Optimization of PID Tuning of DC-DC Bidirectional Converter Control Strategy for EV Charger using GA and IoT based SOC Estimation

Electric Vehicle (EV) charger combines power electronics devices with a control scheme consisting of an AC to DC converter for rectification and DC to DC converter for required voltage level. The objective presents to put the desired level of output. Bi-directional converter of DC to DC operation charges EV battery by taking energy of DC link voltage, and for discharging of battery, the DC to DC converter feeds 800V dc. Here DC-DC converter (non-isolated) designed and implemented for bidirectional function. The converter controlling scheme is created by a tune PID controller encapsulated by genetic algorithm based gain defining to get less integral time absolute error (ITAE). To charge the battery and discharging as an integral part of the grid to vehicle (G2V) method to support peak load and grid to vehicle (G2V) as conventional charging. The EV battery parameters are monitored, and the expected time of completion of charge is calculated and represented. SOC estimation and estimated charging time are done by the coulomb counting method. Battery SOC, charger rating, and expected completion time of charging are all put into the cloud storage in ThingSpeak for monitoring purposes. The stored data is used for analysis and visualization using internet of things (IoT).