Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Yusuf KARABACAK; İlker Ali OZKAN; İsmail SARİTAS

doi:https://doi.org/10.31593/ijeat.778689

Makale detayları

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Yazarlar:

Yusuf KARABACAK | İlker Ali OZKAN | İsmail SARİTAS

Yayıncı:

İlker ÖRS

Ana Başlıklar:

Diğer mühendislik dalları

Dergi:

International Journal of Energy Applications and Technologies 2020 Vol.7 Issue 3, pp.88-94

Belge Tipi:

Makale

Yayın Tarihi:

10/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000719010

Sıklık:

Üç Aylık

eISSN:

2548-060X

DOI:

https://doi.org/10.31593/ijeat.778689

Özet:

 

Thanks to their electrochemical structure, batteries are the elements that can store electrical energy and spend on a load when the electrical energy they store is needed. Today, with the widespread use of electrically powered mobile devices, rechargeable batteries have become widespread and battery technologies have developed. With the idea that the latest technology systems and electric vehicles will become widespread in the future, the studies on batteries are increasing day by day. In this study, charge state estimation of Li-ion battery cell used to provide power in many applications was realized by using adaptive neural fuzzy inference system (ANFIS). A Li-ion battery was discharged using variable electrical loads with a battery discharge circuit modeled on MATLAB Simulink and current, voltage, temperature and current power parameters of the battery were selected as input variables. Battery parameters and charge status data obtained from discharge tests using different electrical loads on MATLAB Simulink were used as training and test parameters of neural network. Using the MATLAB ANFIS toolbox, the system was trained with 80% of the battery parameters obtained in the battery discharge experiments and with 20% as testing data, the success performance was interpreted by applying the adaptive neural fuzzy inference system.

Makale detayları

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Yazarlar: Yusuf KARABACAK | İlker Ali OZKAN | İsmail SARİTAS

Yayıncı: İlker ÖRS

Ana Başlıklar: Diğer mühendislik dalları

Dergi: International Journal of Energy Applications and Technologies 2020 Vol.7 Issue 3, pp.88-94

Belge Tipi: Makale

Yayın Tarihi: 10/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000719010

Sıklık: Üç Aylık

eISSN: 2548-060X

DOI: https://doi.org/10.31593/ijeat.778689

Sayı

Yazarlar:

Yusuf KARABACAK | İlker Ali OZKAN | İsmail SARİTAS

Yayıncı:

İlker ÖRS

Ana Başlıklar:

Diğer mühendislik dalları

Dergi:

International Journal of Energy Applications and Technologies 2020 Vol.7 Issue 3, pp.88-94

Belge Tipi:

Makale

Yayın Tarihi:

10/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000719010

Sıklık:

Üç Aylık

eISSN:

2548-060X

DOI:

https://doi.org/10.31593/ijeat.778689