TY - GEN
T1 - State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method
AU - Nugraha, Derry Rifqi Septian
AU - Pangestu, Anjar Bryantiko
AU - Husnayain, Faiz
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/23
Y1 - 2020/9/23
N2 - This study aims to design and assess the simulation of the state of charge (SoC) estimation on lead-acid batteries using the Coulomb counting (CC) and feed-forward neural network (FFNN) method. Also, this study compared the effectiveness of each technique. CC and FFNN methods were designed and simulated in Simulink, and the results were analyzed. The two estimation results are compared to see the level of efficiency of each technique. The results of this study show that the feed-forward neural network method is better than the Coulomb counting method in load variation with a ratio of 5.56% on the first data input and 0.46% on the second data input, and 5.78% in temperature variation.
AB - This study aims to design and assess the simulation of the state of charge (SoC) estimation on lead-acid batteries using the Coulomb counting (CC) and feed-forward neural network (FFNN) method. Also, this study compared the effectiveness of each technique. CC and FFNN methods were designed and simulated in Simulink, and the results were analyzed. The two estimation results are compared to see the level of efficiency of each technique. The results of this study show that the feed-forward neural network method is better than the Coulomb counting method in load variation with a ratio of 5.56% on the first data input and 0.46% on the second data input, and 5.78% in temperature variation.
KW - Coulomb counting
KW - feed-forward neural network
KW - lead-acid battery
KW - state of charge
UR - http://www.scopus.com/inward/record.url?scp=85097720997&partnerID=8YFLogxK
U2 - 10.1109/FORTEI-ICEE50915.2020.9249870
DO - 10.1109/FORTEI-ICEE50915.2020.9249870
M3 - Conference contribution
AN - SCOPUS:85097720997
T3 - Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
SP - 119
EP - 124
BT - Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
Y2 - 23 September 2020 through 24 September 2020
ER -