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通过改进的秃鹫优化器设计为质子交换膜燃料电池设计一种新的最优控制器。

Designing a new optimal controller for a PEMFC by an improved design of the Coot Optimizer.

作者信息

Wang Zheng, Rezaie Mehrdad, Fathi Gholamreza

机构信息

Chongqing Chemical Industry Vocational College, Chongqing, 401220, China.

Department of Electrical Engineering, Malayer University, Malayer, Iran.

出版信息

Sci Rep. 2025 May 14;15(1):16682. doi: 10.1038/s41598-025-01637-4.

Abstract

This research introduces a novel optimal control strategy for Proton Exchange Membrane Fuel Cells (PEMFCs) utilizing a DC/DC converter, aimed at enhancing performance and longevity. The core of this strategy is an Improved Coot Optimizer algorithm (ICOA), designed to optimize a PID controller for precise voltage regulation of the PEMFC stack. The ICOA incorporates self-adaptive and chaotic mechanisms to improve solution quality and prevent premature convergence. Simulation results demonstrate that the proposed ICOA-optimized PID controller significantly reduces voltage ripples and overshoot, key factors in improving PEMFC lifetime. Specifically, compared to non-optimized performance with a 2.47% overshoot and 4.7 s settling time, the ICOA-optimized system exhibits superior dynamic response and stability. Comparative analyses against three other control techniques confirm a wide system enhancement, evidenced by a substantial reduction in both current and overshoot ripples. Algorithm verification using benchmark functions shows the ICOA achieves lower mean values and standard deviations, with p-values indicating statistically significant improvements (p < 0.05) in Root Mean Square Error (RMSE) compared to COA, MVO, EPO, and LOA algorithms, validating its enhanced optimization capabilities for PEMFC control.

摘要

本研究介绍了一种利用DC/DC转换器的质子交换膜燃料电池(PEMFC)新型最优控制策略,旨在提高性能和延长使用寿命。该策略的核心是一种改进的黑鸭优化算法(ICOA),旨在优化PID控制器,以精确调节PEMFC电池堆的电压。ICOA纳入了自适应和混沌机制,以提高解的质量并防止过早收敛。仿真结果表明,所提出的ICOA优化PID控制器显著降低了电压纹波和超调量,这是提高PEMFC寿命的关键因素。具体而言,与具有2.47%超调量和4.7秒稳定时间的未优化性能相比,ICOA优化系统表现出卓越的动态响应和稳定性。与其他三种控制技术的对比分析证实了系统有广泛的提升,电流纹波和超调量均大幅降低就是证明。使用基准函数进行的算法验证表明,ICOA实现了更低的均值和标准差,p值表明与黑鸭算法(COA)、蛾火优化算法(MVO)、帝王蝶优化算法(EPO)和狮子优化算法(LOA)相比,均方根误差(RMSE)有统计学上的显著改善(p < 0.05),验证了其在PEMFC控制方面增强的优化能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5018/12078516/8592ebf03c87/41598_2025_1637_Fig1_HTML.jpg

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