TY - GEN
T1 - Charging Station Controller Design using Particle Swarm Optimization Algorithms for Electric Vehicles with NiMH Battery
AU - Enola, Nuh
AU - Iskandar, Eka
AU - Fatoni, Ali
AU - Santoso, Ari
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, electric vehicles are growing rapidly with various study about it, no exception regarding to the battery charging system. Electric vehicles use many types of battery, including NiMH battery. One of the study or research about charging system is about how to optimize battery charging using an intelligent algorithm. However, this method is rarely implemented in real-Time and only through the help of the computer software. In this project, we discuss the implementation of the Particle Swarm Optimization Algorithm as a real time optimization method on the charger controller with the hope of providing solutions according to the user needs. For the 3 optimization charging conditions the prototype results in calculations with error costs of 0.33%, 7.22%, and 5.55%, respectively, compared to the results in the simulation. With this value, the implementation of PSO in real-Time systems has achieved a 95% success rate.
AB - Nowadays, electric vehicles are growing rapidly with various study about it, no exception regarding to the battery charging system. Electric vehicles use many types of battery, including NiMH battery. One of the study or research about charging system is about how to optimize battery charging using an intelligent algorithm. However, this method is rarely implemented in real-Time and only through the help of the computer software. In this project, we discuss the implementation of the Particle Swarm Optimization Algorithm as a real time optimization method on the charger controller with the hope of providing solutions according to the user needs. For the 3 optimization charging conditions the prototype results in calculations with error costs of 0.33%, 7.22%, and 5.55%, respectively, compared to the results in the simulation. With this value, the implementation of PSO in real-Time systems has achieved a 95% success rate.
KW - Charger Controller
KW - Electric Vehicle
KW - NiMH
KW - PSO
UR - http://www.scopus.com/inward/record.url?scp=85149138397&partnerID=8YFLogxK
U2 - 10.1109/CENIM56801.2022.10037318
DO - 10.1109/CENIM56801.2022.10037318
M3 - Conference contribution
AN - SCOPUS:85149138397
T3 - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
SP - 353
EP - 358
BT - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
Y2 - 22 November 2022 through 23 November 2022
ER -