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基于自适应神经模糊推理系统（ANFIS）的最大功率点跟踪（MPPT）算法实现质子交换膜燃料电池的最优发电

Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm.

作者信息

S Devakirubakaran, C Bharatiraja, T Narasimha Prasad, B Praveen Kumar, S Shitharth

机构信息

Center for Smart Energy Systems, Chennai Institute of Technology, Chennai, Tamilnadu, India.

Center for Electric Mobility, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Chengalpattu, Tamil Nadu, India.

出版信息

Sci Rep. 2024 Nov 2;14(1):26455. doi: 10.1038/s41598-024-77696-w.

DOI:10.1038/s41598-024-77696-w
PMID:39488651
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11531494/
Abstract

Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10-15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

摘要

燃料电池是满足未来能源需求最具前景的能源。汽车行业正在考虑将燃料电池与电动汽车（EV）集成。这种集成带来了许多挑战，如动态运行行为。为了以最高效率运行燃料电池，本文提出了一种基于自适应神经模糊推理系统（ANFIS）的最大功率点跟踪（MPPT）方法。氢气流速、压力和电池组温度是用于跟踪燃料电池最大功率点的参数。ANFIS-MPPT算法已在MATLAB/Simulink中与1.26千瓦燃料电池集成，并在不同场景下进行了验证，如氢气压力动态变化、电池组温度变化、负载变化。观察了该算法的性能，并与传统的扰动观察（P&O）算法和增量电导（InC）算法的MPPT算法进行了比较。所提出的ANFIS-MPPT算法比P&O和InC方法提高了10%-15%的功率稳定性。此外，与P&O算法相比，所提出的ANFIS-MPPT响应速度快30%，与InC算法相比快23%。通过分析发现，ANFIS、P&O和InC方法的响应时间分别为2.5秒、3.6秒和4.5秒。此外，ANFIS方法的最大功率输出为1.26千瓦，而P&O和InC分别为1.13千瓦、1.19千瓦。本文给出了详细的仿真分析和结果。

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本文引用的文献

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Sci Rep. 2024 Feb 9;14(1):3342. doi: 10.1038/s41598-024-53763-0.
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Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control.基于模糊控制的混合人工蜂群算法的质子交换膜燃料电池最大功率点跟踪
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Proton Exchange Membrane Fuel Cells (PEMFCs): Advances and Challenges.
质子交换膜燃料电池（PEMFCs）：进展与挑战
Polymers (Basel). 2021 Sep 10;13(18):3064. doi: 10.3390/polym13183064.
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Composites of Platinum-Iridium Alloy Nanoparticles and Graphene Oxide for the Dimethyl Amine Borane (DMAB) dehydrogenation at ambient conditions: An Experimental and Density Functional Theory Study.用于环境条件下二甲胺硼烷（DMAB）脱氢的铂铱合金纳米颗粒与氧化石墨烯复合材料：实验与密度泛函理论研究
Sci Rep. 2019 Oct 29;9(1):15543. doi: 10.1038/s41598-019-52038-3.