ijeat

International Journal of Energy Applications and Technologies

2548-060X

İlker ÖRS

10.31593/ijeat.778689

Mechanical Engineering

Makine Mühendisliği

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

https://orcid.org/0000-0001-9864-7512

Karabacak

Yusuf

Turkish National University

https://orcid.org/0000-0002-5715-1040

Ozkan

İlker Ali

SELCUK UNIVERSITY

https://orcid.org/0000-0002-5743-4593

Saritas

İsmail

SELCUK UNIVERSITY

10 05 2020

7 3 88 94 08 10 2020 09 29 2020

2014

International Journal of Energy Applications and Technologies

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.

ANFIS Battery charge status Charge status Estimation Rechargeable batteries

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