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Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state...

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Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan Abdul, Abdul Aziz
Format: Conference or Workshop Item
Language:English
English
Published: 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/38769/1/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters.pdf
http://umpir.ump.edu.my/id/eprint/38769/2/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters%20in%20battery%20state%20of%20charge%20estimation_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38769/
https://doi.org/10.1109/ISCAIE57739.2023.10164965
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spelling my.ump.umpir.387692023-11-06T04:36:25Z http://umpir.ump.edu.my/id/eprint/38769/ Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation Zuriani, Mustaffa Mohd Herwan, Sulaiman Azlan Abdul, Abdul Aziz QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38769/1/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters.pdf pdf en http://umpir.ump.edu.my/id/eprint/38769/2/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters%20in%20battery%20state%20of%20charge%20estimation_ABS.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Azlan Abdul, Abdul Aziz (2023) Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation. In: 13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023, 20-21 May 2023 , Penang. pp. 56-61.. ISBN 979-835034731-9 https://doi.org/10.1109/ISCAIE57739.2023.10164965
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan Abdul, Abdul Aziz
Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
description To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation.
format Conference or Workshop Item
author Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan Abdul, Abdul Aziz
author_facet Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan Abdul, Abdul Aziz
author_sort Zuriani, Mustaffa
title Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
title_short Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
title_full Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
title_fullStr Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
title_full_unstemmed Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
title_sort metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38769/1/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters.pdf
http://umpir.ump.edu.my/id/eprint/38769/2/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters%20in%20battery%20state%20of%20charge%20estimation_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38769/
https://doi.org/10.1109/ISCAIE57739.2023.10164965
_version_ 1822923746282831872
score 13.244413

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