Yasemin Vural Centre for Computational Fluid Dynamics (CFD) University of Leeds, UK ICAT 08 Conference November 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:

Introduction Modeling & Results Conclusion -3- Proton Exchange Membrane Fuel Cell (PEMFC) Operating Temp : C Efficiency : (%) 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 density A/cm2 Cell temperature C Anode humidification temperature C Cathode humidification temperature C Pressure 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: Voltage (V) Cell Temp (C) Current density (A/cm2) Anode and cathode humidification temperature: 70 C

-13- Effect of Anode Humidification Temperature: Voltage (V) Anode humid. temp (C) Current density (A/cm2) Cell temp and cathode humidification temperature: 70 C Effect of the Operational Conditions on the Cell Performance

-14- Effect of Cathode Humidification Temperature: Voltage (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: Voltage (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.

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