Parameter identification of an SOFC model with an efficient,adaptive differential evolution algorithm |
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Authors: | Wenyin Gong Zhihua Cai Jie Yang Xi Li Li Jian |
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Affiliation: | 1. School of Computer Science, China University of Geosciences, Wuhan 430074, PR China;2. School of Mechanical and Electronic Information, China University of Geosciences, Wuhan 430074, PR China;3. Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan 430074, PR China;4. State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, PR China |
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Abstract: | An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as IJADE (Improved Jingqiao Adaptive DE). To verify the performance of IJADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, IJADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed. |
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Keywords: | Solid oxide fuel cell (SOFC) Parameter identification Electrochemical model Differential evolution algorithms |
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