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Fast and accurate parameter extraction for different types of fuel cells with decomposition and nature-inspired optimization method

机译：通过分解和自然优化方法快速，准确地提取不同类型燃料电池的参数

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摘要

Fast and accurate parameter extraction of fuel cells is crucial to the control and performance analysis of fuel cell power systems. Unfortunately, due to the multivariable and nonlinear features of fuel cell models, it is a difficult task to identify the parameters of the models. In this paper, we propose a decomposition technique, where the unknown parameters are divided into two groups: nonlinear and linear. The optimization techniques only need to optimize the nonlinear parameters, and then the linear parameters are determined based on the nonlinear ones. With the help of the decomposition technique, a generalized framework by using the nature-inspired optimization method is proposed to try to fast and accurately extract the parameters for different types of fuel cells. To test the performances of our approach, two widely used types of fuel cells are studied, i.e., proton exchange membrane fuel cell and solid oxide fuel cell. Extensive simulation tests with thirty-two instances are carried out for comparing our approach with existing approaches. The comparison demonstrates the efficiency of the decomposition technique. Moreover, the results show that our approach can not only significantly reduce the computational resources, but also yields high quality solutions.
机译：快速准确地提取燃料电池参数对于燃料电池动力系统的控制和性能分析至关重要。不幸的是，由于燃料电池模型的多变量和非线性特征，识别模型的参数是一项艰巨的任务。在本文中，我们提出了一种分解技术，将未知参数分为两组：非线性和线性。优化技术只需要优化非线性参数，然后根据非线性参数确定线性参数即可。借助分解技术，提出了一种利用自然启发式优化方法的通用框架，以试图快速，准确地提取不同类型燃料电池的参数。为了测试我们的方法的性能，研究了两种广泛使用的燃料电池，即质子交换膜燃料电池和固体氧化物燃料电池。进行了32个实例的广泛仿真测试，以将我们的方法与现有方法进行比较。比较证明了分解技术的效率。此外，结果表明，我们的方法不仅可以显着减少计算资源，而且可以提供高质量的解决方案。

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