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Effective Parameter Extraction of Different Polymer Electrolyte Membrane Fuel Cell Stack Models Using a Modified Artificial Ecosystem Optimization Algorithm

机译：不同聚合物电解质膜燃料电池堆模型的有效参数提取使用改进的人工生态系统优化算法

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Recently, extracting the precise values of unknown parameters of the polymer electrolyte membrane fuel cell (PEMFC) is considered one of the most widely nonlinear and semi-empirical optimization problems. This paper proposes and applies a Modified Artificial Ecosystem Optimization (MAEO) algorithm to solve the problem of PEMFC parameters extraction. The conventional AEO is a novel optimization technique that is inspired by the energy flow in a natural ecosystem which is defined as abiotic, which includes non-living bodies and elements such as light, water and air. The proposed optimization algorithm, MAEO, is used to enhance the performance of conventional AEO and provide faster convergence rate as well as to be far away from falling into the local optima. In the proposed MAEO, an operator is suggested to improve the balance between exploitation and Exploration phases. The accurate estimation of PEMFC unknown parameters leads to develop a precise mathematical model which simulates the electrochemical and electrical characteristics of PEMFC. The objective function of the studied optimization problem is formulated as the sum of squared errors (SSE) between the measured and simulated stack voltages. To prove the reliability and capability of the proposed MAEO algorithm in solving this problem compared with other recent algorithms, it is tested on four different PEMFC stack models, namely, BCS-500W, SR-12 500W, 250W and Temasek 1 kW stacks. Moreover, statistical measures are performed to assess the superiority and robustness of the proposed algorithm. In addition, the accuracy of optimized parameters is assessed through the dynamic characteristics of PEMFCs under varying the reactants & x2019; pressures and temperature of the cell. However, the simulation results confirm that the proposed MAEO algorithm has high accuracy and reliability in extracting the PEMFC optimal parameters compared with the conventional AEO and other effective algorithms.
机译：最近，提取聚合物电解质膜燃料电池（PEMFC）的未知参数的精确值被认为是最广泛的非线性和半经验优化问题之一。本文提出并应用了修改的人工生态系统优化（MAEO）算法来解决PEMFC参数提取问题。传统的AEO是一种新颖的优化技术，其受到自然生态系统中的能量流动的启发，该能量流动被定义为非生物，其包括非生物体和诸如光，水和空气的元素。所提出的优化算法Maeo用于增强常规AEO的性能，并提供更快的收敛速度以及远离落入本地最佳的速度。在拟议的Maeo中，建议经营者提高剥削与勘探阶段之间的平衡。 PEMFC未知参数的准确估计导致开发精确的数学模型，用于模拟PEMFC的电化学和电气特性。研究的优化问题的目标函数作为测量和模拟堆叠电压之间的平方误差（SSE）的总和。为了证明所提出的MAEO算法在解决此问题的情况下，与其他最近的算法相比，它在四种不同的PEMFC堆栈模型中测试了它，即BCS-500W，SR-12 500W，250W和TemaseK 1 KW堆叠。此外，进行统​​计措施以评估所提出的算法的优越性和鲁棒性。此外，通过改变反应物和X2019的PEMFC的动态特性来评估优化参数的准确性;细胞的压力和温度。然而，仿真结果证实，与传统的AEO和其他有效算法相比，提出的MAEO算法在提取PEMFC最佳参数方面具有高精度和可靠性。

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