GA-Optimized PI Control in CC-CV Based On-Board Charging For EVs

Electric vehicle (EV) charging systems are a crucial aspect of the fast-growing ecosystem of sustainable transportation, whose operations can be potentially optimized based on a variety of factors, such as efficient energy utilization, safe energy transfer, etc. This study proposes a genetic algorithm-optimized Constant Current-Constant Voltage (CC-CV) charging model for developing on-board EV charging systems, performed using MATLAB/Simulink. The proposed model has two PI controllers, each controls the current and the voltage characteristics during charging of the battery. The model takes advantage of Genetic Algorithm (GA)-based optimization method for tuning the PI controller gains. The objective function is minimized by means of GA-based tuning which inculcates factors such as settling time, overshoot, and Integral Absolute Error (IAE). The system framework consists of a DC-DC converter, using which the gate signals are dynamically triggered based on the battery’s State of Charge (SOC) at that time, consequently resulting in charging via either the CC mode or the CV mode at a given time. The results of the simulations indicate improved charging efficiency and better obedience to desired voltage and current profiles. These findings symbolize the enhancement in performance of the CC-CV charging method due to GA-based tuning which makes GA-based tuning an impactful solution for onboard EV battery charging systems.