Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm
Backpropagation; Charging (batteries); Errors; Industrial electronics; Ions; Lithium-ion batteries; Mean square error; Particle swarm optimization (PSO); Recurrent neural networks; Temperature; Back-propagation neural networks; Drive cycles; Lithium ions; NARX neural network; Nonlinear AutoRegressiv...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-23759 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-237592023-05-29T14:51:36Z Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm Hossain Lipu M.S. Hussain A. Saad M.H.M. Ayob A. Hannan M.A. 36518949700 57208481391 7202075525 26666566900 7103014445 Backpropagation; Charging (batteries); Errors; Industrial electronics; Ions; Lithium-ion batteries; Mean square error; Particle swarm optimization (PSO); Recurrent neural networks; Temperature; Back-propagation neural networks; Drive cycles; Lithium ions; NARX neural network; Nonlinear AutoRegressive with eXogenous input models; Particle swarm optimization algorithm; State of charge; State-of-charge estimation; Battery management systems This paper aims to develop an accurate estimation technique for computing state of charge (SOC) of a lithium-ion battery using recurrent neural network algorithm. Nonlinear autoregressive with exogenous input (NARX) model is a well-known subclass of the recurrent neural network which has proven to be very effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARX neural network depends on the amount of input and output order as well as a number of neurons in a hidden layer. Therefore, this study presents an improved recurrent NARX neural network based SOC estimation with particle swarm optimization (PSO) algorithm for finding the best value of input delays, feedback delays and a number of neurons in a hidden layer. The proposed model uses three most significant factor such as current, voltage and temperature without considering battery model. The model robustness is checked at low temperature (0�C), medium temperature (25�C), and high temperature (45�C). The US06 drive cycle is selected for model training and testing. The effectiveness of the proposed approach is compared with the back-propagation neural network (BPNN) optimized by PSO based on the SOC error, root mean square error (RMSE) and mean absolute error (MAE) and average execution time (AET). The results prove that the proposed model has higher estimation speed and achieves higher accuracy in reducing RMSE and MAE by 53% and 50% than BPNN based PSO model at 25�C. � 2018 IEEE. Final 2023-05-29T06:51:36Z 2023-05-29T06:51:36Z 2018 Conference Paper 10.1109/ISCAIE.2018.8405498 2-s2.0-85050666856 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050666856&doi=10.1109%2fISCAIE.2018.8405498&partnerID=40&md5=e446377ff1407b9aaab3a9628af99a6a https://irepository.uniten.edu.my/handle/123456789/23759 354 359 Institute of Electrical and Electronics Engineers Inc. Scopus |
| 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/ |
| description |
Backpropagation; Charging (batteries); Errors; Industrial electronics; Ions; Lithium-ion batteries; Mean square error; Particle swarm optimization (PSO); Recurrent neural networks; Temperature; Back-propagation neural networks; Drive cycles; Lithium ions; NARX neural network; Nonlinear AutoRegressive with eXogenous input models; Particle swarm optimization algorithm; State of charge; State-of-charge estimation; Battery management systems |
| author2 |
36518949700 |
| author_facet |
36518949700 Hossain Lipu M.S. Hussain A. Saad M.H.M. Ayob A. Hannan M.A. |
| format |
Conference Paper |
| author |
Hossain Lipu M.S. Hussain A. Saad M.H.M. Ayob A. Hannan M.A. |
| spellingShingle |
Hossain Lipu M.S. Hussain A. Saad M.H.M. Ayob A. Hannan M.A. Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| author_sort |
Hossain Lipu M.S. |
| title |
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| title_short |
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| title_full |
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| title_fullStr |
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| title_full_unstemmed |
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| title_sort |
improved recurrent narx neural network model for state of charge estimation of lithium-ion battery using pso algorithm |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806426024242577408 |