Nonlinear controller design based on cascade adaptive sliding mode control for PEM fuel cell air supply systems

Optimized robust control for proton exchange membrane (PEM) fuel cell air supply systems is now a hot topic in improving the performance of oxygen excess ratio (OER) and the net power. In this paper, a cascade adaptive sliding mode control method is proposed to regulate oxygen excess ratio (OER) for proton exchange membrane (PEM) fuel cell air supply systems. Based on a simplified sixth-order nonlinear dynamic model, which takes parametric uncertainties, external disturbances and measurement noises into consideration, the nonlinear controller based on cascade adaptive sliding mode (NC-ASM) control is proposed. The method combines the nonlinear terms of super twisting algorithm and two added linear terms, and the modified second order sliding mode (SOSM) algorithm based on an observer is employed to form a cascade structure. Besides, an adaptive law is also utilized to regulate the parameters of the NC-ASM controller online. The performance of the controller is implemented on a real-time emulator. The results show that the proposed strategy performs better than the conventional constant sliding mode (CSM) control and PID method. Though during large range of load current and in the presence of various uncertainties, disturbances and noises, the NC-ASM controller can always converge rapidly, the feasibility and effectiveness are validated.     

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.     

Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer

In order to improve the safety and reliability of proton exchange membrane fuel cell system, this paper proposes a novel robust fault observer for the fault diagnosis and reconstruction of the PEMFC air management system. First, considering the complexity and large computation of the nonlinear PEMFC system, a linear parameter-varying (LPV) model is introduced to describe the system behavior and reduce the computation cost. Then, an augmented state observer based on the LPV model is proposed for simultaneously estimating the internal states and component faults. The robustness is guaranteed by taking the system disturbances and measurement noises into consideration when designing the observer gain. The observer design is transformed into a process of solving a set of linear inequality matrices. According to the results, the augmented robust observer can accurately estimate the system states and faults under different conditions. Moreover, to realize the fault tolerant control of the air supply, the oxygen stoichiometry estimator is designed taking consideration of system fault information and a corresponding controller is employed for air compressor voltage following the net power maximization strategy.     

Robust SVM-direct torque control of induction motor based on sliding mode controller and sliding mode observer

This paper proposes a design of control and estimation strategy for induction motor based on the variable structure approach. It describes a coupling of sliding mode direct torque control (DTC) with sliding mode flux and speed observer. This algorithm uses direct torque control basics and the sliding mode approach. A robust electromagnetic torque and flux controllers are designed to overcome the conventional SVM-DTC drawbacks and to ensure fast response and full reference tracking with desired dynamic behavior and low ripple level. The sliding mode controller is used to generate reference voltages in stationary frame and give them to the controlled motor after modulation by a space vector modulation (SVM) inverter. The second aim of this paper is to design a sliding mode speed/flux observer which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The effectiveness of the whole composed control algorithm is investigated in different robustness tests with simulation using Matlab/Simulink and verified by real time experimental implementation based on dS pace 1104 board.     

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.     

Experimental analysis of optimal performance for a 5 kW PEMFC system

This paper investigates the issue of performance optimization for proton exchange membrane fuel cell (PEMFC) system. In PEMFC system, the system efficiency is the key parameters to evaluate the system performance which is sensitive to the air flow rate. Thus, the careful selection of the air flow rate is crucial to ensure efficient, reliable and durable operation of the PEMFC system. In this paper, the dynamic response of the system under variable air flow rate is studied in detail by means of experiments on the built 5 kW PEMFC system with 110 cells and a catalyst active area of 250 cm². The oxygen excess ratio (OER) is defined to indicate the state of oxygen supply. The experimental results show that the maximum efficiency is existed under certain net current. The OER conditions have the optimal characteristic for system efficiency. Through the optimization of system performance, the system efficiency can be increased by 12.2% on average. At the same time, the system dynamic characteristic under oxygen starvation and oxygen saturation are analyzed in detail based on the experimental data.     

Evaluation method of oxygen excess ratio control under typical control laws for proton exchange membrane fuel cells

For real-used proton exchange membrane fuel cells (PEMFC), it is critical to design an effective controller and evaluate its performance. Current evaluations of controllers are often empirical or qualitative, and quantitative evaluation methods are lacking. In this paper, the quantifiable objective evaluation method is proposed for assessing the controller performance, including optimal control, adaptive control, variable structure control, and model-based control, aiming at the oxygen excess ratio. In the method, the anti-starvation, transient-state, steady-state, and multiple load-changing performances are comprehensively considered through weighting, rating, and especially the introduction of negative scores through the integration of four independent indexes. The importance and effectiveness of evaluation method are verified through the specific analysis of four controllers and the internal states of PEMFC. Besides, the evaluation method can be extended appropriately, such as considering the robustness, optimal output power, and other practical problems, which is significant for the development of PEMFC system controller.     

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.     

Multivariable robust PID control for a PEMFC system

This paper proposes robust proportional-integral-derivative (PID) control for a proton exchange membrane fuel cell (PEMFC) system. We model a PEMFC as a multivariable system, and apply identification techniques to obtain the system’s transfer function matrices, where system variations and disturbances are regarded as uncertainties. Because robust control can cope with system uncertainties and disturbances, it has been successfully applied to improve the stability, performance, and efficiency of PEMFC systems in previous studies. However, the resulting robust controllers might be too complicated for hardware implementation. On the other hand, PID control has been widely applicable to engineering practices because of its simple structure, but it lacks stability analysis for systems with uncertainties. Therefore, by combining the merits of robust control and PID control, we design robust PID controllers for the PEMFC system. Based on evaluation of stability, performance, and efficiencies, the proposed robust PID controllers are shown to be effective.     

Disturbance observer‐based integral sliding mode control for wind energy conversion systems

Recently, wind power production has been under the focus in generating power and became one of the main sources of alternative energy. Generating of maximum power from wind energy conversion system (WECS) requires accurate estimation of aerodynamic torque and uncertainties presented in the system. The current paper proposed the generalized high‐order disturbance observer (GHODO) with integral sliding mode control (ISMC) for extraction of maximum power via variable speed wind turbine by accurate estimation of wind speed. The assumption in previous works that considers the aerodynamic torque as slow‐varying is not applicable for the real system. Therefore, the high‐order disturbance observers were designed for precise estimation of uncertainties with fast‐changing behavior. A robust control system was designed to control the speed of the rotor at the optimal speed ratio. The obtained simulation results have shown the better performance characteristics than conventional linear quadratic regulator (LQR) approach. The stability of the proposed algorithm was proven by Lyapunov stability anaysis. Simulations results were obtained in Matlab/Simulink environment.     

Effect of MEA conditioning on PEMFC performance and EIS response under steady state condition

In this work, effect of three different commonly used on-line membrane-electrode assembly (MEA) conditioning procedures on the final MEA performance and electrochemical impedance spectroscopy (EIS) response was studied under different operation conditions. The conditioning methods were: constant voltage, constant current and US fuel cell council (USFCC) protocol.     

One-pot synthesis of Pt/CeO2/C catalyst for improving the ORR activity and durability of PEMFC

Oxygen reduction reaction (ORR) activity and durability of Pt catalysts should be both valued for successful commercialization of proton exchange membrane fuel cells (PEMFCs). We offer a facile one-pot synthesis method to prepare Pt/CeO₂/C composite catalysts. CeO₂ nanoparticles, with high Ce³⁺ concentration ranging from 30.9% to 50.6%, offers the very defective surface where Pt nanoparticles preferentially nuclear and growth. The Pt nanoparticles are observed sitting on the CeO₂ surface, increasing the PtCeO₂ interface. The high concentration of oxygen vacancies on CeO₂ surface and large PtCeO₂ interface lead to the strong PtCeO₂ interaction, effectively improving the ORR activity and durability. The mass activity is increased by up to 50%, from 36.44 mA mg^?1 of Pt/C to 52.09 mA mg^?1 of Pt/CeO₂/C containing 20 wt.% CeO₂. Pt/CeO₂/C composite catalysts containing 10–30 wt.% CeO₂ loss about 80% electrochemical surface area after 10,000 cycles, which is a fivefold enhancement in durability, compared to Pt/C losing 79% electrochemical surface area after 2000 cycles.     

Pressure control in a PEM fuel cell via second order sliding mode

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

The optimal performance estimation for an unknown PEMFC based on the Taguchi method and a generic numerical PEMFC model

In this paper, a new approach to estimate the optimal performance of an unknown proton exchange membrane fuel cell (PEMFC) has been proposed. This proposed approach combines the Taguchi method and the numerical PEMFC model. Simulation results obtained using the Taguchi method help to determine the value of control factors that represent the tested unknown PEMFC. The objective of reducing both fuel consumption and operation cost can be achieved by determining the parameters for the unknown PEMFC. In addition, the optimal operation power for the tested unknown PEMFC can also be predicted. Experimental results on the test equipment show that the proposed approach is effective in optimal performance estimation for the tested unknown PEMFC, thus demonstrating the success achieved by combining the Taguchi method and the numerical PEMFC model.     

The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks

This paper has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a genetic algorithm neural networks (GANN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the GANN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in GANN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this paper will present an approach for constructing a GANN model with precise accuracy for the output voltage of PEMFC. For achieving the GANN model with high precision, a troublesome work has to be taken care of, that is, to determine all the parameters required in GANN. We will introduce Taguchi method to solve this problem as well. Finally, to show the superiority of proposed model, this approach has compared the estimation values of output voltage for PEMFC from GANN and BPNN models without using Taguchi method. One can easily find that the error of the proposed method is much smaller than that of the GANN model without Taguchi method and of the BPNN model; that is, the proposed approach has better performance on estimation for PEMFC output voltages.     

A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.     

Robust control of four-phase interleaved boost converter by considering the performance of PEM fuel cell current

Hydrogen associated with Proton Exchange Membrane Fuel Cell (PEMFC) as the prime candidate energy is becoming attention in transportation. However, the cost and the service lifespan are the main reasons that limit PEMFC wide application. In this paper, the super-twisting sliding mode (STSM) controller is designed for a four-phase interleaved boost converter (IBC) coupled with a PEMFC. The proposed controller can enhance the robustness of the output voltage while reducing the PEMFC current overshoot as much as possible for protection under a certain limitation of the PEMFC current ripple. The stability of the proposed controller is proved by the Lyapunov theorem. A typical proportional-integral (PI) controller based on ac small-signal model is designed for further comparison and discussion. The effectiveness of the STSM controller is further evaluated through experimental results obtained with a 1 kW fuel cell system based on a real-time hardware-in-the-loop system.     

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer’s objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies.To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells

The accurate mathematical model is the key issue to simulation and design of the fuel cell power systems. Aiming at estimating the proton exchange membrane fuel cell (PEMFC) model parameters, an adaptive RNA genetic algorithm (ARNA-GA) which is inspired by the mechanism of biological RNA is proposed. The ARNA-GA uses the RNA strands to represent the potential solutions and new genetic operators are designed for improving the global searching ability. In order to maintain the population diversity and avoid premature convergence, on the basis of the dissimilarity coefficient, the adaptive genetic strategy that allows the algorithm dynamically select crossover operation or mutation operation to execute is proposed. Numerical experiments have been conducted on some benchmark functions with high dimensions. The results indicate that ARNA-GA has better search capability and a higher quality of solutions. Finally, the proposed approach has been applied for the parameter estimation of PEMFC model and the satisfactory results are reached.