TY - GEN
T1 - Optimization PI-ACO for Photovoltaic System Battery and Supercapacitor on Electric Vehicle
AU - Robandi, Imam
AU - Ajiatmo, Dwi
AU - Muhlasin,
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - 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.
AB - 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.
KW - Ant Colony optimization
KW - Photovoltaic
KW - battery
KW - supercapacitor
UR - http://www.scopus.com/inward/record.url?scp=85114600974&partnerID=8YFLogxK
U2 - 10.1109/ISITIA52817.2021.9502221
DO - 10.1109/ISITIA52817.2021.9502221
M3 - Conference contribution
AN - SCOPUS:85114600974
T3 - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021
SP - 370
EP - 375
BT - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
Y2 - 21 July 2021 through 22 July 2021
ER -
