PEMFC Transient Response Characteristics Analysis in Case of Temperature Sensor Failure

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1353
Author(s):  
Jaeyoung Han ◽  
Sangseok Yu ◽  
Jinwon Yun
Keyword(s):  
Fuel Cell ◽  
Temperature Sensor ◽  
Cell Model ◽  
Transient Behavior ◽  
Cell System ◽  
Polymer Electrolyte Fuel Cell ◽  
System Level ◽  
Sensor Fault ◽  
Fuel Cell System ◽  
Free System

In this study, transient responses of a polymer electrolyte fuel cell system were performed to understand the effect of sensor fault signal on the temperature sensor of the stack and the coolant inlet. We designed a system-level fuel cell model including a thermal management system, and a controller to analyze the dynamic behavior of fuel cell system applied with variable sensor fault scenarios such as stuck, offset, and scaling. Under drastic load variations, transient behavior is affected by fault signals of the sensor. Especially, the net power of the faulty system is 45.9 kW. On the other hand, the net power of the fault free system is 46.1 kW. Therefore, the net power of a faulty system is about 0.2 kW lower than that of a fault-free system. This analysis can help in understanding the transient behavior of fuel cell systems at the system level under fault situations and provide a proper failure avoidance control strategy for the fuel cell system.

Implementation of a Fuel Cell System Model Into Building Energy Simulation Software IDA-ICE

2006 ◽  
Vol 4 (4) ◽  
pp. 511-515 ◽  
Author(s):  
Teemu Vesanen ◽  
Krzysztof Klobut ◽  
Jari Shemeikka
Keyword(s):  
Fuel Cell ◽  
Cell Model ◽  
Building Energy ◽  
Cell System ◽  
System Model ◽  
System Level ◽  
Fuel Cell System ◽  
Cell Systems ◽  
Level Model ◽  
System Level Model

Due to constantly increasing electricity consumption, networks are becoming overloaded and unstable. Decentralization of power generation using small-scale local cogeneration plants becomes an interesting option to improve economy and energy reliability of buildings in terms of both electricity and heat. It is expected that stationary applications in buildings will be one of the most important fields for fuel cell systems. In northern countries, like Finland, efficient utilization of heat from fuel cells is feasible. Even though the development of some fuel cell systems has already progressed to a field trial stage, relatively little is known about the interaction of fuel cells with building energy systems during a dynamic operation. This issue could be addressed using simulation techniques, but there has been a lack of adequate simulation models. International cooperation under IEA/ECBCS/Annex 42 aims at filling this gap, and the study presented in this paper is part of this effort. Our objective was to provide the means for studying the interaction between a building and a fuel cell system by incorporating a realistic fuel cell model into a building energy simulation. A two-part model for a solid-oxide fuel cell system has been developed. One part is a simplified model of the fuel cell itself. The other part is a system level model, in which a control volume boundary is assumed around a fuel cell power module and the interior of it is regarded as a “black box.” The system level model has been developed based on a specification defined within Annex 42. The cell model (programed in a spreadsheet) provides a link between inputs and outputs of the black box in the system model. This approach allows easy modifications whenever needed. The system level model has been incorporated into the building simulation tool IDA-ICE (Indoor Climate and Energy) using the neutral model format language. The first phase of model implementation has been completed. In the next phase, model validation will continue. The final goal is to create a comprehensive but flexible model, which could serve as a reliable tool to simulate the operation of different fuel cell systems in different buildings.

Integrated Dynamic Simulation Model With Supervisory Control Strategy for a PEM Fuel Cell Hybrid Vehicle

2004 ◽  
Author(s):  
Chan-Chiao Lin ◽  
Huei Peng ◽  
Min Joong Kim ◽  
Jessy W. Grizzle
Keyword(s):  
Fuel Cell ◽  
Control Strategy ◽  
Cell Model ◽  
Cell System ◽  
System Level ◽  
Fuel Cell Vehicle ◽  
Strategy Development ◽  
Stochastic Dynamic ◽  
Fuel Cell System ◽  
Level Control

System-level modeling and control strategy development for a hybrid fuel cell vehicle (HFCV) are presented in this paper. A reduced-order fuel cell model is created to accurately predict the fuel cell system efficiency while retaining dynamic effects of important reactant variables. The fuel cell system model is then integrated with a DC/DC converter, a Li-Ion battery, an electric drive and tire/vehicle dynamics to form a HFCV. The supervisory-level control problem of the HFCV is subsequently investigated. A stochastic dynamic programming (SDP) based approach is applied to obtain an optimal power management strategy. Simulations over different driving cycles showed that the SDP control strategy not only saved a significant amount of hydrogen but also produced smoother load for the fuel cell stack—both of which help the long term viability of the fuel cell technology for automotive applications.

Control Strategy Optimization of a Fuel-Cell Electric Vehicle

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Vanessa Paladini ◽  
Teresa Donateo ◽  
Arturo de Risi ◽  
Domenico Laforgia
Keyword(s):  
Fuel Cell ◽  
Control Strategy ◽  
Cell Model ◽  
Control Strategies ◽  
Storage System ◽  
Model Development ◽  
Cell System ◽  
Polymer Electrolyte Fuel Cell ◽  
Solid Polymer ◽  
Maximum Efficiency

In the last decades, due to emission reduction policies, research focused on alternative powertrains among which electric vehicles powered by fuel cells are becoming an attractive solution. The main issues of these vehicles are the energy management system and the overall fuel economy. An overview of the existing solutions with respect to their overall efficiency is reported in the paper. On the bases of the literature results, the more efficient powertrain scheme has been selected. The present investigation aims at identifying the best control strategy to power a vehicle with both fuel cell and battery to reduce fuel consumption. The optimization of the control strategy is achieved by using a genetic algorithm. To model the powertrain behavior, an on purpose made simulation program has been developed and implemented in MATLAB/SIMULINK. In particular, the fuel cell model is based on the theory of Amphlett et al. (1995, “Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell. II. Empirical Model Development,” J. Electrochem. Soc., 142(1)) whereas the battery model also accounts for the charge/discharge efficiency. The analyzed powertrain is equipped with an energy recovery system. During acceleration, power is demanded to the storage system, while during deceleration the battery is recharged. All the tested control strategies assume charge sustaining operation for the battery and that the fuel cell system has to work around its maximum efficiency. All the tested strategies have been validated on four driving cycles.

High Efficiency Operation Conditions in Polymer Electrolyte Fuel Cell System with Hydrogen Circulation

2020 ◽  
Vol MA2020-02 (34) ◽  
pp. 2211-2211
Author(s):  
Yulei Ma ◽  
Kazuhiro Yamaguchi ◽  
Miho Kageyama ◽  
Motoaki Kawase
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
High Efficiency ◽  
Cell System ◽  
Polymer Electrolyte Fuel Cell ◽  
Fuel Cell System ◽  
Operation Conditions

Gain-Scheduled Control for an Automotive Traction PEM Fuel Cell System

2007 ◽  
Author(s):  
Ahmed A. Al-Durra ◽  
Stephen Yurkovich ◽  
Yann Guezennec
Keyword(s):  
Fuel Cell ◽  
Nonlinear Model ◽  
Internal Combustion Engines ◽  
Air Flow ◽  
Transient Behavior ◽  
Cell System ◽  
Fuel Cell System ◽  
Cell Systems ◽  
Hydrogen Flow ◽  
Gain Scheduled

To be practical in automotive traction applications, fuel cell systems must provide power output levels of performance that rival that of typical internal combustion engines. In so doing, transient behavior is one of the keys for success of fuel cell systems in vehicles. From a model-based control perspective, regulation of the fuel cell system through transients is critical, where the response of a fuel cell system depends on the air and hydrogen (flow and pressure regulation) and heat and water management. The focus of this paper is on the air/fuel supply subsystem in tracking an optimum variable pressurization and air flow for maximum system efficiency during load transients. The control-oriented model developed for this study considers electrochemistry, thermodynamics, and fluid flow principles for a 13-state, nonlinear model of a pressurized fuel cell system. For control purposes, a model reduction is performed by converting some of the model dynamics to simple algebraic relationships. A single reference input, the power demanded by the user, is utilized to produce a corresponding reference air flow and back-pressure valve opening, after passing through a static calculation and a tabulated map. Because of the complexity of the full nonlinear model (used in simulation as the truth model), where several maps are used rather than functional forms, two different control techniques are examined separately, each using a feedforward component. The first technique uses an observer-based linear optimum control which combines a feed-forward approach based on the steady state plant inverse response, coupled to a multi-variable LQR feedback control. An extension of that approach, for control in the full nonlinear range of operation, leads to the second technique, nonlinear gain-scheduled control.

A Dynamic Model of an Atmospheric Solid Oxide Fuel Cell System for Stationary Power Generation

2006 ◽  
Author(s):  
Miriam Kemm ◽  
Azra Selimovic ◽  
Mohsen Assadi
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Dynamic Model ◽  
Transient Behavior ◽  
Cell System ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Fuel Cell System ◽  
System Components ◽  
Solid Part

This paper focuses on the transient behavior of a solid oxide fuel cell system used for stationary power production. Dynamic modelling is applied to identify the characteristic time scales of the system components when introducing a disturbance in operational parameters of the system. The information on the response of the system may be used to specify the control loops needed to manage the changes with respect to safe component operation. The commercial process modelling tool gPROMS is used to perform the system simulations. The component library of the tool is completed with dynamic models of a fuel cell stack and a prereformer. The other components are modelled for steady state operation. For the fuel cell a detailed dynamic model is obtained by writing the constitutive laws for heat transfer in the solid part of the cell and conservation of heat and mass in the air and fuel channels. Comprehensive representation of resistive cell losses, reaction kinetics for the reforming and heat conduction through the solid part of the cell is also included in the model. The prereformer is described as a dynamic pseudo-homogeneous one-dimensional tubular reactor accounting for methane steam reforming and water-gas shift reaction. The differences in the transient behavior of the system components and their interaction are investigated under load changes and feed disturbances.

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