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用于超级电容器模型参数优化的遗传算法

Genetic algorithm for parameter optimization of supercapacitor model.

作者信息

Menezes Filipe, Cunha Sérgio, Assis William, Manito Allan, Leite Reinaldo, Soares Thiago, Lott Hugo

机构信息

Electrical and Biomedical Engineering Faculty, Institute of Thecnology, Federal University of Pará, Belém Pennsylvania, Brazil.

Norte Energia, Pará, Brazil.

出版信息

PLoS One. 2025 Jul 17;20(7):e0325645. doi: 10.1371/journal.pone.0325645. eCollection 2025.

Abstract

Electric energy storage systems have advanced significantly in recent years, driven by the growing expansion of renewable energy sources, the rise of electromobility, and other emerging configurations within the current electrical energy system. Among the various energy storage technologies, supercapacitors have gained considerable attention. Due to their ability to deliver large amounts of power over short periods, supercapacitors can be highly effective in hybrid storage systems, for example, enhancing overall system performance. Therefore, detailed studies on supercapacitors and their electrical circuit models have been developed with the aim of representing them as close as possible to actual physical behavior for numerous applications, such as in the context of Digital Twin (DT), an application that will support the monitoring of the operation and health of the supercapacitor throughout its useful life. The present work aims to estimate optimally some parameters of an electrical circuit model of a supercapacitor, in such a way as to obtain responses with very low errors and, thus, be able to use this computational electrical modeling for the development of a Digital Twin system. For the optimal adjustment of the electrical circuit model parameters, a Genetic Algorithm (GA) is used. The response of the electrical circuit, adjusted by the Genetic Algorithm (GA), is then compared to the response obtained through computer simulation of a supercapacitor using PSIM software, which is a software well validated in such studies. The results demonstrated strong alignment between the response using GA and the response using PSIM. Specifically, the charge and discharge curves of the supercapacitor, obtained through GA adjustment and PSIM simulation, were very similar, showing an error of just 2.2%. Thus, the supercapacitor model adjusted via GA demonstrates a good response to the physical phenomenon in question and can be used to develop a Digital Twin (DT) system, aiding in the operational and health monitoring of the supercapacitor.

摘要

近年来,在可再生能源不断扩张、电动出行兴起以及当前电能系统中其他新兴配置的推动下,电能存储系统取得了显著进展。在各种储能技术中,超级电容器受到了广泛关注。由于超级电容器能够在短时间内提供大量电力,因此在混合储能系统中非常有效,例如可以提高整个系统的性能。因此,针对超级电容器及其电路模型开展了详细研究,目的是在众多应用中尽可能精确地模拟其实际物理行为,比如在数字孪生(DT)的背景下,该应用将支持对超级电容器在其整个使用寿命期间的运行和健康状况进行监测。本研究旨在对超级电容器电路模型的一些参数进行优化估计,以便获得误差极低的响应,从而能够将这种计算电气模型用于数字孪生系统的开发。为了对电路模型参数进行优化调整,使用了遗传算法(GA)。然后将通过遗传算法(GA)调整后的电路响应与使用PSIM软件对超级电容器进行计算机模拟得到的响应进行比较,PSIM软件在这类研究中经过了充分验证。结果表明,使用GA得到的响应与使用PSIM得到的响应高度一致。具体而言,通过GA调整和PSIM模拟得到的超级电容器充放电曲线非常相似,误差仅为2.2%。因此,通过GA调整的超级电容器模型对相关物理现象表现出良好的响应,可用于开发数字孪生(DT)系统,辅助对超级电容器的运行和健康状况进行监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf4/12270144/076e18753829/pone.0325645.g001.jpg

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