Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery

Huzaifa Rauf; Muhammad Shuzub Gul; Muhammad Khalid; Naveed Arshad

doi:10.1109/ICRERA59003.2023.10269362

Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery

Huzaifa Rauf
, Muhammad Shuzub Gul
, Muhammad Khalid
, Naveed Arshad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Li-ion batteries are widely used in electric vehicles (EVs), but they suffer from battery degradation, particularly in terms of calendar loss. Machine learning has been used to predict battery health deterioration due to cyclic loss, but the accuracy depends on input feature selection. This study introduces an improved feature selection method, enhancing battery calendar loss prediction. Eight machine learning algorithms are applied to an EV battery dataset, and results show improved prediction accuracy and reduced mean absolute error (MAE). Notably, Gaussian process regression (GPR), random forest (RF) regression, and XGBoost methods combined with the proposed feature selection method show the most significant accuracy improvement.

Original language	English
Title of host publication	12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	300-303
Number of pages	4
ISBN (Electronic)	9798350337938
DOIs	https://doi.org/10.1109/ICRERA59003.2023.10269362
State	Published - 2023
Event	12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 - Oshawa, Canada Duration: 29 Aug 2023 → 1 Sep 2023

Publication series

Name	12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023

Conference

Conference	12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023
Country/Territory	Canada
City	Oshawa
Period	29/08/23 → 1/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Battery Degradation
Calendar Loss Prediction
Electric Vehicles
Feature Selection
Machine Learning

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ICRERA59003.2023.10269362

Cite this

Rauf, H., Gul, M. S., Khalid, M., & Arshad, N. (2023). Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery. In 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 (pp. 300-303). (12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA59003.2023.10269362

Rauf, Huzaifa ; Gul, Muhammad Shuzub ; Khalid, Muhammad et al. / Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery. 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 300-303 (12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023).

@inproceedings{dd6d92067a5a4ace870bd50e4be5ca40,

title = "Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery",

abstract = "Li-ion batteries are widely used in electric vehicles (EVs), but they suffer from battery degradation, particularly in terms of calendar loss. Machine learning has been used to predict battery health deterioration due to cyclic loss, but the accuracy depends on input feature selection. This study introduces an improved feature selection method, enhancing battery calendar loss prediction. Eight machine learning algorithms are applied to an EV battery dataset, and results show improved prediction accuracy and reduced mean absolute error (MAE). Notably, Gaussian process regression (GPR), random forest (RF) regression, and XGBoost methods combined with the proposed feature selection method show the most significant accuracy improvement.",

keywords = "Battery Degradation, Calendar Loss Prediction, Electric Vehicles, Feature Selection, Machine Learning",

author = "Huzaifa Rauf and Gul, \{Muhammad Shuzub\} and Muhammad Khalid and Naveed Arshad",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

year = "2023",

doi = "10.1109/ICRERA59003.2023.10269362",

language = "English",

series = "12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "300--303",

booktitle = "12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023",

address = "United States",

}

Rauf, H, Gul, MS, Khalid, M & Arshad, N 2023, Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery. in 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023. 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 300-303, 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023, Oshawa, Canada, 29/08/23. https://doi.org/10.1109/ICRERA59003.2023.10269362

Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery. / Rauf, Huzaifa; Gul, Muhammad Shuzub; Khalid, Muhammad et al.
12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 300-303 (12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery

AU - Rauf, Huzaifa

AU - Gul, Muhammad Shuzub

AU - Khalid, Muhammad

AU - Arshad, Naveed

PY - 2023

Y1 - 2023

N2 - Li-ion batteries are widely used in electric vehicles (EVs), but they suffer from battery degradation, particularly in terms of calendar loss. Machine learning has been used to predict battery health deterioration due to cyclic loss, but the accuracy depends on input feature selection. This study introduces an improved feature selection method, enhancing battery calendar loss prediction. Eight machine learning algorithms are applied to an EV battery dataset, and results show improved prediction accuracy and reduced mean absolute error (MAE). Notably, Gaussian process regression (GPR), random forest (RF) regression, and XGBoost methods combined with the proposed feature selection method show the most significant accuracy improvement.

AB - Li-ion batteries are widely used in electric vehicles (EVs), but they suffer from battery degradation, particularly in terms of calendar loss. Machine learning has been used to predict battery health deterioration due to cyclic loss, but the accuracy depends on input feature selection. This study introduces an improved feature selection method, enhancing battery calendar loss prediction. Eight machine learning algorithms are applied to an EV battery dataset, and results show improved prediction accuracy and reduced mean absolute error (MAE). Notably, Gaussian process regression (GPR), random forest (RF) regression, and XGBoost methods combined with the proposed feature selection method show the most significant accuracy improvement.

KW - Battery Degradation

KW - Calendar Loss Prediction

KW - Electric Vehicles

KW - Feature Selection

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/85175641464

U2 - 10.1109/ICRERA59003.2023.10269362

DO - 10.1109/ICRERA59003.2023.10269362

M3 - Conference contribution

AN - SCOPUS:85175641464

T3 - 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023

SP - 300

EP - 303

BT - 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023

Y2 - 29 August 2023 through 1 September 2023

ER -

Rauf H, Gul MS, Khalid M, Arshad N. Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery. In 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 300-303. (12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023). doi: 10.1109/ICRERA59003.2023.10269362

Smart Feature Selection-Based Machine Learning Framework for Calendar Loss Prediction of Li-Ion Electric Vehicle Battery

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this