State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm
Charging (batteries); Decision trees; Electric vehicles; Forestry; Ions; Learning algorithms; Lithium-ion batteries; Radial basis function networks; Gravitational search algorithm (GSA); Gravitational search algorithms; Radial basis function neural networks; Random forests; State of charge; State of...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-23504 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-235042023-05-29T14:49:58Z State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm Lipu M.S.H. Ayob A. Saad M.H.M. Hussain A. Hannan M.A. Faisal M. 36518949700 26666566900 7202075525 57208481391 7103014445 57215018777 Charging (batteries); Decision trees; Electric vehicles; Forestry; Ions; Learning algorithms; Lithium-ion batteries; Radial basis function networks; Gravitational search algorithm (GSA); Gravitational search algorithms; Radial basis function neural networks; Random forests; State of charge; State of charge estimations (SOC); State-of-charge estimation; Sustainable transportation systems; Battery management systems An accurate state of charge (SOC) estimation for lithium-ion battery has been an intensively researched subject in electric vehicle (EV) application towards the advancement of the sustainable transportation system. However, SOC estimation with high accuracy is challenging because of the complex internal characteristics of the lithium-ion battery which is changed by different environmental situations. This paper develops an accurate method for the state of charge (SOC) estimation of a lithium-ion battery using random forests (RFs) algorithm. However, the accuracy of RFs highly depends on the appropriate selection of trees and leaves per tree in a forest. Thus, this research develops an enhanced model with RFs based gravitational search algorithm (GSA). The aim of GSA is to find the best value of trees and leaves per tree. The robustness and accuracy of the proposed model are tested under different temperatures. The model training and validation are executed using federal urban driving schedule (FUDS). The effectiveness of the proposed method is compared with the conventional RFs and radial basis function neural network (RBFNN) and optimal RBFNN-GSA models using different statistical error terms and computational cost. The proposed RFs based GSA model offers higher robustness and accuracy in reducing RMSE by 55.4 % , 67.4%, and MAE by 39.1% and 78.1% than conventional RFs and RBFNN based GSA model, respectively at 25�C. � 2018 IEEE. Final 2023-05-29T06:49:58Z 2023-05-29T06:49:58Z 2018 Conference Paper 10.1109/APPEEC.2018.8566648 2-s2.0-85060375580 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060375580&doi=10.1109%2fAPPEEC.2018.8566648&partnerID=40&md5=6e4aff5b9fa26b590a19971902c87977 https://irepository.uniten.edu.my/handle/123456789/23504 2018-October 8566648 45 50 IEEE Computer Society 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 |
Charging (batteries); Decision trees; Electric vehicles; Forestry; Ions; Learning algorithms; Lithium-ion batteries; Radial basis function networks; Gravitational search algorithm (GSA); Gravitational search algorithms; Radial basis function neural networks; Random forests; State of charge; State of charge estimations (SOC); State-of-charge estimation; Sustainable transportation systems; Battery management systems |
| author2 |
36518949700 |
| author_facet |
36518949700 Lipu M.S.H. Ayob A. Saad M.H.M. Hussain A. Hannan M.A. Faisal M. |
| format |
Conference Paper |
| author |
Lipu M.S.H. Ayob A. Saad M.H.M. Hussain A. Hannan M.A. Faisal M. |
| spellingShingle |
Lipu M.S.H. Ayob A. Saad M.H.M. Hussain A. Hannan M.A. Faisal M. State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| author_sort |
Lipu M.S.H. |
| title |
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| title_short |
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| title_full |
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| title_fullStr |
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| title_full_unstemmed |
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| title_sort |
state of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm |
| publisher |
IEEE Computer Society |
| publishDate |
2023 |
| _version_ |
1806425674192257024 |
| score |
13.214268 |