Improving the load-following capability of a solid oxide fuel cell system through the use of time delay control

As a new energy technology with distributed generation prospects, the load-following capability of solid oxide fuel cell (SOFC) systems is one of the main obstacles for commercial operation. This paper introduces a time delay control with an observer (TDO) in the fuel supply system to enhance the load-following capability to realize rapid load-following without fuel starvation. A dynamic model reflecting a 5-kW SOFC system with components such as a cell stack, combustor, heat exchanger and gas supply is developed in MATLAB/Simulink to implement time delay control. TDO is improved to TDOF (time delay control with an observer and filter) to avoid the undesirable impact of external disturbances (such as fuel disturbances) on the stability of the SOFC system. The fuel supply system can be driven by the TDOF controller to closely follow the reference trajectory based on the past responses and the input variables of the system during load-following operation. Simulation results reveal that the SOFC system is able to meet the requirement of load-following due to the satisfactory dynamic performance when the TDOF controller is applied in the fuel supply system. The system is appropriate for complicated power environments.     

Experimental investigation on the dynamic performance of a hybrid PEM fuel cell/battery system for lightweight electric vehicle application

A hybrid system combining a 2 kW air-blowing proton exchange membrane fuel cell (PEMFC) stack and a lead–acid battery pack is developed for a lightweight cruising vehicle. The dynamic performances of this PEMFC system with and without the assistance of the batteries are systematically investigated in a series of laboratory and road tests. The stack current and voltage have timely dynamic responses to the load variations. Particularly, the current overshoot and voltage undershoot both happen during the step-up load tests. These phenomena are closely related to the charge double-layer effect and the mass transfer mechanisms such as the water and gas transport and distribution in the fuel cell. When the external load is beyond the range of the fuel cell system, the battery immediately participates in power output with a higher transient discharging current especially in the accelerating and climbing processes. The DC–DC converter exhibits a satisfying performance in adaptive modulation. It helps rectify the voltage output in a rigid manner and prevent the fuel cell system from being overloaded. The dynamic responses of other operating parameters such as the anodic operating pressure and the inlet and outlet temperatures are also investigated. The results show that such a hybrid system is able to dynamically satisfy the vehicular power demand.     

Fuzzy neural control of a hybrid fuel cell/battery distributed power generation system

An innovative control strategy is proposed of hybrid distributed generation (HDG) systems, including solid oxide fuel cell (SOFC) as the main energy source and battery energy storage as the auxiliary power source. The overall configuration of the HDG system is given, and dynamic models for the SOFC power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between power sources, the fuzzy switching controller has been developed. Then, a Lyapunov based-neuro fuzzy algorithm is presented for designing the controllers of fuel cell power plant, DC/DC and DC/AC converters; to regulate the input fuel flow and meet a desirable output power demand. Simulation results are given to show the overall system performance including load-following and power management of the system.     

ANFIS-MPPT control algorithm for a PEMFC system used in electric vehicle applications

Fuel cell has been considered as one of the optimistic renewable power technologies for the automotive applications. The output power of a fuel cell is immensely dependent on cell temperature and membrane water content. Hence, a maximum power point tracking controller is essentially required to extract the optimum power from the fuel cell stack. In this paper, an adaptive neuro-fuzzy inference system based maximum power point tracking controller is presented for 1.26 kW proton exchange membrane fuel cell system used in electric vehicle applications. In order to extract the optimum power, a high step-up boost converter is connected between the fuel cell and the BLDC motor. The duty cycle of the converter is controlled by using ANFIS reference model, so that the maximum power is delivered to the BLDC motor. The performance of the proposed controller is tested under normal operating conditions and also for sudden variations in the cell temperatures of the fuel cell. In addition to this, to analyze the effectiveness and tracking behaviour of the proposed controller, the results were compared with those obtained using the fuzzy logic controller. Compared to the fuzzy logic controller, the proposed ANFIS controller has increased the average DC link power by 1.95% and the average time taken to reach the maximum power point is reduced by 17.74%.     

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

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

Optimization of fuel cell system operating conditions for fuel cell vehicles

Proton exchange membrane fuel cell (PEMFC) technology for use in fuel cell vehicles and other applications has been intensively developed in recent decades. Besides the fuel cell stack, air and fuel control and thermal and water management are major challenges in the development of the fuel cell for vehicle applications. The air supply system can have a major impact on overall system efficiency. In this paper a fuel cell system model for optimizing system operating conditions was developed which includes the transient dynamics of the air system with varying back pressure. Compared to the conventional fixed back pressure operation, the optimal operation discussed in this paper can achieve higher system efficiency over the full load range. Finally, the model is applied as part of a dynamic forward-looking vehicle model of a load-following direct hydrogen fuel cell vehicle to explore the energy economy optimization potential of fuel cell vehicles.     

MPPT controller for an interleaved boost dc–dc converter used in fuel cell electric vehicles

Fuel cell electric vehicle (FCEV) has recently attracted increasing research interest. This paper investigates the performances of MPPT-FC generators supplying electric vehicle power train through an interleaved boost DC/DC converter (IBC). The accent is made on forcing the FC generator to operate at its maximum power point by using perturb and observe (P&O) algorithm integrated to the IBC control. However, the MPPT-FC control creates rapid changes in the power output from the fuel cell, which cause serious life shortening, severe cell degradation, and decrease the system efficiency. To overcome these shortcomings, the control of air generation system was designed to improve the power quality and to prevent fuel starvation phenomenon during rapid power transitions. The work involves the modeling and the simulation of the fuel cell power train in the vehicular application using the experimental data obtained in previous works. The experimental part of the proposed FCEV is based on a low-cost, low-power consumption microcontroller, which controls the IBC and performs the MPPT-FC operation. A microcontroller is used to measure the FC output power and to change the duty ratio of the IBC control signals.     

Time delay control for fuel cells with bidirectional DC/DC converter and battery

Transient behavior is a key property in the vehicular application of proton exchange membrane (PEM) fuel cells. A better control technology is constructed to increase the transient performance of PEM fuel cells. A steady-state isothermal analytical fuel cell model is constructed to analyze mass transfer and water transport in the membrane. To prevent the starvation of air in the PEM fuel cell, time delay control is used to regulate the optimum stoichiometric amount of oxygen, although dynamic fluctuations exist in the PEM fuel cell power. A bidirectional DC/DC converter connects the battery to the DC link to manage the power distribution between the fuel cell and the battery. Dynamic evolution control (DEC) allows for adequate pulse-width modulation (PWM) control of the bidirectional DC/DC converter with fast response. Matlab/Simulink/Simpower simulation is performed to validate the proposed methodology, increase the transient performance of the PEM fuel cell system and satisfy the requirement of energy management.     

Modeling and analysis of an FC/UC hybrid vehicular power system using a wavelet-fuzzy logic based load sharing and control algorithm

Fuel cell (FC) systems are potentially promising candidates as alternative energy sources for use in vehicular applications. The natural advantages of hybrid power sources may be effectively utilized to improve the efficiency and dynamic response of a vehicular system. Fuel cell (FC) and ultra-capacitor (UC) based hybrid power systems appear to be very promising for satisfying high energy and high power requirements for vehicular applications. In this paper, a FC/UC hybrid vehicular power system using a wavelet based load sharing and fuzzy logic based control algorithm is proposed. While wavelet transforms are suitable for analyzing and evaluating the dynamic load demand profile of a hybrid electric vehicle (HEV), the use of fuzzy logic controller is appropriate for the hybrid system control. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB^®, Simulink^® and SimPowerSystems^® environments.     

Development of hybrid photovoltaic-fuel cell system for stand-alone application

In this paper we present firstly the different hybrid systems with fuel cell. Then, the study is given with a hybrid fuel cell–photovoltaic generator. The role of this system is the production of electricity without interruption in remote areas. It consists generally of a photovoltaic generator (PV), an alkaline water electrolyzer, a storage gas tank, a proton exchange membrane fuel cell (PEMFC), and power conditioning units (PCU) to manage the system operation of the hybrid system. Different topologies are competing for an optimal design of the hybrid photovoltaic–electrolyzer–fuel cell system. The studied system is proposed. PV subsystem work as a primary source, converting solar irradiation into electricity that is given to a DC bus. The second working subsystem is the electrolyzer which produces hydrogen and oxygen from water as a result of an electrochemical process. When there is an excess of solar generation available, the electrolyzer is turned on to begin producing hydrogen which is sent to a storage tank. The produced hydrogen is used by the third working subsystem (the fuel cell stack) which produces electrical energy to supply the DC bus. The modelisation of the global system is given and the obtained results are presented and discussed.     

A novel self-tuning type-2 fuzzy maximum power point tracking technique for efficiency enhancement of fuel cell based battery chargers

Given the uncertainties associated with proton-exchange membrane fuel cell systems and relatively low efficiency of the fuel cell stacks for low-power applications, designing a high-efficiency maximum power point tracking (MPPT) controller for the fuel cell electric vehicles is an important and also technically challenging issue. For this purpose, in this article, aiming to develop a high-efficiency and low cost battery charger, a novel self-tuning type-2 fuzzy MPPT controller is presented. The main task of the controller is to provide the better performance and regulate the switching duty cycle of the used power converter under the system's uncertainty conditions in order to dynamically extract the maximum power from the fuel cell system and maintain the battery at its highest possible state of charge while protecting it from overcharging. For the sake of computational efficiency, an improved invasive weed optimization algorithm, called elitist invasive weed optimization (EIWO), is also presented to tune the type-2 fuzzy set parameters, whose improvement is demanding due to the limited human experience and knowledge. All data processing and simulations are conducted in the MATLAB software. Finally, the performance of the proposed MPPT controller is examined through using experimental tests with a prototype device.     

Analysis and comparative study of pulsating current of fuel cells by inverter load with different power converter topologies

Fuel cells are being increasingly used for stand alone and grid connected systems in wide range of applications due to their high efficiency and low emissions. Because of unregulated nature of fuel cell voltage a power conditioning unit, consisting of DC-DC converter and an inverter, is invariably used as an interface between the fuel cell and the load in a typical fuel cell system for ac applications. Major issues with the use of fuel cells for ac applications are the low frequency pulsating current propagation on to the fuel cell side and dynamic response to various loads during transient conditions. Low frequency pulsating currents are reported to affect reactant utilization, degrade the performance and life of fuel cells. These current ripples can be reduced by filters with passive elements having bulky inductor and capacitor in the dc-link between the fuel cell and the inverter but, it will add to the weight and cost. DC-DC converters of different configurations are being used in the power conditioning unit of fuel cell systems. These converters are operated at high frequencies and the filtering units of these converters have minimal effect on low frequency ripple. But, it is observed that different configurations of power conditioner with same filtering components perform differently for the low frequency current ripple of the inverter load by mitigating the power mismatch between load and source at the DC link. This paper investigates and compares the low frequency current ripple mitigation by cascaded converters with conventional push-pull and also with series connected converters in the power conditioning stage of fuel cell system for ac applications. Parameters such as peak switching currents, the percentage of peak to DC level of low frequency current ripple are analyzed using these conversion topologies in power conditioning unit. The analytical and simulation results related to the study are presented. Key results are verified with experimental work.     

A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system

Due to increasing concerns on environmental pollution and depleting fossil fuels, fuel cell (FC) vehicle technology has received considerable attention as an alternative to the conventional vehicular systems. However, a FC system combined with an energy storage system (ESS) can display a preferable performance for vehicle propulsion. As the additional ESS can fulfill the transient power demand fluctuations, the fuel cell can be downsized to fit the average power demand without facing peak loads. Besides, braking energy can be recovered by the ESS. This study focuses on a vehicular system powered by a fuel cell and equipped with two secondary energy storage devices: battery and ultra-capacitor (UC). However, an advanced energy management strategy is quite necessary to split the power demand of a vehicle in a suitable way for the on-board power sources in order to maximize the performance while promoting the fuel economy and endurance of hybrid system components. In this study, a wavelet and fuzzy logic based energy management strategy is proposed for the developed hybrid vehicular system. Wavelet transform has great capability for analyzing signals consisting of instantaneous changes like a hybrid electric vehicle (HEV) power demand. Besides, fuzzy logic has a quite suitable structure for the control of hybrid systems. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB^®, Simulink^® and SimPowerSystems^® environments.     

Modeling and control of a portable proton exchange membrane fuel cell-battery power system

A portable proton exchange membrane (PEM) fuel cell-battery power system that uses hydrogen as fuel has a higher power density than conventional batteries, and it is one of the most promising environmentally friendly small-scale alternative energy sources. A general methodology of modeling, control and building of a proton exchange membrane fuel cell-battery system is introduced in this study. A set of fuel cell-battery power system models have been developed and implemented in the Simulink environment. This model is able to address the dynamic behaviors of a PEM fuel cell stack, a boost DC/DC converter and a lithium-ion battery. To control the power system and thus achieve proper performance, a set of system controllers, including a PEM fuel cell reactant supply controller and a power management controller, were developed based on the system model. A physical 100 W PEM fuel cell-battery power system with an embedded micro controller was built to validate the simulation results and to demonstrate this new environmentally friendly power source. Experimental results demonstrated that the 100 W PEM fuel cell-battery power system operated automatically with the varying load conditions as a stable power supply. The experimental results followed the basic trend of the simulation results.     

A prototype of a fuel cell PEM emulator based on a buck converter

After a brief introduction about fuel cell systems, and their modelling, this paper proposes a possible solution to emulate a proton exchange membrane fuel cell (PEM-FC) system by using a DC–DC buck converter. The fuel cell system, including all its auxiliaries and related control systems, is emulated by a buck converter realized experimentally and controlled in the DSPACE environment. The realization of the buck converter allows the behaviour of any fuel cells to be easily emulated since only the modification of the control law of the switch is necessary. The proposed emulator can be applied easily to other fuel cell systems if the polarization curve has the same current rate and maximum power. In this way it is possible to utilize the converter and perform the necessary tests to optimize a fuel cell system by avoiding the waste of hydrogen and the purchase of cells as well as any cell damage. With regard to current other types of emulators, the one presented here has the following characteristics: (1) all the auxiliaries of the system have been considered, each including its own control system, as in a real FCS, (2) the converter is a classical buck converter with a free-wheeling diode and is designed to have a high bandwidth and to be practically always in conduction mode (discontinuous mode appears only at very low currents) (3) the voltage control is made by a space-state controller, able to fix properly the closed loop poles of the system, thus guaranteeing the desired bandwidth of the control system and (4) it can be used in laboratory as a stand-alone low-cost system for design and experimental purposes.     

Dynamic modeling of electrical characteristics of solid oxide fuel cells using fractional derivatives

An efficient controller is greatly important for the quick load-following response of solid oxide fuel cell (SOFC) power systems, which is vital for the fuel utilization and the life of the stack. To design such a controller, an accurate dynamic model of SOFC electrical characteristics is critical. Here an integer order dynamic model is established by a transient equivalent circuit, and then a fractional order dynamic model is done in the perspective of the fractional derivatives theory. The parameters of the dynamic models then are optimized via genetic algorithms according to electrochemical impedance spectroscopy (EIS) experimental data. Finally, the dynamic response experiments from the models are studied. The results show that the fractional order dynamic model has higher accuracy for representing the dynamics of the SOFC electrical characteristics, which lays a solid foundation for the controller based on the accurate model.     

Fast identification of power change rate of PEM fuel cell based on data dimensionality reduction approach

The efficiency, reliability and lifespan of the fuel cell are strongly affected by the dynamic load variation of its output, which renders it desperate to investigate the influence of the power change rate on the fuel cell. In this study, three data dimensionality reduction algorithms are applied to identify the power change rate of the fuel cell in a less time-consuming way. To achieve that, the cell voltages of the stack as high-dimensional dataset is instantly projected onto the one-dimensional eigenvector space by principal component analysis (PCA), Fisher discriminant analysis (FDA) and locally linear embedding (LLE), respectively. The eigenvalue of one-dimensional eigenvector has the potential for instantly identifying the power change rate of the fuel cell and can be used as a feedback parameter in the control, which can improve the dynamic response, reliability, and lifespan of the fuel cell stack. According to the performance indicators of the algorithms including monotonicity, linearity and program execution time, the result shows that the PCA algorithm is the best-matched method for the real-time control of the fuel cell system. In the end, this study discussed some potential applications of this method in the fuel cell system, be it to be used alone or in a vehicular fuel cell hybrid system.     

Development of a power management unit for small portable direct borohydride fuel cell–NiMH battery hybrid system

In this study, a small portable fuel cell/battery hybrid system has been developed. The system consists of a single portable direct borohydride/peroxide fuel cell (DBPFC), NiMH battery and power management unit (PMU). The battery has been used as a primary power source and has been discharged at constant load. When its state of charge is reduced, the DBPFC charges the battery and powers the load simultaneously. A DC–DC Boost converter has been used as a PMU. The DBPFC has provided the total power of 0.21 Wh into the system during the charge. During this experimental study fuel (NaBH₄) efficiency of 37% has been achieved in the hybrid system, while the system efficiency has been calculated as 34.5%.     

Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy

An energy management strategy (EMS) is one of the most important issues for the efficiency and performance of a hybrid vehicular system. This paper deals with a neural network and wavelet transform based EMS proposed for a fuel cell/ultra-capacitor hybrid vehicular system. The proposed method combines the capability of wavelet transform to treat transient signals with the ability of auto-associative neural network supervisory mode control. The main originality of the paper is related with the application of neural network instead of another intelligent control method, fuzzy logic, which is presented in the recent publication of the authors, and the combination of neural network-wavelet transform approaches. Then, the effectiveness comparison of both methods considering one of the most important points in a vehicular system, fuel consumption (or hydrogen consumption), is realized. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB^®, Simulink^® and SimPowerSystems^® environments.     

Maximum power cold start mode of proton exchange membrane fuel cell

Successful and fast cold start is important for proton exchange membrane (PEM) fuel cell in vehicular applications in addition to the desired maximum power in any case. In this study, the maximum power cold start mode is investigated in details and compared with other cold start modes based on a multiphase stack model. It is found that for the maximum power cold start mode, the current density is generally kept at high levels, and the performance improvement caused by the membrane hydration and temperature increment may not be observable. Therefore, before the melting point, the performance drops continuously. The maximum power cold start mode could better balance the heat generation and ice formation, leading to improved cold start survivability than that in the constant voltage and constant current modes, with a fast start-up generally guaranteed. Once the survivability can be ensured, the initial water content needs to be higher for fast cold start, suggesting that over purging should be avoided. The maximum power mode is suggested to be optimal for PEM fuel cell cold start based on the modeling results.