TY - GEN
T1 - Comparison of ANFIS and FLC for CC-CV Precise Charging
AU - Murdianto, Farid Dwi
AU - Soedibyo,
AU - Ashari, Mochamad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Currently, electric vehicles in various countries have started to be used, in some areas their use is combined with utilizing renewable energy in terms of charging the battery. The process of charging electric vehicle batteries takes a long time. Charging duration can be reduced by providing high current and voltage. However, the use of voltage and current above the battery capacity is the main cause of a damaged battery. To overcome the values from solar panels, a DC-DC Converter is needed to adjust the voltage stability. In preventing damage to the battery, it is necessary to choose the right charging method to regulate the incoming power flow according to its needs. Therefore, in this study, a lithium-ion battery fast-charging system is simulated using a solar panel source regulated by the SEPIC Converter. In addition, the charging method used the Constant Current Constant Voltage method (CCCV). Based on the results that have been obtained through simulation testing of the Fast Charging Lithium-Ion battery system, it is proven that the ANFIS (Adaptive Neuro-Fuzzy System) method is more reliable than the Fuzzy Logic Controller method with a voltage error in ANFIS of 0.0065 Volt and a Fuzzy voltage error of 0.04 Volt.
AB - Currently, electric vehicles in various countries have started to be used, in some areas their use is combined with utilizing renewable energy in terms of charging the battery. The process of charging electric vehicle batteries takes a long time. Charging duration can be reduced by providing high current and voltage. However, the use of voltage and current above the battery capacity is the main cause of a damaged battery. To overcome the values from solar panels, a DC-DC Converter is needed to adjust the voltage stability. In preventing damage to the battery, it is necessary to choose the right charging method to regulate the incoming power flow according to its needs. Therefore, in this study, a lithium-ion battery fast-charging system is simulated using a solar panel source regulated by the SEPIC Converter. In addition, the charging method used the Constant Current Constant Voltage method (CCCV). Based on the results that have been obtained through simulation testing of the Fast Charging Lithium-Ion battery system, it is proven that the ANFIS (Adaptive Neuro-Fuzzy System) method is more reliable than the Fuzzy Logic Controller method with a voltage error in ANFIS of 0.0065 Volt and a Fuzzy voltage error of 0.04 Volt.
KW - ANFIS
KW - CCCV
KW - Fast Charging
KW - Fuzzy Logic Controller
KW - Lithium-Ion
UR - http://www.scopus.com/inward/record.url?scp=85137903432&partnerID=8YFLogxK
U2 - 10.1109/ISITIA56226.2022.9855368
DO - 10.1109/ISITIA56226.2022.9855368
M3 - Conference contribution
AN - SCOPUS:85137903432
T3 - 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
SP - 395
EP - 400
BT - 2022 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Y2 - 20 July 2022 through 21 July 2022
ER -
