State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method

Derry Rifqi Septian Nugraha; Anjar Bryantiko Pangestu; Faiz Husnayain

doi:10.1109/FORTEI-ICEE50915.2020.9249870

State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method

Derry Rifqi Septian Nugraha, Anjar Bryantiko Pangestu, Faiz Husnayain

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	119-124
Number of pages	6
ISBN (Electronic)	9781728194349
DOIs	https://doi.org/10.1109/FORTEI-ICEE50915.2020.9249870
Publication status	Published - 23 Sept 2020
Event	1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020 - Virtual, Bandung, Indonesia Duration: 23 Sept 2020 → 24 Sept 2020

Publication series

Name	Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020

Conference

Conference	1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
Country/Territory	Indonesia
City	Virtual, Bandung
Period	23/09/20 → 24/09/20

Keywords

Coulomb counting
feed-forward neural network
lead-acid battery
state of charge

Access to Document

10.1109/FORTEI-ICEE50915.2020.9249870

Cite this

Nugraha, D. R. S., Pangestu, A. B., & Husnayain, F. (2020). State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method. In Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020 (pp. 119-124). Article 9249870 (Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FORTEI-ICEE50915.2020.9249870

Nugraha, Derry Rifqi Septian ; Pangestu, Anjar Bryantiko ; Husnayain, Faiz. / State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method. Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 119-124 (Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020).

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title = "State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method",

abstract = "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.",

keywords = "Coulomb counting, feed-forward neural network, lead-acid battery, state of charge",

author = "Nugraha, {Derry Rifqi Septian} and Pangestu, {Anjar Bryantiko} and Faiz Husnayain",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020 ; Conference date: 23-09-2020 Through 24-09-2020",

year = "2020",

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doi = "10.1109/FORTEI-ICEE50915.2020.9249870",

language = "English",

series = "Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020",

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Nugraha, DRS, Pangestu, AB & Husnayain, F 2020, State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method. in Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020., 9249870, Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 119-124, 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020, Virtual, Bandung, Indonesia, 23/09/20. https://doi.org/10.1109/FORTEI-ICEE50915.2020.9249870

State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method. / Nugraha, Derry Rifqi Septian; Pangestu, Anjar Bryantiko; Husnayain, Faiz.
Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 119-124 9249870 (Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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.

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KW - feed-forward neural network

KW - lead-acid battery

KW - state of charge

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T3 - Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020

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BT - Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 23 September 2020 through 24 September 2020

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Nugraha DRS, Pangestu AB, Husnayain F. State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method. In Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 119-124. 9249870. (Proceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020). doi: 10.1109/FORTEI-ICEE50915.2020.9249870

State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method

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