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基于改进的火鹰算法的固体氧化物燃料电池最优参数识别

Optimal parameter identification of solid oxide fuel cell using modified fire Hawk algorithm.

作者信息

Khajuria Rahul, Bukya Mahipal, Lamba Ravita, Kumar Rajesh

机构信息

Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India.

Department of Electrical and Electronics Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

出版信息

Sci Rep. 2024 Sep 28;14(1):22469. doi: 10.1038/s41598-024-72541-6.

Abstract

An accurate and efficient approach is required to identify the unknown parameters of solid oxide fuel cell (SOFC) mathematical model for a robust design of any energy system considering SOFC. This research study proposes a modified fire hawk algorithm (MFHA) to determine the values of SOFC model parameters. The performance evaluation of MFHA is tested on two case studies. Firstly, the performance of MFHA is tested on commercially available cylindrical cell developed by Siemens at four temperatures. Results reveal that the least value of sum of squared error (SSE) is 1.04E-05, 2.30E-05, 1.03E-05, and 1.60E-05 at 1073 K, 1173 K, 1213 K, and 1273 K respectively. Results obtained using MFHA have been compared with original fire hawk algorithm (FHA) and other well established and recent algorithms. Secondly, MFHA is implemented for estimating unknown parameters of a 5 kW dynamic tabular stack of 96 cells at various pressures and temperatures. The obtained value of SSE at different temperatures of 873 K, 923 K, 973 K, 1023 K and 1073 K is 1.18E-03, 6.12E-03, 2.21E-02, 5.18E-02, and 6.00E-02, respectively whereas, SSE at different pressures of 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm is 6.05E-02, 6.11E-02, 5.53E-02, 5.11E-02, and 6.64E-02 respectively.

摘要

对于考虑固体氧化物燃料电池(SOFC)的任何能源系统进行稳健设计,都需要一种准确高效的方法来识别SOFC数学模型的未知参数。本研究提出了一种改进的火鹰算法(MFHA)来确定SOFC模型参数的值。在两个案例研究中对MFHA的性能进行了评估。首先,在西门子公司开发的商用圆柱形电池上,于四个温度下测试了MFHA的性能。结果表明,在1073 K、1173 K、1213 K和1273 K时,均方误差(SSE)的最小值分别为1.04E - 05、2.30E - 05、1.03E - 05和1.60E - 05。使用MFHA获得的结果已与原始火鹰算法(FHA)以及其他成熟的和最新的算法进行了比较。其次,将MFHA用于估计一个由96个电池组成的5 kW动态表格堆栈在不同压力和温度下的未知参数。在873 K、923 K、973 K、1023 K和1073 K不同温度下获得的SSE值分别为1.18E - 03、6.12E - 03、2.21E - 02、5.18E - 02和6.00E - 02,而在1 atm、2 atm、3 atm、4 atm和5 atm不同压力下的SSE值分别为6.05E - 02、6.11E - 02、5.53E - 02、5.11E - 02和6.64E - 02。

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