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USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM

Year 2013, Volume: 5 Issue: 3, 1 - 10, 01.09.2013

Alireza Askarzadeh

Abstract

For delivering the maximum power output at the load’s operating voltage, the fuel cell-based stand alone power supply system has to be configured in terms of number of cells in series, number of cells in parallel, and cell’s surface area. In this paper, in order to optimize a stand alone power supply system of proton exchange membrane fuel cell (PEMFC), an artificial immune system (AIS) based on the clonal selection algorithm is proposed. For this aim, a mathematical model for the PEMFC stack is introduced, then, based on this model an AIS code is developed in the Matlab environment. The results manifest that the AIS is a reliable technique for finding the optimal configuration of the fuel cell stack

Keywords

Fuel cell-based stand alone power supply system Stack configuration Artificial immune system

References

  • Mo ZJ, Zhu XJ, Wei LY, Cao GY. Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research 2006; 30: 585-597.
  • Mohamed I, Jenkins N. Proton exchange membrane (PEM) fuel cell stack configuration using genetic algorithms. Journal of Power Sources 2004; 131: 142-146.
  • Bao C, Ouyang M, Yi BL. Modeling and optimization of the air system in polymer exchange membrane fuel cell systems. Journal of Power Sources 2006; 156: 232-243.
  • Jurado F, Valverde M. Enhancing the electrical performance of a solid oxide fuel cell using multiobjective genetic algorithms. Renewable Energy 2005; 30: 881-902.
  • Sun XJ, Cao GY, Zhu XJ. A novel genetic algorithm and its application in fuzzy variable structure control of fuel cell. Journal of Intelligent and Robotic Systems 2001; 31: 299-316.
  • Zhu C, Zhao B, Ye B, Cao Y. An improved immune algorithm and its evaluation of optimization efficiency. Lecture Notes in Computer Science, Springer 2005; 3611: 895-904.
  • Larmini F, Dicks A. Fuel Cell Systems Explained. John Wiley & Sons. Ltd 2000.
  • Bernardi DM, Verbrugge MW. A mathematical-model of the solid- polymer-electrolyte fuelcell. Journal of the Electrochemical Society 1992; 139: 2477–2491.
  • Springer TE, Zawodzinski TA, Gottesfeld S. Polymer electrolyte fuel-cell model. Journal of the Electrochemical Society 1991; 138: 2334–2342.
  • Fuller TF, Newman J. Water and thermal management in solid-polymer-electrolyte fuel-cells. Journal of the Electrochemical Society 1993; 140: 1218–1225.
  • Nguyen TV, White RE. A water and heat management model for proton-exchange-membrane fuel-cells. Journal of the Electrochemical Society 1993; 140: 2178–2186.
  • Mann RF, Amphlett JC, Hooper MAI, Jensen HM, Peppley BA, Roberge PR. Development and application of a generalised steady-state electrochemical model for a PEM fuel cell. Journal of Power Sources 2000; 86: 173-180.
  • Rowe A, Li X. Mathematical modeling of proton exchange membrane fuel cells. Journal of Power Sources 2001; 102: 82-96.
  • Yan Q, Toghiani H, Causey H. Steady state and dynamic performance of proton exchange membrane fuel cells (PEMFCs) under various operating conditions and load changes. Journal of Power Sources 2006; 161: 492-502.
  • Kim J, Lees M, Srinivasan S. Modeling of proton exchange membrane fuel cell performance with an empirical equation. Journal of the Electrochemical Society 1995; 142: 2670–2674.
  • Jerne NK. Towards a network theory of the immune system. Ann Inst Pasteur/Immunol paris 1974; 125c: 373-398.
  • Jerne NK. Idiotypic networks and other preconceived ideas. Immunological Reviews 1984; 79: 5-24.
  • Chun JS, Kim MK, Jung HK. Shape optimization of electromagnetic devices using immune algorithm. IEEE Transactions on Magnetics 1997; 33: 1876–1879.
  • Kalinli A, Karabogab N. Artificial immune algorithm for IIR filter design. Engineering Applications of Artificial Intelligence 2005; 18: 919–929.
  • Wen X, Song A. An immune evolutionary algorithm for sphericity error evaluation. International Journal of Machine Tools and Manufacture. 2004; 44: 1077–1084.
  • Burnet FM. The clonal selection theory of acquired immunity. Vanderbilt University Press 1959.
  • De Castro LN, Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London: 2002.
  • Lau HYK, Tsang WWP. A parallel immune optimization algorithm for numeric function optimization. Evolutionary Intelligence 2008; 1: 171-185.

Year 2013, Volume: 5 Issue: 3, 1 - 10, 01.09.2013

Alireza Askarzadeh

Abstract

References

  • Mo ZJ, Zhu XJ, Wei LY, Cao GY. Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research 2006; 30: 585-597.
  • Mohamed I, Jenkins N. Proton exchange membrane (PEM) fuel cell stack configuration using genetic algorithms. Journal of Power Sources 2004; 131: 142-146.
  • Bao C, Ouyang M, Yi BL. Modeling and optimization of the air system in polymer exchange membrane fuel cell systems. Journal of Power Sources 2006; 156: 232-243.
  • Jurado F, Valverde M. Enhancing the electrical performance of a solid oxide fuel cell using multiobjective genetic algorithms. Renewable Energy 2005; 30: 881-902.
  • Sun XJ, Cao GY, Zhu XJ. A novel genetic algorithm and its application in fuzzy variable structure control of fuel cell. Journal of Intelligent and Robotic Systems 2001; 31: 299-316.
  • Zhu C, Zhao B, Ye B, Cao Y. An improved immune algorithm and its evaluation of optimization efficiency. Lecture Notes in Computer Science, Springer 2005; 3611: 895-904.
  • Larmini F, Dicks A. Fuel Cell Systems Explained. John Wiley & Sons. Ltd 2000.
  • Bernardi DM, Verbrugge MW. A mathematical-model of the solid- polymer-electrolyte fuelcell. Journal of the Electrochemical Society 1992; 139: 2477–2491.
  • Springer TE, Zawodzinski TA, Gottesfeld S. Polymer electrolyte fuel-cell model. Journal of the Electrochemical Society 1991; 138: 2334–2342.
  • Fuller TF, Newman J. Water and thermal management in solid-polymer-electrolyte fuel-cells. Journal of the Electrochemical Society 1993; 140: 1218–1225.
  • Nguyen TV, White RE. A water and heat management model for proton-exchange-membrane fuel-cells. Journal of the Electrochemical Society 1993; 140: 2178–2186.
  • Mann RF, Amphlett JC, Hooper MAI, Jensen HM, Peppley BA, Roberge PR. Development and application of a generalised steady-state electrochemical model for a PEM fuel cell. Journal of Power Sources 2000; 86: 173-180.
  • Rowe A, Li X. Mathematical modeling of proton exchange membrane fuel cells. Journal of Power Sources 2001; 102: 82-96.
  • Yan Q, Toghiani H, Causey H. Steady state and dynamic performance of proton exchange membrane fuel cells (PEMFCs) under various operating conditions and load changes. Journal of Power Sources 2006; 161: 492-502.
  • Kim J, Lees M, Srinivasan S. Modeling of proton exchange membrane fuel cell performance with an empirical equation. Journal of the Electrochemical Society 1995; 142: 2670–2674.
  • Jerne NK. Towards a network theory of the immune system. Ann Inst Pasteur/Immunol paris 1974; 125c: 373-398.
  • Jerne NK. Idiotypic networks and other preconceived ideas. Immunological Reviews 1984; 79: 5-24.
  • Chun JS, Kim MK, Jung HK. Shape optimization of electromagnetic devices using immune algorithm. IEEE Transactions on Magnetics 1997; 33: 1876–1879.
  • Kalinli A, Karabogab N. Artificial immune algorithm for IIR filter design. Engineering Applications of Artificial Intelligence 2005; 18: 919–929.
  • Wen X, Song A. An immune evolutionary algorithm for sphericity error evaluation. International Journal of Machine Tools and Manufacture. 2004; 44: 1077–1084.
  • Burnet FM. The clonal selection theory of acquired immunity. Vanderbilt University Press 1959.
  • De Castro LN, Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London: 2002.
  • Lau HYK, Tsang WWP. A parallel immune optimization algorithm for numeric function optimization. Evolutionary Intelligence 2008; 1: 171-185.
There are 23 citations in total.

Details

Other ID JA66BP57UY
Journal Section Articles
Authors

Alireza Askarzadeh This is me

Publication Date September 1, 2013
Published in Issue Year 2013 Volume: 5 Issue: 3

Cite

APA Askarzadeh, A. (2013). USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM. International Journal of Engineering and Applied Sciences, 5(3), 1-10.
AMA Askarzadeh A. USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM. IJEAS. September 2013;5(3):1-10.
Chicago Askarzadeh, Alireza. “USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM”. International Journal of Engineering and Applied Sciences 5, no. 3 (September 2013): 1-10.
EndNote Askarzadeh A (September 1, 2013) USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM. International Journal of Engineering and Applied Sciences 5 3 1–10.
IEEE A. Askarzadeh, “USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM”, IJEAS, vol. 5, no. 3, pp. 1–10, 2013.
ISNAD Askarzadeh, Alireza. “USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM”. International Journal of Engineering and Applied Sciences 5/3 (September 2013), 1-10.
JAMA Askarzadeh A. USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM. IJEAS. 2013;5:1–10.
MLA Askarzadeh, Alireza. “USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM”. International Journal of Engineering and Applied Sciences, vol. 5, no. 3, 2013, pp. 1-10.
Vancouver Askarzadeh A. USING ARTIFICIAL IMMUNE SYSTEM FOR OPTIMAL CONFIGURATION OF FUEL CELL-BASED STAND ALONE POWER SUPPLY SYSTEM. IJEAS. 2013;5(3):1-10.

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