Adaptive online parameter estimation algorithm of PEM fuel cells

Since most of fuel cell models are generally nonlinearly parameterized functions, existing modeling techniques rely on the optimization approaches and impose heavy computational costs. In this paper, an adaptive online parameter estimation approach for PEM fuel cells is developed in order to directly estimate unknown parameters. The general framework of this approach is that the electrochemical model is first reformulated using Taylor series expansion. Then, one recently proposed adaptive parameter estimation method is further tailored to estimate the unknown parameters. In this method, the adaptive law is directly driven by the parameter estimation errors without using any predictors or observers. Moreover, parameter estimation errors can be guaranteed to achieve exponential convergence. Besides, the online validation of regressor matrix invertibility are avoided such that computation costs can be effectively reduced. Finally, comparative simulation results demonstrate that the proposed approach can achieve better performance than least square algorithm for estimating unknown parameters of fuel cells.Postprint (published version

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.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

Adaptive nonlinear parameter estimation for a proton exchange membrane fuel cell

© 2022 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 worksParameter estimation is vital for modeling and control of fuel cell systems. However, the nonlinear parameterization is an intrinsic characteristic in the fuel cell models such that classical parameter estimation schemes developed for linearly parameterized systems cannot be applied. In this paper, an alternative framework of adaptive parameter estimation is designed to address the real-time parameter estimation for fuel cell systems. The parameter estimation can be divided into two cascaded components. First, the dynamics with the unknown parameters are estimated by a new unknown system dynamics estimator (USDE). Inspired by an invariant manifold, this USDE is designed by applying simple filter operations such that the information of the state derivative is not required. Secondly, an adaptive law driven by the function approximation error is proposed for recovering unknown model parameters. Exponential convergence of the estimated parameters to the true values can be proved under the monotonicity condition. Finally, experimental results on a practical proton exchange membrane fuel cell system are given to verify the effectiveness of the proposed schemes.Peer ReviewedPostprint (author's final draft

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

Control Strategies of DC–DC Converter in Fuel Cell Electric Vehicle

There is a significant need to research and develop a compatible controller for the DC–DC converter used in fuel cells electric vehicles (EVs). Research has shown that fuel cells (FC) EVs have the potential of providing a far more promising performance in comparison to conventional combustion engine vehicles. This study aims to present a universal sliding mode control (SMC) technique to control the DC bus voltage under varying load conditions. Additionally, this research will utilize improved DC–DC converter topologies to boost the output voltage of the FCs. A DC–DC converter with a properly incorporated control scheme can be utilized to regulate the DC bus voltage–. A conventional linear controller, like a PID controller, is not suitable to be used as a controller to regulate the output voltage in the proposed application. This is due to the nonlinearity of the converter. Furthermore, this thesis will explore the use of a secondary power source which will be utilized during the start–up and transient condition of the FCEV. However, in this instance, a simple boost converter can be used as a reference to step–up the fuel cell output voltage. In terms of application, an FCEV requires stepping –up of the voltage through the use of a high power DC–DC converter or chopper. A control scheme must be developed to adjust the DC bus or load voltage to meet the vehicle requirements as well as to improve the overall efficiency of the FCEV. A simple SMC structure can be utilized to handle these issues and stabilize the output voltage of the DC–DC converter to maintain and establish a constant DC–link voltage during the load variations. To address the aforementioned issues, this thesis presents a sliding mode control technique to control the DC bus voltage under varying load conditions using improved DC–DC converter topologies to boost and stabilize the output voltage of the FCs

Real-time adaptive parameter estimation for a polymer electrolyte membrane fuel cell

© 2019 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 worksIn this paper, we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the well-recognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenariosPeer ReviewedPostprint (author's final draft

Modeling and control of fuel cell-battery hybrid energy sources

Environmental, political, and availability concerns regarding fossil fuels in recent decades have garnered substantial research and development in the area of alternative energy systems. Among various alternative energy systems, fuel cells and batteries have attracted significant attention both in academia and industry considering their superior performances and numerous advantages. In this dissertation, the modeling and control of these two electrochemical sources as the main constituents of fuel cell-battery hybrid energy sources are studied with ultimate goals of improving their performance, reducing their development and operational costs and consequently, easing their widespread commercialization. More specifically, Paper I provides a comprehensive background and literature review about Li-ion battery and its Battery Management System (BMS). Furthermore, the development of an experimental BMS design testbench is introduced in this paper. Paper II discusses the design of a novel observer for Li-ion battery State of Charge (SOC) estimation, as one of the most important functionalities of BMSs. Paper III addresses the control-oriented modeling and analysis of open-cathode fuel cells in order to provide a comprehensive system-level understanding of their real-time operation and to establish a basis for control design. Finally, in Paper IV a feedback controller, combined with a novel output-injection observer, is designed and implemented for open-cathode fuel cell temperature control. It is shown that temperature control not only ensures the fuel cell temperature reference is properly maintained, but, along with an uncertainty estimator, can also be used to adaptively stabilize the output voltage --Abstract, page iv

Investigating controller performance in hybrid SOFC systems in the presence of unknown nonlinearities

Solid oxide fuel cells (SOFCs) are energy conversion devices that offer many benefits over various other fuel cell types as a result of high operating temperatures (800-1000 °C). Unfortunately, SOFCs tend to possess poor load following capabilities due to delays along the fuel path and complex system dynamics. Maintaining safe operating conditions during changes in power demand is addressed using a controller designed to regulate the fuel cell current based on fuel flow measurement. In order to compensate for the resulting mismatch between demanded and delivered power, the SOFC system is hybridized with an energy storage device, such as an ultra-capacitor. Prior research at the HySES laboratory at RIT has led to control designs that guarantee robustness to uncertainties in system parameters such power electronics efficiencies. However, existing controllers for this system were developed under assumptions made about the unknown dynamics of the fuel supply system (FSS), such as exponential or bounded tracking. Retaining these controller designs, this thesis develops a general set of closed loop system equations in which the prior assumptions about the FSS are relaxed. The FSS behavior is treated as an unknown nonlinearity. Thereafter, concepts of absolute stability, Lyapunov stability and linear system approximation are used to evaluate the closed-loop system. The analysis leads to analytical conditions relating the controller gains and the local behavior of the FSS, predicting the onset of instability in the closed-loop system. The results are validated using simulations and using a hardware-in-the-loop test stand. Additionally, the problem of transient fuel utilization control of SOFCs is revisited and addressed by using a nonlinear observer design and an auxiliary hydrogen injection strategy. These approaches aim to compensate for fuel path delays and maintain desired operating conditions during transient loading conditions. Findings are validated using desktop simulations