State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach
Automotive batteries; Charging (batteries); Deep neural networks; Electric vehicles; Ions; Lithium compounds; Lithium-ion batteries; Neural networks; Ambient environment; Battery chemistries; Crucial parameters; Federal test procedures; State of charge; State-of-charge estimation; Training algorithm...
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-25262 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-252622023-05-29T16:07:40Z State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach How D.N.T. Hannan M.A. Lipu M.S.H. Sahari K.S.M. Ker P.J. Muttaqi K.M. 57212923888 7103014445 36518949700 57218170038 37461740800 55582332500 Automotive batteries; Charging (batteries); Deep neural networks; Electric vehicles; Ions; Lithium compounds; Lithium-ion batteries; Neural networks; Ambient environment; Battery chemistries; Crucial parameters; Federal test procedures; State of charge; State-of-charge estimation; Training algorithms; Vehicle applications; Battery management systems The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles-akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively. � 1972-2012 IEEE. Final 2023-05-29T08:07:40Z 2023-05-29T08:07:40Z 2020 Conference Paper 10.1109/TIA.2020.3004294 2-s2.0-85091776896 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091776896&doi=10.1109%2fTIA.2020.3004294&partnerID=40&md5=cfa56c5953537c5c5c1b5375bfe42a32 https://irepository.uniten.edu.my/handle/123456789/25262 56 5 9122534 5565 5574 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 |
Automotive batteries; Charging (batteries); Deep neural networks; Electric vehicles; Ions; Lithium compounds; Lithium-ion batteries; Neural networks; Ambient environment; Battery chemistries; Crucial parameters; Federal test procedures; State of charge; State-of-charge estimation; Training algorithms; Vehicle applications; Battery management systems |
| author2 |
57212923888 |
| author_facet |
57212923888 How D.N.T. Hannan M.A. Lipu M.S.H. Sahari K.S.M. Ker P.J. Muttaqi K.M. |
| format |
Conference Paper |
| author |
How D.N.T. Hannan M.A. Lipu M.S.H. Sahari K.S.M. Ker P.J. Muttaqi K.M. |
| spellingShingle |
How D.N.T. Hannan M.A. Lipu M.S.H. Sahari K.S.M. Ker P.J. Muttaqi K.M. State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| author_sort |
How D.N.T. |
| title |
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| title_short |
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| title_full |
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| title_fullStr |
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| title_full_unstemmed |
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach |
| title_sort |
state-of-charge estimation of li-ion battery in electric vehicles: a deep neural network approach |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806428031779078144 |