PENERAPAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM UNTUK ESTIMASI STATE OF CHARGE BATERAI LEAD ACID
Keywords:
state of charge, ANFIS, lead-acid, buck converter, PI, CC-CVAbstract
Excessive and uncontrolled battery charging can lead to rapid overheating and damage to the battery. The State of Charge (SOC) indicates the proportion of remaining capacity relative to the nominal capacity. Understanding the SOC value helps prevent excessive battery charging. This study implements the ANFIS method for estimating the SOC of lead-acid batteries. The combination of ANN and fuzzy logic allows predictions based on the provided training data and decision-making based on applied rules. A buck converter with PI control using CC-CV method is employed to maintain a consistent current and voltage during battery charging. These parameters serve as training data for ANFIS to estimate SOC value. Research results demonstrate that the PI control effectively maintains constant current and voltage charging. Additionally, ANFIS exhibits the capability to estimate battery SOC with an average inaccuracy of 0.121% on simulations and 0.2% on hardware testingReferences
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Indonesian Society of Applied Science (ISAS)
