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.     

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.     

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.     

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.     

On-line implementation of model free controller for oxygen stoichiometry and pressure difference control of polymer electrolyte fuel cell

This paper proposes and validates a model free controller to improve the real time operating conditions of Proton Exchange Membrane Fuel Cells (PEMFC). This approach is based on an ultra-local model that does not depend on a precise knowledge of the system. It is perfectly adapted to a complex system such as the fuel cell, while benefiting from the ease of online implementation and low computational cost. The designed controller is used to regulate both the oxygen stoichiometry and the membrane inlet pressure, which are crucial operating conditions for the fuel cell's lifetime. The objectives of the proposed control strategy are twofold: preventing the starvation failure, and limiting the potential for mechanical degradation of the membrane during a large pressure difference. The performance of the proposed control strategy is initially evaluated by a simulation environment for both oxygen stoichiometry and inlet pressure difference control of fuel cell stack. An online validation on 1.2 KW fuel cell stack is conducted to control the membrane pressure drop. Two case studies are comprehensively investigated in relation to stoichiometry control: set point tracking and rejection of unmeasured disturbances caused by current variations. Simulations and experimental results reveal that the proposed controller provides significantly better performance in terms of fast trajectory tracking, and ensures less overshoot compared to the Fuzzy PID and PID controller. This efficiency is proven using the Integral Absolute Error (IAE), Integral Squared Error (ISE) and Integral of the Square input (ISU) performance indexes.     

PEM fuel cell supported distribution static compensator for power quality enhancement in three-phase four-wire distribution system

One of the major problems in electrical power system is the lack of quality of power due to the rapid growth of nonlinear load and unbalanced load utilization in three-phase four-wire distribution system. In this paper, PEM (Proton Exchange Membrane) fuel cell supported four-leg Distribution Static Compensator (DSTATCOM) is modelled to mitigate harmonics, neutral current and load balancing under nonlinear load and unbalanced load conditions in three-phase four-wire distribution system. The instantaneous reactive power (IRP) theory control algorithm is proposed for four-leg DSTATCOM. The Real coded Genetic Algorithm (RGA) optimized Proportional Integral (PI) controller and Adaptive Neuro Fuzzy Inference System (ANFIS) controller are used for regulating the DC link voltage of DSTATCOM. This paper also investigates the performance of ANFIS based DSTATCOM with conventional method. The proposed system is modelled and its performance is analyzed in 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.     

Dynamic modelling of PEM fuel cell system for simulation and sizing of marine power systems

This article presents a model of a proton exchange membrane fuel cell (PEMFC) system for marine power systems. PEMFC in marine hybrid power sources can have various power ranges and capacities in contrast with vehicle applications. Investigating PEMFCs behaviour and performance for various conditions and configurations is demanded for proper sizing and feasibility studies. Hence, modelling and simulation facilitate understanding the performance of the PEMFC behaviour with various sizes and configurations in power systems. The developed model in this work has a system level fidelity with real time capabilities, which can be utilized for simulator approaches besides quasi-static studies with a power-efficiency curve. Moreover, the model can be used for scaling the PEMFC power range by considering transient responses and corresponding efficiencies. The Bond graph approach as a multi-disciplinary energy based modelling strategy is employed for the PEMFC as a multi domains system. In the end, various PEMFC cell numbers and compressor sizes have been compared with power-efficiency curves and transient responses in a benchmark.     

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.     

Study on electrochemical and mass transfer coupling characteristics of proton exchange membrane (PEM) fuel cell based on a fin-like electrode surface

Proton exchange membrane (PEM) Fuel cells are widely used because of its environmental protection and high efficiency. In the present study, a novel fin-like structure of the electrode surface is investigated by establishing the theoretical model and numerical simulating. For this purpose, the influence of different fin spacing and pressure boost on the performance of the PEM fuel cell is analyzed by numerical simulation. Results show that increasing pressure of cathode or both side experiences greater performance improvement compared to the other cases, and the maximum values of power density during both conditions is founding for fin density 1/1, followed by fin density 1/12, then last the basic model. Furthermore, the analysis shows that increasing CL surface area combined with cathode pressurization is the best strategy for fuel cell performance optimization. The fin structure under the condition of cathode pressurization can effectively reduce the transmission resistance and over potential of the fuel cell by theoretical calculation, which is coinciding well with the simulation results.     

Effects of temperature uncertainty on the performance of a degrading PEM fuel cell model

The performance of a fuel cell is subject to uncertainties on its operational and material parameters. Among operational parameters, temperature is one of the most influential factors. This work focuses on this parameter. A statistical analysis is developed on the output voltage of proton exchange membrane fuel cell models. The first model does not include any degradation, whereas the second one introduces a degradation rate on the cell active area. To complete the simulation work, a full factorial design is carried out and a statistical sensitivity analysis (ANOVA) is used to compute the effects and contributions of important parameters of the model on the output voltage.     

Prognostics of PEM fuel cell in a particle filtering framework

Proton Exchange Membrane Fuel Cells (PEMFC) suffer from a limited lifespan, which impedes their uses at a large scale. From this point of view, prognostics appears to be a promising activity since the estimation of the Remaining Useful Life (RUL) before a failure occurs allows deciding from mitigation actions at the right time when needed. Prognostics is however not a trivial task: 1) underlying degradation mechanisms cannot be easily measured and modeled, 2) health prediction must be performed with a long enough time horizon to allow reaction. The aim of this paper is to face these problems by proposing a prognostics framework that enables avoiding assumptions on the PEMFC behavior, while ensuring good accuracy on RUL estimates. Developments are based on a particle filtering approach that enables including non-observable states (degradation through) into physical models. RUL estimates are obtained by considering successive probability distributions of degrading states. The method is applied on 2 data sets, where 3 models of the voltage drop are tested to compare predictions. Results are obtained with an accuracy of 90 h around the real RUL value (for a 1000 h lifespan), clearly showing the significance of the proposed approach.     

Numerical studies on an air-breathing proton exchange membrane (PEM) fuel cell stack

Air-breathing proton exchange membrane (PEM) fuel cells provide for fully or partially passive operation and have gained much interest in the past decade, as part of the efforts to reduce the system complexity. This paper presents a detailed physics-based numerical analysis of the transport and electrochemical phenomena involved in the operation of a stack consisting of an array of vertically oriented air-breathing fuel cells. A comprehensive two-dimensional, nonisothermal, multi-component numerical model with pressurized hydrogen supply at the anode and natural convection air supply at the cathode is developed and validated with experimental data. Systematic parametric studies are performed to investigate the effects of cell dimensions, inter-cell spacing and the gap between the array and the substrate on the performance of the stack. Temperature and species distributions and flow patterns are presented to elucidate the coupled multiphysics phenomena. The analysis is used to determine optimum stack designs based on constraints on desired performance and overall stack size.     

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.     

Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell

This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations.     

Design of proton exchange membrane fuel cell grid-connected system based on resonant current controller

Because of its high efficiency, low pollution and good stability, proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising technologies for a wide range of applications, such as distributed power generation, transportation, portable power source and automobile. In a PEMFC grid-connected system, the proportion integration (PI) regulator can achieve zero error for the dc components in the rotating frame, but cannot achieve zero error for the ac components in the rotating frame. Hence, the PEMFC grid-connected system will produce a large number of harmonics. In order to overcome this shortcoming, a proportion integration resonant (PIR) controller is utilized to realize zero magnitude error and selective disturbance rejection. Instead of the PIR controller, a vector proportion integration (VPI) controller is designed to quickly and accurately regulate current which achieve zero both amplitude and phase frequency response at the resonant frequency simultaneously. In this paper, the PEMFC grid-connected system based on PIR and VPI controllers are developed according to the operating characteristics of a PEMFC generation system, then analyze and compare the performance of compensation harmonics between them. The total harmonic distortion (THD) of grid-connected voltage and current are measured by means of the criterion of IEEE Std1547-2003. This proposed grid-connected method will provide a novel approach for the design of advanced PEMFC grid-connected control system.     

Analysis of PEM fuel cell experimental data using principal component analysis and multi linear regression

Polarisation curves performed at the Fuel Cell System Laboratory (FC LAB) at Belfort on a PEM fuel cell stack using a homemade fully instrumented test bench led to more than 100 variables depending on time. Visualising and analysing all the different test variables are complex. In this work, we show how the Principal Component Analysis (PCA) method helps to explore correlations between variables and similarities between measurements at a specific sampling time (individuals). To complete this method, an empirical model of the PEM fuel cell is proposed by linking the different input parameters to the cell voltage using Multiple Linear Regression.