Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsio...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-23926 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-239262023-05-29T14:53:13Z Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm Hannan M.A. Lipu M.S.H. Hussain A. Saad M.H. Ayob A. 7103014445 36518949700 57208481391 7202075525 26666566900 Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures. � 2013 IEEE. Final 2023-05-29T06:53:13Z 2023-05-29T06:53:13Z 2018 Article 10.1109/ACCESS.2018.2797976 2-s2.0-85040974639 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040974639&doi=10.1109%2fACCESS.2018.2797976&partnerID=40&md5=624fe570a4d3e072457b6386d1dd3917 https://irepository.uniten.edu.my/handle/123456789/23926 6 10069 10079 All Open Access, Gold 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; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems |
| author2 |
7103014445 |
| author_facet |
7103014445 Hannan M.A. Lipu M.S.H. Hussain A. Saad M.H. Ayob A. |
| format |
Article |
| author |
Hannan M.A. Lipu M.S.H. Hussain A. Saad M.H. Ayob A. |
| spellingShingle |
Hannan M.A. Lipu M.S.H. Hussain A. Saad M.H. Ayob A. Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| author_sort |
Hannan M.A. |
| title |
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| title_short |
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| title_full |
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| title_fullStr |
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| title_full_unstemmed |
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| title_sort |
neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806427725374685184 |
| score |
13.19449 |