State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review
Charging (batteries); Classification (of information); Disaster prevention; Electric discharges; Electric vehicles; Estimation; Ions; Life cycle; Lithium-ion batteries; Data-driven approach; Data-driven methods; High energy densities; Mathematical equations; Model based approach; Model-based method;...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Review |
| 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-24903 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-249032023-05-29T15:28:34Z State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review How D.N.T. Hannan M.A. Hossain Lipu M.S. Ker P.J. 57212923888 7103014445 36518949700 37461740800 Charging (batteries); Classification (of information); Disaster prevention; Electric discharges; Electric vehicles; Estimation; Ions; Life cycle; Lithium-ion batteries; Data-driven approach; Data-driven methods; High energy densities; Mathematical equations; Model based approach; Model-based method; State of charge; State-of-charge estimation; Battery management systems Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery's behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-Tech EV applications. � 2013 IEEE. Final 2023-05-29T07:28:33Z 2023-05-29T07:28:33Z 2019 Review 10.1109/ACCESS.2019.2942213 2-s2.0-85076262905 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076262905&doi=10.1109%2fACCESS.2019.2942213&partnerID=40&md5=e6b8e1add9ea15ec9781d8e1ae4f8f09 https://irepository.uniten.edu.my/handle/123456789/24903 7 8843918 136116 136136 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 |
Charging (batteries); Classification (of information); Disaster prevention; Electric discharges; Electric vehicles; Estimation; Ions; Life cycle; Lithium-ion batteries; Data-driven approach; Data-driven methods; High energy densities; Mathematical equations; Model based approach; Model-based method; State of charge; State-of-charge estimation; Battery management systems |
| author2 |
57212923888 |
| author_facet |
57212923888 How D.N.T. Hannan M.A. Hossain Lipu M.S. Ker P.J. |
| format |
Review |
| author |
How D.N.T. Hannan M.A. Hossain Lipu M.S. Ker P.J. |
| spellingShingle |
How D.N.T. Hannan M.A. Hossain Lipu M.S. Ker P.J. State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| author_sort |
How D.N.T. |
| title |
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| title_short |
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| title_full |
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| title_fullStr |
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| title_full_unstemmed |
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review |
| title_sort |
state of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424324635099136 |