An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications
Yükleniyor...
Dosyalar
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Modeling of batteries in photovoltaic systems has been a major issue related to the random dynamic regime imposed by the changes of solar irradiation and ambient temperature added to the complexity of battery electrochemical and electrical behaviors. However, various approaches have been proposed to model the battery behavior by predicting from detailed electrochemical, electrical or analytical models to high-level stochastic models. In this paper, an improvement of dynamic electrical battery model is proposed by automatic parameter extraction using genetic algorithm in order to give usefulness and future implementation for practical application. It is highlighted that the enhancement of 21 values of the parameters of CEIMAT model presents a good agreement with real measurements for different modes like charge or discharge and various conditions. (C) 2016 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Photovoltaic Systems (PVS), Genetic algorithm (GA), Lead-acid battery, Stand-alone systems
Kaynak
Applied Energy
WoS Q Değeri
Q1
Scopus Q Değeri
Cilt
169
