Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm
In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the...
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| Published in: | Energy (Oxford) Vol. 90; pp. 1334 - 1341 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.10.2015
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| ISSN: | 0360-5442 |
| Online Access: | Get full text |
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| Abstract | In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance.
•We propose a hybrid adaptive differential evolution algorithm (HADE).•The search efficiency is enhanced in low and high dimension search space.•The effectiveness is confirmed by testing benchmark functions.•The identification of the PEMFC model is conducted by adopting HADE. |
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| AbstractList | In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. •We propose a hybrid adaptive differential evolution algorithm (HADE).•The search efficiency is enhanced in low and high dimension search space.•The effectiveness is confirmed by testing benchmark functions.•The identification of the PEMFC model is conducted by adopting HADE. |
| Author | Bi, Yunrui Wang, Ning Srinivasan, Dipti Sun, Zhe |
| Author_xml | – sequence: 1 givenname: Zhe surname: Sun fullname: Sun, Zhe email: jickrey@163.com, nwang@iipc.zju.edu.cn organization: National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, PR China – sequence: 2 givenname: Ning surname: Wang fullname: Wang, Ning organization: National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, PR China – sequence: 3 givenname: Yunrui surname: Bi fullname: Bi, Yunrui organization: School of Automation, Southeast University, Nanjing 210096, PR China – sequence: 4 givenname: Dipti surname: Srinivasan fullname: Srinivasan, Dipti organization: Department of Electrical Computer Engineering, National University of Singapore, 117576, Singapore |
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| Keywords | Parameter identification Hybrid adaptive DE algorithm Proton exchange membrane fuel cell (PEMFC) |
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| SubjectTerms | algorithms bees foraging fuel cells Hybrid adaptive DE algorithm Parameter identification probability Proton exchange membrane fuel cell (PEMFC) |
| Title | Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm |
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