State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network
Charging (batteries); Deep learning; Learning systems; Lithium compounds; Lithium-ion batteries; Long short-term memory; Multilayer neural networks; Temperature; Empirical evaluations; Experiment set-up; Hyper-parameter; Learning methods; Multi layer perceptron; Simple recurrent networks; State of c...
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!
|
| id |
my.uniten.dspace-25197 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-251972023-05-29T16:07:17Z State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network Hannan M.A. How D.N.T. Mansor M. Lipu M.S.H. Ker P.J. Muttaqi K.M. 7103014445 57212923888 6701749037 36518949700 37461740800 55582332500 Charging (batteries); Deep learning; Learning systems; Lithium compounds; Lithium-ion batteries; Long short-term memory; Multilayer neural networks; Temperature; Empirical evaluations; Experiment set-up; Hyper-parameter; Learning methods; Multi layer perceptron; Simple recurrent networks; State of charge; State-of-charge estimation; Battery management systems The state of charge (SOC) is a crucial indicator of a Li-ion battery management system (BMS). A BMS with a good SOC assessment can dramatically improve the lifespan of the battery and ensure the safety of the end-user. With deep learning making tremendous strides in many other fields, this study aims to provide an empirical evaluation of commonly used deep learning methods on the task of SOC estimation. We propose the use of two-hidden-layer gated recurrent units (GRU) to estimate the SOC at various ambient temperatures. In this work, we conducted two experiment setups to showcase the capability of the proposed GRU model. In the first setup, the GRU was trained on the DST, BJDST and US06 drive cycle and evaluated the FUDS drive cycle upon convergence. The same procedure was repeated with the second setup except the GRU was trained on the DST, BJDST and FUDS drive cycle and evaluated on the US06 drive cycle. In both experiment setups, the proposed GRU was evaluated on a novel drive cycle that it has not encountered during the training phase. We show that a two-hidden-layer GRU with appropriate hyperparameter combination and training methodology can reliably estimate the SOC of novel drive cycles at various ambient temperatures in comparison with other deep learning methods such as simple recurrent network (SRNN), Long Short-Term Memory (LSTM), 1D Residual Network (Resnet), 1D Visual Geometry Group Network (VGG) and the Multilayer Perceptron (MLP). The proposed GRU achieves 2.3% RMSE on the FUDS drive cycle and 1.2% RMSE on the US06 drive cycle outperforming all other models. � 2020 IEEE. Final 2023-05-29T08:07:17Z 2023-05-29T08:07:17Z 2020 Conference Paper 10.1109/IAS44978.2020.9334824 2-s2.0-85101014266 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101014266&doi=10.1109%2fIAS44978.2020.9334824&partnerID=40&md5=8bb1c1dde6d13d9305be8529b5d145ef https://irepository.uniten.edu.my/handle/123456789/25197 9334824 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 |
Charging (batteries); Deep learning; Learning systems; Lithium compounds; Lithium-ion batteries; Long short-term memory; Multilayer neural networks; Temperature; Empirical evaluations; Experiment set-up; Hyper-parameter; Learning methods; Multi layer perceptron; Simple recurrent networks; State of charge; State-of-charge estimation; Battery management systems |
| author2 |
7103014445 |
| author_facet |
7103014445 Hannan M.A. How D.N.T. Mansor M. Lipu M.S.H. Ker P.J. Muttaqi K.M. |
| format |
Conference Paper |
| author |
Hannan M.A. How D.N.T. Mansor M. Lipu M.S.H. Ker P.J. Muttaqi K.M. |
| spellingShingle |
Hannan M.A. How D.N.T. Mansor M. Lipu M.S.H. Ker P.J. Muttaqi K.M. State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| author_sort |
Hannan M.A. |
| title |
State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| title_short |
State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| title_full |
State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| title_fullStr |
State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| title_full_unstemmed |
State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network |
| title_sort |
state-of-charge estimation of li-ion battery at variable ambient temperature with gated recurrent unit network |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424038185107456 |
| score |
13.214268 |