yasemin vural centre for computational fluid dynamics (cfd) university of leeds, uk icat 08...
TRANSCRIPT
Yasemin Vural
Centre for Computational Fluid Dynamics (CFD) University of Leeds, UK
ICAT 08 ConferenceNovember 13-14, Istanbul, Turkey
PERFORMANCE PREDICTION OF A PROTON EXCHANGE MEMBRANE FUEL CELL USING THE ANFIS
MODEL
OUTLINE
• Introduction
• Modeling & Results
• Conclusion
Introduction
Modeling & Results
Conclusion
Fuel Cells
• Fuel cells are the electrochemical devices that converts chemical energy into electrical energy
fuel water oxidant heat electricity
• Clean, high efficiency, quite (no moving parts) energy production
• Applications: automotive, stationary power industry, portable applicatons (mobile phones, PCs) • Types: PEMFC, SOFC, DMFC, Alkaline Fuel cells etc.
• Recent Research: material type, manufacturing, understand the processes (through modelling)
-1-
Fuel Cell
Introduction
Modeling & Results
Conclusion
Proton Exchange Membrane Fuel Cell (PEMFC)
-2-(Source: http://www.fueleconomy.gov)
Introduction
Modeling & Results
Conclusion
-3-
Proton Exchange Membrane Fuel Cell (PEMFC)
Operating Temp : 60-80 C
Efficiency : 35-45 (%)
Applications : Automotive, small-scale stationary, portable
Challenges
• Cost
• Lifetime/ Degradation
• Start up (subzero temperature, freezing)
• Water Management
Applications in Automotive Industry
-4-
Toyota FCHC
Volkswagen Bora
Ford Explorer
2008 Honda FCX Clarity
Introduction
Modeling & Results
Conclusion
-5-
Typical Polarization curve of a PEFMC
(Source: Buasri P.and Salameh Z.H.)
Voltage loss due to activation polarization
Voltage loss due to ohmic polarization
Voltage loss due to concentration polarization
Introduction
Modeling & Results
Conclusion
-6-
Proton Exchange Membrane Fuel Cell (PEMFC)
• Performance (I-V curve) prediction of a cell is important for design improvements.
•Measurements in a fuel cell is usually difficult and expensive.
• Modelling is an important tool for performance prediction.
• Mathematical models: complicated, empirical parameters.
• Soft computing models: easier, rapid.
Introduction
Modeling & Results
Conclusion
-7-
Proton Exchange Membrane Fuel Cell (PEMFC)
Purpose of the study:
To predict the performance of a PEM fuel cell using a soft computing technique, namely the ANFIS model and validate the model for different operational conditions.
• Solution using MATLAB software, Fuzzy Logic Toolbox
Introduction
Modeling & Results
Conclusion
-8-
Artificial Neuro Fuzzy Inference System (ANFIS)
• Advantages: No prior knowledge of the system is necessary.
• combines the advantages of the Artificial Neural Network(ANN) and Fuzzy Logic (FL)
The ANFIS structure
Voltage (V)ANFIS
-9-
Current density0 -1.68 A/cm2
Cell temperature 50-90 C
Anode humidification temperature
25 -90 C
Cathode humidification temperature
40 - 90 C
Pressure1.0-3.74 atm
Experimental data of Wang et al J. of Hydrogen Energy, 2002.
-10-
Results
MAPE (%) =1.86
-11-
MAPE (%) =2.06
Results
-12-
Effect of the Operational Conditions on the Cell Performance
Effect of Cell Temperature: V
olta
ge (
V)
Cell Temp (C)Current density (A/cm2)
Anode and cathodehumidification temperature: 70 C
-13-
Effect of Anode Humidification Temperature: V
olta
ge (
V)
Anode humid. temp (C)
Current density (A/cm2)
Cell temp and cathodehumidification temperature: 70 C
Effect of the Operational Conditions on the Cell Performance
-14-
Effect of Cathode Humidification Temperature: V
olta
ge (
V)
Current density (A/cm2)
Cathode humid. temp (C)
Cell temp and anode humidification temperature: 70 C
Effect of the Operational Conditions on the Cell Performance
-15-
Effect of Pressure:
Vol
tage
(V
)
Pressure (atm) Current density (A/cm2)
Cell temp, anode and Cathode humidification temperature: 70 C
Effect of the Operational Conditions on the Cell Performance
Introduction
Modeling & Results
Conclusion
-16-
Conclusion
• Models are important tools for the prediction of a fuel cell performance.
• The ANFIS model trained and tested with the set of experimental data.
• The effects of the operational conditions on the cell performance were discussed.
• ANFIS can be used as a viable tool for the prediction of the cell performance.
Thank you !