基于分数杜鹃搜索的柔性超级电容器参数辨识

A. M. Abdelaty, M. Fouda, M. M. Elbarawy, H. Attia, A. Radwan
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引用次数: 0

摘要

本文采用分数阶杜鹃搜索法从时域数据中识别柔性超级电容器的模型参数。时域数据是还原石墨烯基柔性超级电容器在不同剥离-石墨烯介导氧化石墨烯(EGM-rGO)相对含量下的5条恒流充放电(GCD)曲线。这些时域曲线采用了Rs- cpe、Rp-Rs-CPE和Rs- C-CPE三种著名超级电容器模型的参数。对提取参数进行了总结,并讨论了EGM-rGO相对含量的影响。在这些讨论的基础上,针对每一种EGM-rGO相对含量推荐一种模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter Identification of Flexible Supercapacitors with Fractional Cuckoo Search
In this paper, we identify the model parameters of flexible supercapacitors from time-domain data using fractional cuckoo search. The time-domain data are five galvanostatic charging/discharging (GCD) curves of reduced graphene oxide-based flexible supercapacitor at different exfoliated-graphene-mediated graphene oxide (EGM-rGO) relative content. These time-domain curves are used the parameters of three well-known supercapacitor models: Rs-CPE, Rp-Rs-CPE, and Rs- C-CPE. The extracted parameters are summarized, and the effect of EGM-rGO relative content is discussed. Based on these discussions, one model is recommended for each EGM-rGO relative content.
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