State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm
Backpropagation; Charging (batteries); Ions; Learning algorithms; Lighting; Lithium-ion batteries; Particle swarm optimization (PSO); Radial basis function networks; Back-propagation neural networks; Electrochemical reactions; NARX neural network; Non-linear autoregressive with exogenous; Radial bas...
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2023
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my.uniten.dspace-238222023-05-29T14:52:09Z State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm Lipu M.S.H. Hannan M.A. Hussain A. Saad M.H.M. Ayob A. Blaabjerg F. 36518949700 7103014445 57208481391 7202075525 26666566900 7004992352 Backpropagation; Charging (batteries); Ions; Learning algorithms; Lighting; Lithium-ion batteries; Particle swarm optimization (PSO); Radial basis function networks; Back-propagation neural networks; Electrochemical reactions; NARX neural network; Non-linear autoregressive with exogenous; Radial basis function neural networks; Search Algorithms; State of charge; State-of-charge estimation; Battery management systems State of charge (SOC) is one of the crucial parameters in a lithium-ion battery. The accurate estimation of SOC guarantees the safe and efficient operation of a specific application. However, SOC estimation with high accuracy is a serious concern to the automobile engineer due to the battery nonlinear characteristics and complex electrochemical reactions. This paper presents an improved nonlinear autoregressive with exogenous input (NARX)-based neural network (NARXNN) algorithm for an accurate and robust SOC estimation of lithium-ion battery which is effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARXNN depends on the amount of input order, output order, and hidden layer neurons. The unique contribution of the improved recurrent NARXNN-based SOC estimation is developed using lighting search algorithm (LSA) for finding the best value of input delays, feedback delays, and hidden layer neurons. The contributions are summarized as: 1) the computational capability of NARXNN model which does not require battery model and parameters rather only needs current, voltage, and temperature sensors; 2) the effectiveness of LSA which is verified with particle swarm optimization; 3) the adaptability, efficiency, and robustness of the model which are evaluated using FUDS and US06 drive cycles at varying temperatures conditions; and 4) the performance of the proposed model which is compared with back propagation neural network and radial basis function neural network optimized by LSA using different error statistical terms and computational time. Furthermore, a comparative analysis of SOC estimation in proposed method and existing techniques is presented for validation of NARXNN performance. The results prove that the proposed NARXNN model achieves higher accuracy with less computational time than other existing SOC algorithms under different temperature conditions and electric vehicle drive cycles. � 2013 IEEE. Final 2023-05-29T06:52:09Z 2023-05-29T06:52:09Z 2018 Article 10.1109/ACCESS.2018.2837156 2-s2.0-85046997280 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046997280&doi=10.1109%2fACCESS.2018.2837156&partnerID=40&md5=6767fb7d7cf8552eb5db7bb1ff5429bb https://irepository.uniten.edu.my/handle/123456789/23822 6 28150 28161 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
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Backpropagation; Charging (batteries); Ions; Learning algorithms; Lighting; Lithium-ion batteries; Particle swarm optimization (PSO); Radial basis function networks; Back-propagation neural networks; Electrochemical reactions; NARX neural network; Non-linear autoregressive with exogenous; Radial basis function neural networks; Search Algorithms; State of charge; State-of-charge estimation; Battery management systems |
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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. Blaabjerg F. |
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Article |
| author |
Lipu M.S.H. Hannan M.A. Hussain A. Saad M.H.M. Ayob A. Blaabjerg F. |
| spellingShingle |
Lipu M.S.H. Hannan M.A. Hussain A. Saad M.H.M. Ayob A. Blaabjerg F. State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| author_sort |
Lipu M.S.H. |
| title |
State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| title_short |
State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| title_full |
State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| title_fullStr |
State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| title_full_unstemmed |
State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm |
| title_sort |
state of charge estimation for lithium-ion battery using recurrent narx neural network model based lighting search algorithm |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424471258529792 |
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13.188404 |