Open AccessAdaptive charging control using ANN-PID controllers on multiple DC loads with varying battery voltages
Author(s) -
Indhana Sudiharto,
Farid Dwi Murdianto,
Ayu Wulandari
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijpeds.v13.i1.pp620-630
Subject(s) - pid controller , control theory (sociology) , setpoint , duty cycle , voltage , battery (electricity) , controller (irrigation) , computer science , supervisor , voltage regulation , buck converter , power (physics) , engineering , electrical engineering , control engineering , control (management) , temperature control , agronomy , physics , quantum mechanics , artificial intelligence , law , political science , biology
Various rechargeable electronic devices currently have batteries with different capacities and voltages, while the available chargers are generally fixed for one device. This is considered less effective because different types of electronic devices will require different battery chargers. Therefore, the adaptive power charge is needed to recharge batteries with different voltages and capacities through a single port by adjusting the type of load connected. This system uses buck converter with duty cycle settings through microcontrollers to lower the input voltage to variable output voltage. When the load is connected, the limit switch will be depressed and the system will start the duty cycle tracking process. The voltage will be increased gradually until the current is read at a certain value to identify the load. After the current reads the duty cycle stops tracking, then the current and voltage characteristics are used as input variables for the artificial neural network (ANN) algorithm to determine the target setpoint voltage to be executed by the proportional, integral and derivative (PID) controller. The designed adaptive power charge can identify the connected load accurately. The average ANN output error is 1.46e-4% and the average PID controller error is 6.4e-2%. The system can reach a steady state at 0.01 s.