Robust fuzzy sliding mode control for air supply on PEM fuel cell system

In this paper, an adaptive fuzzy sliding mode controller is employed for air supply on proton exchange membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given set point in order to prevent oxygen starvation and damage to the fuel-cell stack. The proposed control scheme consists of two parts: a sliding mode controller (SMC) and fuzzy logic controller (FLC) with an adjustable gain factor. The SMC is used to calculate the equivalent control law and the FLC is used to approximate the control hitting law. The performance of the proposed control strategy is analysed through simulations for different load variations. The results indicated that the adaptive fuzzy sliding mode controller (AFSMC) is excellent in terms of stability and several key performance indices such as the integral squared error (ISE), the integral absolute error (IAE) and the integral time-weighted absolute error (ITAE), as well as the settling and rise times for the closed-loop control system.Peer ReviewedPostprint (author's final draft

Algebraic observer design for PEM fuel cell system

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, the concept of the algebraic observer is applied to Proton Exchange Membrane Fuel Cell (PEMFC) system. The aim of the proposed observer is to reconstruct the oxygen excess ratio through estimation of their relevant states in real time from the measurement of the supply manifold air pressure. A robust differentiation method is adopted to estimate in finite-time the time derivative of the supply manifold air pressure. Then, the relevant states are reconstructed based on the output-state inversion model. The objective is to minimize the use of extra sensors in order to reduce the costs and enhance the system accuracy. The performance of the proposed observer is analyzed through simulations considering measurement noise and different stack-current variations. The results show that the algebraic observer estimates in finite time and robustly the oxygen-excess ratio.Peer ReviewedPostprint (author's final draft

Algebraic observer-based output-feedback controller design for a PEM fuel cell air-supply subsystem

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, an algebraic-observer-based output-feedback controller is proposed for a Proton Exchange Membrane Fuel Cell (PEMFC) air-supply subsystem, based on both algebraic differentiation and sliding-mode control approaches. The goal of the design is to regulate the Oxygen Excess Ratio (OER) towards its optimal setpoint value in the PEMFC air-supply subsystem. Hence, an algebraic estimation approach is used to reconstruct the OER based on a robust differentiation method. The proposed observer is known by its finite-time convergence and low computational time compared to other observers presented in the literature. Then, a twisting controller is designed to control the OER by manipulating the compressor motor voltage. The parameters of the twisting controller have been calculated by means of an off-line tuning procedure. The performance of the proposed algebraic-observer-based output-feedback controller is analyzed through simulations for different stack-current changes, for parameter uncertainties and for noise rejection. Results show that the proposed approach properly estimates and regulates the OER in finite-time.Peer ReviewedPostprint (author's final draft

Nonlinear observer design for PEM fuel-cell systems using first-order sliding mode techniques

This paper presents a nonlinear observer design for Proton Exchange Membrane Fuel-Cell (PEMFC) systems. The aim of the proposed observer is to reconstruct the oxygen excess ratio through the estimation of their relevant states in real time from the measurement of the supply manifold air pressure. A First-Order Sliding Mode (FOSM) differentiation method is adopted to estimate, in finite time, the time derivative of the supply manifold air pressure. By means of the output-state inversion model, the relevant states are reconstructed. The objective of the proposed appproach is to minimize the use of additional sensors in order to reduce the costs and enhance the system accuracy. The performance of the proposed observer is analyzed through simulations considering measurement noise and different stack-current variations. The results show that the nonlinear observer properly estimates in finite time and robustly the oxygen excess ratio.Peer ReviewedPostprint (author's final draft

Performance improvement in polymer electrolytic membrane fuel cell based on nonlinear control strategies—A comprehensive study

A Polymer Electrolytic Membrane Fuel Cell (PEMFC) is an efficient power device for automobiles, but its efficiency and life span depend upon its air delivery system. To ensure improved performance of PEMFC, the air delivery system must ensure proper regulation of Oxygen Excess Ratio (OER). This paper proposes two nonlinear control strategies, namely Integral Sliding Mode Control (ISMC) and Fast Terminal ISMC (FTISMC). Both the controllers are designed to control the OER at a constant level under load disturbances while avoiding oxygen starvation. The derived controllers are implemented in MATLAB/ Simulink. The corresponding simulation results depict that FTISMC has faster tracking performance and lesser fluctuations due to load disturbances in output net power, stack voltage/power, error tracking, OER, and compressor motor voltage. Lesser fluctuations in these parameters ensure increased efficiency and thus extended life of a PEMFC. The results are also compared with super twisting algorithm STA to show the effectiveness of the proposed techniques. ISMC and FTISMC yield 7% and 20% improved performance as compared to STA. The proposed research finds potential applications in hydrogen-powered fuel cell electric vehicles

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

In this research work, a Neural Network (NN) and Uniform Robust Exact Differentiator (URED) observer-based Fast Terminal Integral Sliding Mode Control (FTISMC) has been proposed for Oxygen Excess Ratio (OER) regulation of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) power systems for vehicular applications. The controller uses URED as an observer for supply manifold pressure estimation. NN is used to estimate the stack temperature which is unavailable. The suggested control method increased the PEMFC's effectiveness and durability while demonstrating the finite-time convergence of system trajectories. By controlling the air-delivery system in the presence of uncertain current requirements and measurement noise, the approach ensures maximum power efficiency. The Lyapunov stability theorem has been used to confirm the stability of the presented algorithm. In addition, the suggested method eliminated the chattering phenomenon and improved power efficiency. Given these noteworthy characteristics, the research has the potential to decrease sensor dependence and production costs while also improving the transient and steady-state response in vehicular applications

Control of Proton Exchange Membrane Fuel Cell System

265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system

Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach

In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.Peer ReviewedPostprint (author's final draft

Air flow regulation in fuel cells: an efficient design of hybrid fuzzy-PID control

This paper presents a hybrid fuzzy-PID controller for air flow supply on a Proton Exchange Membrane fuel cell (PEMFC) system. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme combines a fuzzy logic controller (FLC) and classical PID controller with a view to benefit the advantages of both controllers. The results show that the proposed technique performs significantly better than the classical PID controller and the FLC in terms of several key performances indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE) for the closed-loop control system.Peer ReviewedPostprint (author's final draft

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

A marine energy system, which is fundamentally not paired with electric grids, should work for an extended period with high reliability. To put it in another way, by employing electrical utilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells in marine power generation may reduce the loss of energy and weight in long cables and provide a platform such that each piece of marine equipment is supplied with its own isolated wire connection. Hence, fuel cells can be promising power generation equipment in the marine industry. Besides, failure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable tool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe hydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially hazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic set. The general type-2 fuzzy system is decomposed of several interval type-2 fuzzy logic systems to reduce the inherent highly computational burden of the general type-2 fuzzy systems. Linguistic rules are directly incorporated into the fuzzy system. Finally, the results demonstrate the success and effectiveness of the proposed approach in computing the risk priority number as compared to state-of-the-art methods