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.

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.

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.

scholarly journals Determination of the optimal battery capacity of a PEM fuel cell vehicle taking into account recuperation and supercapacitors

2021 ◽  
Author(s):  
Swantje C. Konradt ◽  
Hermann Rottengruber
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Cell Model ◽  
Storage System ◽  
Test Procedure ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Proton Exchange ◽  
Fuel Cell Vehicle ◽  
Battery Capacity

AbstractProton exchange membrane (PEM) fuel cell vehicles require an electrical intermediate storage system to compensate for dynamic load requirements. That storage system uses a battery and has the task to increase tolerance to dynamic operation. In addition, energy can be recuperated and stored in supercapacitors to increase the fuel cell vehicle’s efficiency. To determine the optimal battery capacity according to the recuperation potential and possible use of a supercapacitor, a reference vehicle with PEM fuel cell was transferred to the simulation environment Matlab/Simulink. The model is based on a cell model describing the electrochemical and physical interactions within the cell. It has been implemented in a complete vehicle model for the representation of a fuel cell vehicle. Various legal driving cycles, such as the WLTP (“Worldwide harmonized Light Vehicles Test Procedure”), were used for the calculations. A further step sets the optimal battery capacity depending on the dynamic of the fuel cell system. With this simulation model, dynamic requirements—for the fuel cell and the associated system components—can be determined in the future, scalable for each vehicle depending on the battery capacity and recuperation potential.

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.

The PEM Fuel Cell System for Autonomous Underwater Vehicles

2005 ◽  
Vol 39 (3) ◽  
pp. 56-64 ◽  
Author(s):  
Satoshi Tsukioka ◽  
Taro Aoki ◽  
Ikuo Yamamoto ◽  
Hiroshi Yoshida ◽  
Tadahiro Hyakudome ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Metal Hydride ◽  
Autonomous Underwater Vehicle ◽  
Earth Science ◽  
Autonomous Underwater Vehicles ◽  
Storage System ◽  
Cell System ◽  
Hydrogen Gas ◽  
Fuel Cell System ◽  
Control Electronics

An ocean-going autonomous underwater vehicle powered by a polymer electrode membrane fuel cell system was completed by The Japan Agency for Marine-Earth Science and Technology. The fuel cell system generates 4kW of electric power for the control electronics and propulsion system. Hydrogen gas is stored under low pressure in the metal hydride. Heat generated by the fuel cell is used to discharge hydrogen gas into the metal hydride. This paper presents the test results of the fuel cell, storage system and the 317km sea test.

Thermodynamic and dynamic analysis off a wind-powered off-grid industrial building integrated with solid oxide fuel cell and electrolyzer for energy management and storage

2021 ◽  
pp. 1-30
Author(s):  
Pegah Mottaghizadeh ◽  
Mahshid Fardadi ◽  
Faryar Jabbari ◽  
Jacob Brouwer
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Wind Farm ◽  
Control Strategies ◽  
Storage System ◽  
Energy Storage System ◽  
Solid Oxide ◽  
System Level ◽  
Industrial Building ◽  
Oxide Fuel

Abstract In this study, an islanded microgrid system is proposed that integrates identical stacks of solid oxide fuel cell and electrolyzer to achieve a thermally self-sustained energy storage system. Thermal management of the SOEC is achieved by use of heat from the SOFC with a heat exchanger network and control strategies. While the SOFC meets the building electricity demand and heat from its electrochemical reactions is transferred to the SOEC for endothermic heat and standby demands. Each component is physically modelled in Simulink and ultimately integrated at the system level for dynamic analyses. The current work simulates a system comprised of a wind farm in Palm Springs, CA coupled with the SOEC (for H2 generation), and an industrial building powered by the SOFC. Results from two-weeks of operation using measured building and wind data showed that despite fluctuating power profiles, average temperature and local temperature gradients of both the SOEC and SOFC were within desired tolerances. However, for severe conditions of wind power deficit, H2 had to be supplied from previous windy days' storage or imported.

Component Power Sizing and Limits of Operation for Proton Exchange Membrane (PEM) Fuel Cell/Battery Hybrid Automotive Applications

2001 ◽  
Author(s):  
Daisie D. Boettner ◽  
Gino Paganelli ◽  
Yann G. Guezennec ◽  
Giorgio Rizzoni ◽  
Michael J. Moran
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Control Strategies ◽  
Cell System ◽  
Proton Exchange ◽  
System Model ◽  
Automotive Applications ◽  
Fuel Cell System ◽  
Vehicle Performance ◽  
Exchange Membrane

Abstract This paper describes use of a Proton Exchange Membrane (PEM) fuel cell system model for automotive applications in a fuel cell system/battery hybrid configuration. The fuel cell system model has been integrated into a vehicle performance simulator that determines fuel economy and allows consideration of control strategies. The simulator is used to explore relevant regions of the fuel cell-powered hybrid electric vehicle design space by conducting simulations using two simple supervisory-control strategies: thermostatic control and proportional control. During the simulations power provided by the battery and fuel cell system and operational limits on battery state of charge and fuel cell system current density are varied while maintaining minimum component sizing to meet vehicle performance criteria. Analysis of results from these simulations provides component power sizing and limits of operation suitable for development of a more advanced supervisory vehicle control strategy for a fuel cell vehicle.

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