Open AccessState of charge estimation of lithium-ion batteries using adaptive neuro fuzzy inference system
Author(s) -
Imane Chaoufi,
Othmane Abdelkhalek,
Brahim Gasbaoui
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp473-484
Subject(s) - state of charge , battery (electricity) , adaptive neuro fuzzy inference system , computer science , inference system , artificial neural network , neuro fuzzy , residual , voltage , lithium ion battery , lithium (medication) , control theory (sociology) , fuzzy control system , fuzzy logic , artificial intelligence , algorithm , control (management) , electrical engineering , engineering , power (physics) , physics , medicine , quantum mechanics , endocrinology
A battery’s state of charge (SOC) is used to assess its residual capacity. It is a very important parameter for the control of the electric vehicle (EV). The objective of this paper is to estimate the SOC of a lithium-ion battery (LIB) using an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) because SOC of a battery must be estimated from measurable battery parameters such as current, voltage or temperature. Two intelligent SOC estimation methods are compared according to their suitability and accuracy. ANN estimation is more precise and perfectly represents the experimental data.