFC degradation prediction. [ABSTRACT FROM PUBLISHER]

Proton exchange membrane fuel cells (PEMFCs) are considered as a potential candidate in the green-energy applications in the near future. Comparing with other energy options, the PEMFCs need only hydrogen and air during operation. Meanwhile, as a by-product during operation, water is produced. This energy-conversion process is 100% eco-friendly and completely unharmful to the environment. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition, which could cause the degradation of output performance over time during operation. Thus, the prediction of the performance degradation is critical to the PEMFC system. In this work, a novel PEMFC performance-forecasting model based on a modified relevance vector machine (RVM) has been proposed, followed by a comparison with the approach of classic support vector machine (SVM). First, the theoretical formulation of RVM is briefly introduced, then the implementation steps of RVM using the experimental aging data sets of PEMFC stack output voltage are presented. By considering the specific feature of aging data-prediction problem, an innovative modified RVM formulation is proposed. The results of proposed modified RVM method are analyzed and compared to the results of SVM. The results have demonstrated that the modified RVM can achieve better performance of prediction than SVM, especially in the cases with relatively small training data sets. This novel method based on modified RVM approach has been demonstrated to show its effectiveness on forecasting the performance degradation of PEMFCs. [ABSTRACT FROM AUTHOR]

A non-isolated impulse commutated current-fed voltage doubler based non-isolated inverter for a solar photovoltaic, battery or fuel cell application is proposed in this paper. Impulse commutation enables device voltage clamping with zero current commutation of the semiconductor devices. It eliminates the traditional problem of turn-off voltage spike across the devices in current-fed converters. Unlike resonant converters, resonance pulse appears for a very short interval leading to zero current turn-off of devices. It therefore, limits peak and circulating currents through the components. Voltage doubler is selected to achieve 2 $\times$ gain. Variable frequency modulation ensures output voltage regulation with input and load variations. Steady-state operation and analysis of front-end converter is explained. A 500 W prototype has been designed and developed to demonstrate the performance and verify the proposed operation, analysis, and claims. [ABSTRACT FROM AUTHOR]