Design of Sepic Converter as Battery Charger with Artificial Neural Network - PID Method for Load Variation
DOI:
https://doi.org/10.33019/jurnalecotipe.v13i1.4565Keywords:
Artificial Neural Network, Battery Adaptive Charge, PID, Sepic ConverterAbstract
More adaptable charging methods are required as a result of the growing variety of electronic gadgets that use rechargeable batteries brought about by technological advancements. When trying to charge different battery types with a same charger, differences in battery parameters, like voltage and capacity, present difficulties. This study suggests an adaptive charging system that can charge several battery types using a single port connection in order to solve this problem. To transform a 24 V DC input into an output voltage appropriate for the particular battery requirements, the system uses a SEPIC converter whose duty cycle is controlled by a microprocessor. The duty cycle rises until a specific current level is measured when a battery is attached and the limit switch is activated. The measured voltage and current data are then processed by an Artificial Neural Network (ANN) algorithm to determine the battery type and set the appropriate charging voltage setpoint. After that, a PID controller is used to keep the charging conditions steady. LiFePO4 (12.8 V, setpoint 14.6 V), Li-ion (14.8 V, setpoint 16.8 V), and Lead Acid (12 V, setpoint 14.4 V) batteries were used to test the system. The system successfully charged all three battery types with an average error of 0.174%, according to experimental findings, demonstrating precise and reliable control performance.
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