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Parameter identification of solid oxide fuel cells with ranking teaching-learning based algorithm

机译：基于分级教学法的固体氧化物燃料电池参数识别

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摘要

The performance of a solid oxide fuel cell (SOFC) is tightly related to relevant parameters associated with the internal multi-physicochemical processes. Accurate identification of these parameters is considerably important for modelling the voltage versus current (V-I) characteristic of SOFCs. In this paper, an improved teaching-learning based algorithm (TLBO) referred to as RTLBO is proposed to identify the exact values for these parameters. The parameter identification of SOFCs is transformed into a minimization optimization problem. The mean square error (MSE) between the measured output voltage and the calculated output voltage is used as the objective function. TLBO has been shown to be competitive with other population-based algorithms. However, its convergence rate is relatively slow especially for complex optimization problems. Inspired by the ranking mechanism in the actual scenarios of teaching-learning process, a ranking based learner selection method is proposed and integrated into both the teacher and learner phases of RTLBO. In RTLBO, poor learners are more likely to be eliminated from the current class in the ranking based teacher phase and good learners are more likely to be chosen to interact with others in the ranking based learner phase, which hence can improve the overall performance of the class quickly. The experimental results on a 5-kW SOFC stack comprehensively demonstrate that RTLBO is able to achieve a better trade-off between the exploration and exploitation compared with twelve advanced TLBO variants and eight popular advanced non-TLBO based methods. In addition, the sensitivity of RTLBO to variations of population size is empirically investigated.
机译：固体氧化物燃料电池（SOFC）的性能与与内部多物理化学过程相关的相关参数紧密相关。对SOFC的电压与电流（V-I）特性建模时，准确识别这些参数非常重要。在本文中，提出了一种改进的基于教学的算法（TLBO），称为RTLBO，用于识别这些参数的准确值。 SOFC的参数标识转化为最小化优化问题。测得的输出电压和计算出的输出电压之间的均方误差（MSE）用作目标函数。事实证明，TLBO与其他基于人群的算法相比具有竞争力。但是，其收敛速度相对较慢，尤其是对于复杂的优化问题。在教学过程中，根据排名机制的启发，提出了一种基于排名的学习者选择方法，并将其集成到RTLBO的教师和学习者阶段。在RTLBO中，在基于排名的老师阶段，较差的学习者更有可能从当前班级中被淘汰，而在基于排名的学习者阶段，则更有可能选择优秀的学习者与其他人互动，因此可以改善教师的整体表现。上课很快。在5kW SOFC烟囱上的实验结果全面证明，与十二种高级TLBO变体和八种流行的基于非TLBO的先进方法相比，RTLBO能够在勘探和开发之间实现更好的权衡。此外，还通过经验研究了RTLBO对人口规模变化的敏感性。

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  • 来源
    《Energy Conversion & Management》 |2018年第10期|126-137|共12页
  • 作者

    Xiong Guojiang; Zhang Jing; Shi Dongyuan; He Yu;

  • 作者单位

    Guizhou Univ, Coll Elect Engn, Guizhou Key Lab Intelligent Technol Power Syst, Guiyang 550025, Guizhou, Peoples R China;

    Guizhou Univ, Coll Elect Engn, Guizhou Key Lab Intelligent Technol Power Syst, Guiyang 550025, Guizhou, Peoples R China;

    Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China;

    Guizhou Univ, Coll Elect Engn, Guizhou Key Lab Intelligent Technol Power Syst, Guiyang 550025, Guizhou, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Solid oxide fuel cell; Parameter identification; Teaching-learning based algorithm; Ranking mechanism;

    机译：固体氧化物燃料电池;参数辨识;基于教学算法;排序机制;

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