State of charge estimation of Lithium Polymer battery using ANFIS and IT2FLS

Abstract

in this research, the estimation method using IT2FLS (Interval Type 2 Fuzzy Logic System) and ANFIS (Adaptive Neuro-Fuzzy Inference System) as a base to build the membership functions and the rule base is constructed. The differences area of uncertainty is used to determine a model of type 2 fuzzy systems based on the smallest RMSE value. This study uses two methods of type-reducer, namely Enhanced Iterative Algorithm with Stop Condition (EIASC) and Enhanced Opposite Direction Search (EODS) to determine the most appropriate capacity estimation of the battery. Two types of datasets are used to determine the method performance indicated by MSE, RMSE and MAE. Based on the tests performed in three methods: T1FLS, IT2FLS EIASC, and IT2FLS EODS, it has been found that IT2FLS produces the smallest RMSE value with the RMSE value of 3.3% for static discharge dataset and 5.9% for pulse variation dataset.