Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
Alumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagatio...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-244652023-05-29T15:23:44Z 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. 36518949700 7103014445 57208481391 7202075525 26666566900 55582332500 Alumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagation neural networks; Computation intelligences; Electrochemical batteries; Firefly algorithms; Manganese-cobalt oxides; Radial basis function neural networks; Self-learning capability; State of charge; Lithium-ion batteries 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. Final 2023-05-29T07:23:44Z 2023-05-29T07:23:44Z 2019 Conference Paper 10.1109/IAS.2019.8912322 2-s2.0-85076785379 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076785379&doi=10.1109%2fIAS.2019.8912322&partnerID=40&md5=a012f9e43104e8a75d475818bf70da2f https://irepository.uniten.edu.my/handle/123456789/24465 8912322 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Alumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagation neural networks; Computation intelligences; Electrochemical batteries; Firefly algorithms; Manganese-cobalt oxides; Radial basis function neural networks; Self-learning capability; State of charge; Lithium-ion batteries |
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36518949700 |
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36518949700 Lipu M.S.H. Hannan M.A. Hussain A. Saad M.H.M. Ayob A. Muttaqi K.M. |
| 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_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 |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806428362289184768 |
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13.222552 |