Open AccessBalancing Charging System Using Adaptive Neuro-Fuzzy Inference System Based On CUK Converter
Author(s) -
Mohammad Fajar Setyawan,
Mohammad Zaenal Efendi,
Farid Dwi Murdianto
Publication year - 2021
Publication title -
jaree (journal on advanced research in electrical engineering)
Language(s) - English
Resource type - Journals
eISSN - 2580-0361
pISSN - 2579-6216
DOI - 10.12962/jaree.v5i2.199
Subject(s) - ćuk converter , adaptive neuro fuzzy inference system , duty cycle , buck–boost converter , control theory (sociology) , boost converter , computer science , buck converter , voltage , capacitor , forward converter , battery (electricity) , topology (electrical circuits) , fuzzy logic , electrical engineering , engineering , fuzzy control system , physics , power (physics) , artificial intelligence , control (management) , quantum mechanics
In a battery set, there is always a voltage difference caused by charging and discharging. Therefore, it is necessary to pay attention to the condition of the battery or State of Charge (SOC) so that it is in a balanced state between the batteries. Unbalanced battery conditions result in decreased performance of the battery. For that we need a balancing circuit that works actively with the help of a DC-DC converter. DC-DC converters generally have a principle like a buck-boost converter to increase and decrease the output voltage, however the output still has a fairly large ripple in the waveform. Therefore, the CUK converter is used which is a development of the buck-boost converter topology, where the output of this CUK converter has smaller ripples because it uses two capacitors and two inductors. Of the various methods used to adjust the duty cycle of the CUK converter, a precise and accurate algorithm is needed to overcome the instability of the converter output. The method used to adjust the duty cycle uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm as the development of the Fuzzy method. The system is implemented using MATLAB Simulink software. The simulation results show that the output of the CUK converter with the ANFIS method has a faster response speed reaching a set point of 1.95 × 10-4 seconds and the accuracy of the output voltage with ANFIS is 99.94% while the accuracy of the output converter current using ANFIS is 65.7%.Keywords: ANFIS, balancing, battery, CUK converter, state of charge (SOC).15