Optimization PI-ACO for Photovoltaic System Battery and Supercapacitor on Electric Vehicle

In this study, energy management control for Photovoltaic (PV), battery, and a supercapacitor (SC) uses PI with Ant Colony optimization (ACO) tuning, in reducing the output voltage error rate. Supercapacitors to inject high power frequency fluctuations smooth out the battery system power. The optimization of the proposed PI-ACO control was examined and its performance investigated through MATLAB/Simulink simulation with a comparison of PI and PI-FA controllers (Firefly Algorithm). The reference voltage is 42V, the PI-ACO control overshoot is 42. 11V, the setting time is 0. 252s, and the final value is 42. 05V and the PI-Firefly Algorithm (FA) control is 42.17 V, the setting time is 0. 509s, the final value is 42. 18V with PI control, the overshoot is 42. 22V, the setting time value is 0. 256s, the fmal value is 42. 04V when the reference voltage is 52V with PI-ACO control the overshoot value is 52. 34V, the settlement time value is 0. 229s, the final value is 52. 12V with PI-FA control overshoot value 52.50 V, settling time value 0. 502s, and final value 52. 44V with PI control overshoot 52. 70V, setting time 0. 287s, final value 52. 14V. From the performance of the PI-ACO control system, it is shown that the overshoot is smaller, the response time is faster. Future research optimization using hybrid fuzzy-PI.