Fast Charging on Li-ion Batteries with ANFIS Control
Abstract
Most electrical energy used today comes from fossil fuels, which can deplete and contribute to air pollution. In Indonesia, the sun can be used as an alternative energy source and converted into electrical energy utilizing solar panel technology. The voltage generated by the solar panel is relatively high, so it needs to be lowered using a DC-DC converter type buck converter. This electrical energy can be stored using a battery which can be charged in a fast-charging mode to shorten the charging time. The most suitable battery type for fast charging is the lithium-ion (Li-ion) type for its capability to receive large current as big as 1C or equal to the battery capacity. Due to the temperature and solar irradiance effects, the output generated by the solar panel source is not constant. Moreover, to prevent overcharging, a constant current (CC) method with a constant current of 10 A and a constant voltage of 14.4 V was used which the PWM duty cycle driver was controlled using the adaptive neuro-fuzzy inference system (ANFIS) algorithm to obtain a faster response to reach the specified set point. ANFIS is a combination of two algorithms, i.e., artificial neural network (ANN) and fuzzy inference system (FIS). This research was conducted in simulation, the charging current results at the CC method of 10.01A were obtained and would move from the CC method to constant voltage (CV) when the state of charge (SoC) was 85% and the voltage reached 14.4 V. Then, the charging method would change to CV with a constant charging voltage of 14.4 V. When compared to the previous research using fuzzy control, the time required for ANFIS controls to reach set points was 3.2 ms, which is 2.3 ms faster than fuzzy controls, and ANFIS controls can reach set points with fewer errors.
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