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.     

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.     

Air supply regulation for PEMFC systems based on uncertainty and disturbance estimation

In this paper, a novel system analysis and controller design method for the air supply of proton exchange membrane (PEM) fuel cell systems is proposed. Firstly, a class of nonlinear systems with specific structures are introduced. In further analysis, the introduced system can be divided into two parts: one is fast and include disturbances and uncertainty, and the other is relatively slow. We change the introduced system into an equivalent cascade system. Some state variables of the first subsystem are acted as the inputs of the second subsystem. Furthermore, the similarities between the air supply system and the equivalent cascade system are proved, and a cascade controller is proposed based on uncertainty and disturbance estimation (UDE) and Lyapunov method. Moreover, we implement the algorithm in the air supply system for PEM fuel cells. Experimental results show the effectiveness of the proposed method.     

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.     

A novel robust adaptive sliding mode control using stochastic gradient descent for PEMFC power system

This study represents a comparison of novel robust adaptive sliding mode control using stochastic gradient descent (ASMCSGD) versus the super twisting algorithm (STA) for the proton exchange membrane fuel cell (PEMFC) power system. PEMFC has been constantly encountered with external disturbances such as inlet gas pressures and temperature fluctuations which a novel adaptive control law should be designed to be robust against the mentioned perturbations. The proposed ASMCSGD is based on the conventional sliding mode control (SMC), which guarantees robustness and restraining external disturbances. As is common, the main drawback of conventional SMC is the generation of a chattering phenomenon. Therefore, by using the stochastic gradient descent (SGD), a novel adaptive control law is designed. Hence, the SGD can continuously calculate the adaptive gain and then guarantee robustness besides minimizing the chattering phenomenon. The stability of the PEMFC power system for both controllers ASMCSGD and STA is demonstrated via the Lyapunov theorem. Simulation results have been studied and illustrate the effectiveness of the proposed controller successfully using Matlab/Simulink.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

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.     

Uncertainty analysis and robust control of fuel delivery systems considering nitrogen crossover phenomenon

In this paper, the fuel delivery subsystem (FDS) with hydrogen recirculation and anode bleeding is applied in proton exchange membrane fuel cell (PEMFC) system, which is utilized to supply hydrogen to the anode of stack and recirculate fuel back to the supply line. As the diffusion of nitrogen from cathode to anode is inevitable in a real PEMFC during long-term operation. To prevent system performance decline due to nitrogen accumulation. Therefore, this paper firstly develops a control-oriented nonlinear dynamic FDS model involving gas diffusion. Additionally, the FDS is very sensitive to operating environment, uncontrolled operation conditions may cause stack degradation. Specifically, a method based on Monte Carlo simulation is proposed to identify the key parameter boundaries. Then the gas distribution in FDS due to nitrogen crossover is analyzed in detail. After this, a hybrid robust methodology based on sliding mode algorithm is also proposed to maintain adequate hydrogen pressure supply, suitable hydrogen and nitrogen content in the system in presence of nitrogen crossover and renewed uncertainties. Finally, the performance of the presented controller is compared with nonlinear PID (NPID) control and nonlinear multi-input-multi-output (NMIMO) control through a hardware-in-the-loop test bench. Experimental results show that the hybrid controller is accurate and suitable for control purpose, the nitrogen content is restricted to the given range and the variation of output voltage is limited to the desired boundaries, the feasibility and effectiveness are validated.     

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.     

Oxygen excess ratio control of PEM fuel cells using observer-based nonlinear triple-step controller

This paper proposes a novel observer-based nonlinear triple-step controller for the air supply system of polymer electrolyte membrane (PEM) fuel cell. The control objective is adjusting the oxygen excess ratio to its reference value under fast current transitions, so as to avoid the oxygen starvation and obtain the maximum net power. Considering that the cathode pressure cannot be measured directly, we design a disturbance observer to estimate the cathode pressure based on the developed third-order nonlinear model of air supply system. Next, a triple-step nonlinear method is applied to derive an oxygen excess ratio tracking controller, wherein the stability of closed-loop system is guaranteed by Lyapunov-based technique. Subsequently, several key issues of controller in practical implementation are explained, and then the robustness analysis against the considered lumped disturbance is carried out. Finally, the performance of the proposed control scheme is validated through a series of comparative simulations, and the simulation results demonstrate the effectiveness and robustness of the proposed approach under different load variations and parameter uncertainties.     

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.     

Robust and optimal technical method with application to hydrogen fuel cell systems

In this paper, a robust nonsingular fast converging sliding mode control (RNFCSMC) with particle swarm optimization (PSO)-based radial basis function (RBF) neural network is presented and applied in hydrogen fuel cell systems capable to maintain low harmonic distortion even in case of nonlinear load. The proposed technical method is a modified structure: a RNFCSMC plus a PSO-based RBF neural network. Though the classic sliding mode control has inherent robustness against plant parameter variations and load disturbances, the convergence of the system states to the zero is usually asymptotical in infinite time. The RNFCSMC is introduced to assure the finite time convergence of the system states and there is no singularity problem. But, once a severe load disturbance is applied, the chattering or steady-state error still exists in RNFCSMC. The PSO-based RBF neural network is employed to determine the control gains of the RNFCSMC, thus eliminating the chattering or steady-state error so that the system performance reaches the optimal point. The proposed technical method has been realized (1 kW, 110V_rms/60 Hz) for the actual single-phase hydrogen fuel cell inverters controlled by a TI DSP. Simulation and experimental results reveal that even under nonlinear load circumstances the proposed technical method yields voltage total harmonic distortion (THD) less than 1.4%, which excels the IEEE standard 519, thus demonstrating the effectiveness of the proposed technical method. Because the proposed hydrogen fuel cell system is considerably simpler to implement than classic sliding mode system and offers faster computational speed, this paper will be a beneficial reference to related control designers of hydrogen fuel cell systems.     

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.     

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.     

Control of PEM fuel cell power system using sliding mode and super-twisting algorithms

Traditional sliding mode controller applied to a DC/DC boost converter for the improvement and optimization of the proton exchange membrane fuel cell (PEMFC) system efficiency, has the drawback of chattering phenomenon. Thus, based on the analysis of the mathematical model of PEMFC, this paper addresses the second order super twisting algorithm (STA) as a solution of chattering reduction, Stability of the closed loop system is analytically proved using Lyapunov approach for the proposed controller. The model and the controllers are implemented in the MATLAB and SIMULINK environment. A comparison of results indicates that the suggested approach has considerable advantages compared to the classical sliding mode control.     

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.     

Rapid-convergent sliding mode proportional-integral technology with fuzzy gain scheduling for hydrogen energy applications

This paper presents a rapid-convergent sliding mode proportional-integral (PI) technology with fuzzy gain scheduling for hydrogen energy applications. The rapid-convergent sliding mode control can provide insensitivity to system uncertainties and finite system-state convergence time to origin, however undesirable chattering behavior exists. The proposed technology utilizes the expansion of the plant model for designing the formation of a rapid-convergent sliding mode PI control and then the chatter is remarkably lessened. Moreover, a fuzzy gain scheduling assists in tuning the PI control parameters against uncertain disturbances. Simulations show that the presented hydrogen energy system leads to low distorted output-voltage under nonlinear load and fast transience under step-load change. Lab experiments obtained with a developed hydrogen energy system using a digital signal processing algorithm have been offered to demonstrate the performance improvement, especially in the presence of seriously nonlinear circumstance. In contrast with the proposed hydrogen energy system, the classic sliding mode-controlled hydrogen energy system has also been evaluated via both simulation and experiment.     

Design and implementation of LQR/LQG strategies for oxygen stoichiometry control in PEM fuel cells based systems

This paper presents the oxygen stoichiometry control problem of proton exchange membrane (PEM) fuel cells and introduces a solution through an optimal control methodology. Based on the study of a non-linear dynamical model of a laboratory PEM fuel cell system and its associated components (air compressor, humidifiers, line heaters, valves, etc.), a control strategy for the oxygen stoichiometry regulation in the cathode line is designed and tested. From a linearised model of the system, an LQR/LQG controller is designed to give a solution to the stated control problem. Experimental results show the effectiveness of the proposed controllers design.     

Weighted fusion control for proton exchange membrane fuel cell system

Oxygen excess ratio (OER) is closely correlated with the power generation efficiency and dynamic performance of proton exchange membrane fuel cell (PEMFC) system. As OER changes with varying load, it is prone to oxygen starvation and slow response to OER reference value, and great challenges to OER control technology are brought. To this end, a dual closed-loop weighted fusion control for PEMFC system is proposed. The outer loop is utilized to obtain the optimal OER reference value, and the inner loop is utilized to track the OER reference value. This inner loop combines the merits of active disturbance rejection control (ADRC) algorithm and fuzzy self-tuned PID (FSTPID) method. Simulation results reveal that the proposed approach is superior to the other three methods in reducing the overshoot, settling time and avoiding oxygen starvation issues, and also in improving several key performance indices, such as integrated absolute error, settling time, etc.     

SSCI mitigation of grid‐connected DFIG wind turbines with fractional‐order sliding mode controller

To mitigate subsynchronous control interaction (SSCI) in doubly fed induction generator (DFIG)‐based wind farm, this paper proposes a robust controller for rotor‐side converter (RSC) using fractional‐order sliding mode controller (FOSMC). The proposed FOSMC can improve robustness and convergence properties of the controlled system, thus achieving SSCI damping under various operating conditions. Impedance‐based analysis and time‐domain simulation are performed to check the capability of the designed FOSMC as compared with conventional sliding mode control (SMC) and subsynchronous damping control (SSDC). Simulation results demonstrate that FOSMC can mitigate SSCI within shorter time and effectively reduce the fluctuation range of system transient responses under various operating conditions of wind speeds and compensation levels. Moreover, FOSMC also improves system robustness against parameter uncertainties and external disturbances, which is important for safe operation of realistic wind farms.