Autothermal reforming of gasoline for fuel cell applications: Controller design and analysis

In this work, we address the control system options available to an autothermal reforming (ATR) reactor. The targeted application is within an on-board fuel processor for a hydrogen-fed low-temperature fuel cell. The feedback controller employs air feed rate as the manipulated variable and a measurement of catalyst temperature as the control variable. Disturbances include significant fluctuations in the measured temperature as well as large throughput changes, owning to the on-board application. Our investigation includes an analysis of a simple feedback configuration as well as feed-forward control structure. It is concluded that the feedback only method is insufficient for the unique challenges associated with on-board operation, which include fast start-up and quick load changes. While the feed-forward configuration improves performance, we found a fair amount of sensitivity with respect to model mismatch. The general conclusion is that some form of advanced control will be needed to meet the stringent performance requirements of the on-board fuel processor application.     

Anti-disturbance control of oxygen feeding for vehicular fuel cell driven by feedback linearization model predictive control-based cascade scheme

The accurate control of automotive fuel cell oxygen excess ratio (OER) is necessary to improve system efficiency and service life. To this end, an anti-disturbance control driven by a feedback linearization model predictive control (MPC)-based cascade scheme is proposed. It considers strong nonlinear coupling and disturbance injection of fuel cell oxygen supply. A six-order nonlinear fuel cell oxygen feeding model is presented. It is further formulated using an extended state observer to rapidly reconstruct the OER, to overcome the slow response and interference errors of sensor measurements. In the proposed cascade control, the outer loop is the anti-disturbance control which is used to realize the optimized OER tracking and the inner loop via the feedback linearization to linearize the oxygen feeding behaviors conducts MPC to regulate the air compressor output mass flow. The feedback linearization demonstrates a robust tracking performance of nonlinear outputs, and the integral absolute error of anti-disturbance control is 0.3021 lower than that of PI control under a custom test condition. Finally, the numerical validation on a hybrid driving cycle indicates that the proposed cascade control can regulate the fuel cell OER with an average absolute error of 0.02313 in the high air compressor operation efficiency zone.     

Nonlinear model predictive control based on the moving horizon state estimation for the solid oxide fuel cell

As a nonlinear power generation device, the solid oxide fuel cell (SOFC) often operates under small window of operating conditions due to the constraints stemming from the environmental and safety considerations. The nonlinear model predictive control (NMPC) appears to be well suited control algorithm for this application. NMPC is a closed-loop feedback control scheme that predicts the open-loop optimal input based on the measurements and the setting trajectory. This work aims to develop a closed-loop feedback control strategy based on the NMPC controller for a planar SOFC. The current density, fuel and air molar flow rates are chosen as manipulated variables to control the output power, fuel utilization and temperature. The mole fraction and temperature of the exit gases are set as state variables, which can be estimated from the moving horizon estimation (MHE) method. The validation here is referred to robustness and stability of the controller, a typical case study has been conducted with the power output changes under constant fuel utilization and temperature. Simulation results show that the noise of the output is successfully filtered by the MHE. The NMPC controller works satisfactorily following the setting output trajectory.     

Improving dynamic performance of proton-exchange membrane fuel cell system using time delay control

Transient behaviour is a key parameter for the vehicular application of proton-exchange membrane (PEM) fuel cell. The goal of this presentation is to construct better control technology to increase the dynamic performance of a PEM fuel cell. The PEM fuel cell model comprises a compressor, an injection pump, a humidifier, a cooler, inlet and outlet manifolds, and a membrane-electrode assembly. The model includes the dynamic states of current, voltage, relative humidity, stoichiometry of air and hydrogen, cathode and anode pressures, cathode and anode mass flow rates, and power. Anode recirculation is also included with the injection pump, as well as anode purging, for preventing anode flooding. A steady-state, isothermal analytical fuel cell model is constructed to analyze the mass transfer and water transportation in the membrane. In order to prevent the starvation of air and flooding in a PEM fuel cell, time delay control is suggested to regulate the optimum stoichiometry of oxygen and hydrogen, even when there are dynamical fluctuations of the required PEM fuel cell power. To prove the dynamical performance improvement of the present method, feed-forward control and Linear Quadratic Gaussian (LQG) control with a state estimator are compared. Matlab/Simulink simulation is performed to validate the proposed methodology to increase the dynamic performance of a PEM fuel cell system.     

Coordination control strategy for the air management of heavy vehicle fuel cell engine

Air supply system is an important subsystem in the fuel cell engine with strongly non-linear and coupling interactions. There are strong coupling problems between air flow and pressure in the air supply system, such as the air compressor and electronic throttle opening. This paper introduces a novel coordination control strategy for the air supply system of high power fuel cell engine in heavy truck. It consists of feed-forward and internal model decoupling control (IMC) with tracking an optimized working line of centrifugal air compressor. The strategy can maintain good control effect for model matching and model mismatching with robustness. The working efficiency of the centrifugal air compressor could be significantly increased and avoid the phenomenon of surge by the coordination control strategy. At the same time, the output current of fuel cell engine can meet the load requirement which has the short response time and good follow effect.     

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.     

A nonlinear control strategy for fuel delivery in PEM fuel cells considering nitrogen permeation

Hydrogen energy shows its great potential to be one of the future sustainable energies with abundant storage and high energy content. Proton exchange membrane (PEM) fuel cells, as a hydrogen energy conversation plant with high efficiency, becomes a hot topic of many researches. This paper proposes a multi-input-multi-output (MIMO) nonlinear control strategy for fuel delivery in PEM fuel cell systems. Specifically, a control oriented dynamic model is developed for the fuel delivery system (FDS) with anode recirculation and anode bleeding. Based on the model, a MIMO nonlinear state feedback controller is proposed to maintain adequate hydrogen supply and suitable anode hydrogen concentration. Moreover, an optimized output feedback controller is proposed to improve the state feedback controller, where the unknown hydrogen partial pressures utilized are estimated by developed observers. Lyapunov based stability analysis is carried out to analyze the proposed output feedback controller and the observers. Simulation results show the effectiveness of the proposed controller under various current demands.     

Feedback control of solid oxide fuel cell spatial temperature variation

A high performance feedback controller has been developed to minimize SOFC spatial temperature variation following significant load perturbations. For thermal management, spatial temperature variation along SOFC cannot be avoided. However, results indicate that feedback control can be used to manipulate the fuel cell air flow and inlet fuel cell air temperature to maintain a nearly constant SOFC electrode electrolyte assembly temperature profile. For example temperature variations of less than 5 K are obtained for load perturbations of ±25% from nominal. These results are obtained using a centralized control strategy to regulate a distributed temperature profile and manage actuator interactions. The controller is based on H-infinity synthesis using a physical based dynamic model of a single co-flow SOFC repeat cell. The model of the fuel cell spatial temperature response needed for control synthesis was linearized and reduced from nonlinear model of the fuel cell assembly. A single 11 state feedback linear system tested in the full nonlinear model was found to be effective and stable over a wide fuel cell operating envelope (0.82-0.6 V). Overall, simulation of the advanced controller resulted in small and smooth monotonic temperature response to rapid and large load perturbations. This indicates that future SOFC systems can be designed and controlled to have superb load following characteristic with less than previously expected thermal stresses.     

Low power proton exchange membrane fuel cell system identification and adaptive control

This paper proposes a systematic method of system identification and control of a proton exchange membrane (PEM) fuel cell. This fuel cell can be used for low-power communication devices involving complex electrochemical reactions of nonlinear and time-varying dynamic properties. From a system point of view, the dynamic model of PEM fuel cell is reduced to a configuration of two inputs, hydrogen and air flow rates, and two outputs, cell voltage and current. The corresponding transfer functions describe linearized subsystem dynamics with finite orders and time-varying parameters, which are expressed as discrete-time auto-regression moving-average with auxiliary input models for system identification by the recursive least square algorithm. In the experiments, a pseudo-random binary sequence of hydrogen or air flow rate is fed to a single fuel cell device to excite its dynamics. By measuring the corresponding output signals, each subsystem transfer function of reduced order is identified, while the unmodeled, higher-order dynamics and disturbances are described by the auxiliary input term. This provides a basis of adaptive control strategy to improve the fuel cell performance in terms of efficiency, as well as transient and steady state specifications. Simulation shows that adaptive controller is robust to the variation of fuel cell system dynamics, and it has proved promising from the experimental results.     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

Design and experimental implementation of time delay control for air supply in a polymer electrolyte membrane fuel cell system

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.     

燃油温度的多变量模糊预测函数控制

燃油供给温度的精确控制是一个具有非线性特性的流体加热供给多变量控制问题,实际测试表明,现有的单回路PID控制很难实现对燃油供给温度的动态跟踪控制,影响燃油的充分喷射、雾化及其与空气的混合,使部分燃油得不到充分燃烧,造成了能源浪费和环境污染.基于粒子群优化模糊预测函数控制(PSO-F-MPFC)的油料燃烧供给温度多变量解耦控制方法,通过与PID控制方法的比较,以及在阳极焙烧炉重油燃烧供给温度的动态跟踪控制应用表明,该方法优于原有燃油燃烧系统的PID控制,实现了燃油供给温度的动态跟踪精确控制.     

Oxygen excess ratio control for proton exchange membrane fuel cell using model reference adaptive control

Optimized robust oxygen excess ratio (OER) control for proton exchange membrane fuel cells (PEMFCs) is now a critical issue for improving their economic efficiency and performance. In general, it is very difficult to control the OER due to modeling errors, parameter uncertainties, and disturbances. To address these issues, we propose a control system based on model reference adaptive control (MRAC) various difficulties inherent air supply systems.We utilize an adaptive law to address uncertainties implementation of the MRAC and nominal feedback controllers on a nonlinear model of fuel cell system is presented for illustration of the proposed system's robustness with various operating conditions. In addition, the control performance of MRAC is compared with nominal feedback control. The results show that the presented MRAC strategy performs better than the nominal feedback control method with less wear and less control effort on the compressor. The proposed MRAC algorithm can increase the compressor efficiency by using the adaptive law even with uncertainties.     

Multivariable robust control of a proton exchange membrane fuel cell system

This paper applies multivariable robust control strategies to a proton exchange membrane fuel cell (PEMFC) system. From the system point of view, a PEMFC can be modeled as a two-input-two-output system, where the inputs are air and hydrogen flow rates and the outputs are cell voltage and current. By fixing the output resistance, we aimed to control the cell voltage output by regulating the air and hydrogen flow rates. Due to the nonlinear characteristics of this system, multivariable robust controllers were designed to provide robust performance and to reduce the hydrogen consumption of this system. The study was carried out in three parts. Firstly, the PEMFC system was modeled as multivariable transfer function matrices using identification techniques, with the un-modeled dynamics treated as system uncertainties and disturbances. Secondly, robust control algorithms were utilized to design multivariable H_∞ controllers to deal with system uncertainty and performance requirements. Finally, the designed robust controllers were implemented to control the air and hydrogen flow rates. From the experimental results, multivariable robust control is shown to provide steady output responses and significantly reduce hydrogen consumption.     

An application of indirect model reference adaptive control to a low-power proton exchange membrane fuel cell

Nonlinearity and the time-varying dynamics of fuel cell systems make it complex to design a controller for improving output performance. This paper introduces an application of a model reference adaptive control to a low-power proton exchange membrane (PEM) fuel cell system, which consists of three main components: a fuel cell stack, an air pump to supply air, and a solenoid valve to adjust hydrogen flow. From the system perspective, the dynamic model of the PEM fuel cell stack can be expressed as a multivariable configuration of two inputs, hydrogen and air-flow rates, and two outputs, cell voltage and current. The corresponding transfer functions can be identified off-line to describe the linearized dynamics with a finite order at a certain operating point, and are written in a discrete-time auto-regressive moving-average model for on-line estimation of parameters. This provides a strategy of regulating the voltage and current of the fuel cell by adaptively adjusting the flow rates of air and hydrogen. Experiments show that the proposed adaptive controller is robust to the variation of fuel cell system dynamics and power request. Additionally, it helps decrease fuel consumption and relieves the DC/DC converter in regulating the fluctuating cell voltage.     

Modeling and control of air stream and hydrogen flow with recirculation in a PEM fuel cell system—II. Linear and adaptive nonlinear control

In this part of the paper, linear and nonlinear multivariable controllers are designed for the air stream and hydrogen flow with recirculation in a proton exchange membrane (PEM) fuel cell system. The focus of the model is to obtain the desired transient performance of air stoichiometric ratio, cathode inlet pressure, and pressure difference between the anode and the cathode. Based on linearization of the nonlinear dynamic model in the first part of this paper, the coupling between control inputs and performance is analyzed first. The phase relationship between the stack voltage and water transport in frequency domain is meaningful to the future humidity estimation and active purge operation. Then, linear quadratic Gaussian (LQG) algorithm based on observer feedback is used for set-point tracking, and a model-predictive controller (MPC) with an on-line neural network identifier is also designed to improve robustness. Compared with decentralized PI controllers, the multivariable controllers improve the transient response and shows better disturbance rejection capability.     

Flow rate and humidification effects on a PEM fuel cell performance and operation

A new algorithm is presented to integrate component balances along polymer electrolyte membrane fuel cell (PEMFC) channels to obtain three-dimensional results from a detailed two-dimensional finite element model. The analysis studies the cell performance at various hydrogen flow rates, air flow rates and humidification levels. This analysis shows that hydrogen and air flow rates and their relative humidity are critical to current density, membrane dry-out, and electrode flooding. Uniform current densities along the channels are known to be critical for thermal management and fuel cell life. This approach, of integrating a detailed two-dimensional across-the-channel model, is a promising method for fuel cell design due to its low computational cost compared to three-dimensional computational fluid dynamics models, its applicability to a wide range of fuel cell designs, and its ease of extending to fuel cell stack models.     

Feedback-Linearization-Based Nonlinear Control for PEM Fuel Cells

This paper presents a dynamic nonlinear model for polymer electrolyte membrane fuel cells (PEMFCs). A nonlinear controller is designed based on the proposed model to prolong the stack life of the PEM fuel cells. Since it is known that large deviations between hydrogen and oxygen partial pressures can cause severe membrane damage in the fuel cell, feedback linearization is applied to the PEM fuel cell system so that the deviation can be kept as small as possible during disturbances or load variations. A dynamic PEM fuel cell model is proposed as a nonlinear, multiple-input multiple-output system so that feedback linearization can be directly utilized. During the control design, hydrogen and oxygen inlet flow rates are defined as the control variables, and the pressures of hydrogen and oxygen are appropriately defined as the control objectives. The details of the design of the control scheme are provided in the paper. The proposed dynamic model was tested by comparing the simulation results with the experimental data previously published. The simulation results show that PEMFCs equipped with the proposed nonlinear controls have better transient performances than those with linear controls.     

基于模糊控制的风电机组独立变桨距控制

在额定风速以上时,通常采用变桨距控制技术调节大型风电机组来稳定其输出功率.由于风力发电系统的数学模型具有高度非线性、多变量、强耦合的特点,风速又具有多变性,因此文章在分析传统的PID变桨距控制技术优缺点的基础上,提出了基于三维模糊自适应PID控制的独立变桨距控制技术,并且引入风速的模糊前馈控制技术.对1 MW风电机组进行仿真,结果表明,在额定风速以上时,该方法不仅能稳定风电机组的输出功率,而且可以减小桨叶的拍打振动.