Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm

This paper presents an enhanced machine learning based state of charge (SOC) estimation method for a lithium-ion battery using a deep recurrent neural network (DRNN) algorithm. DRNN is suitable for SOC evaluation due to strong computation intelligence and self-learning capabilities. Nevertheless, th...

Full description

Saved in:
Bibliographic Details
Main Authors: Lipu, M.S.H., Hannan, M.A., Hussain, A., Saad, M.H.M., Ayob, A., Muttaqi, K.M.
Format: Conference Paper
Language:English
Published: 2020
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-12866
record_format dspace
spelling my.uniten.dspace-128662020-07-07T03:45:29Z Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm Lipu, M.S.H. Hannan, M.A. Hussain, A. Saad, M.H.M. Ayob, A. Muttaqi, K.M. This paper presents an enhanced machine learning based state of charge (SOC) estimation method for a lithium-ion battery using a deep recurrent neural network (DRNN) algorithm. DRNN is suitable for SOC evaluation due to strong computation intelligence and self-learning capabilities. Nevertheless, the performance of DRNN is constrained due to the training accuracy and duration which entirely depends on the appropriate selection of hyper-parameters including hidden layer and hidden neurons. Therefore, firefly algorithm (FA) is employed to find the optimal number for hyper-parameters of DRNN networks. The optimized DRNN based FA algorithm for SOC estimation does not require extensive knowledge about battery chemistry, electrochemical battery model and added filter, rather only needs battery test bench to measure current and voltage. The developed model is tested using two different types of lithium-ion batteries namely lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2). The proposed model is validated by two experimental tests; one with static discharge test and other with pulse discharge test at room temperature. The experimental results indicate the superiority of the DRNN based FA method in comparison with the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). © 2019 IEEE. 2020-02-03T03:27:26Z 2020-02-03T03:27:26Z 2019 Conference Paper 10.1109/IAS.2019.8912322 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description This paper presents an enhanced machine learning based state of charge (SOC) estimation method for a lithium-ion battery using a deep recurrent neural network (DRNN) algorithm. DRNN is suitable for SOC evaluation due to strong computation intelligence and self-learning capabilities. Nevertheless, the performance of DRNN is constrained due to the training accuracy and duration which entirely depends on the appropriate selection of hyper-parameters including hidden layer and hidden neurons. Therefore, firefly algorithm (FA) is employed to find the optimal number for hyper-parameters of DRNN networks. The optimized DRNN based FA algorithm for SOC estimation does not require extensive knowledge about battery chemistry, electrochemical battery model and added filter, rather only needs battery test bench to measure current and voltage. The developed model is tested using two different types of lithium-ion batteries namely lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2). The proposed model is validated by two experimental tests; one with static discharge test and other with pulse discharge test at room temperature. The experimental results indicate the superiority of the DRNN based FA method in comparison with the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). © 2019 IEEE.
format Conference Paper
author Lipu, M.S.H.
Hannan, M.A.
Hussain, A.
Saad, M.H.M.
Ayob, A.
Muttaqi, K.M.
spellingShingle Lipu, M.S.H.
Hannan, M.A.
Hussain, A.
Saad, M.H.M.
Ayob, A.
Muttaqi, K.M.
Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
author_facet Lipu, M.S.H.
Hannan, M.A.
Hussain, A.
Saad, M.H.M.
Ayob, A.
Muttaqi, K.M.
author_sort Lipu, M.S.H.
title Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
title_short Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
title_full Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
title_fullStr Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
title_full_unstemmed Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
title_sort lithium-ion battery state of charge estimation method using optimized deep recurrent neural network algorithm
publishDate 2020
_version_ 1672614184943091712
score 13.214268