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State of charge estimation for electric vehicles using random forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precis...

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Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa
Format: Article
Language:English
English
Published: Elsevier 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41124/1/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf
http://umpir.ump.edu.my/id/eprint/41124/7/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf
http://umpir.ump.edu.my/id/eprint/41124/
https://doi.org/10.1016/j.geits.2024.100177
https://doi.org/10.1016/j.geits.2024.100177
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spelling my.ump.umpir.411242024-11-18T07:19:29Z http://umpir.ump.edu.my/id/eprint/41124/ State of charge estimation for electric vehicles using random forest Mohd Herwan, Sulaiman Zuriani, Mustaffa TK Electrical engineering. Electronics Nuclear engineering This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.9028% compared to 6.3127% for ELM, and a lower Mean Absolute Error (MAE) of 4.4321% versus 5.1112% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation. Elsevier 2024-10 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41124/1/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41124/7/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2024) State of charge estimation for electric vehicles using random forest. Green Energy and Intelligent Transportation, 3 (5). pp. 1-39. ISSN 2773-1537. (Published) https://doi.org/10.1016/j.geits.2024.100177 https://doi.org/10.1016/j.geits.2024.100177
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
State of charge estimation for electric vehicles using random forest
description This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.9028% compared to 6.3127% for ELM, and a lower Mean Absolute Error (MAE) of 4.4321% versus 5.1112% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.
format Article
author Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_facet Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_sort Mohd Herwan, Sulaiman
title State of charge estimation for electric vehicles using random forest
title_short State of charge estimation for electric vehicles using random forest
title_full State of charge estimation for electric vehicles using random forest
title_fullStr State of charge estimation for electric vehicles using random forest
title_full_unstemmed State of charge estimation for electric vehicles using random forest
title_sort state of charge estimation for electric vehicles using random forest
publisher Elsevier
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41124/1/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf
http://umpir.ump.edu.my/id/eprint/41124/7/State%20of%20charge%20estimation%20for%20electric%20vehicles%20using%20random%20forest.pdf
http://umpir.ump.edu.my/id/eprint/41124/
https://doi.org/10.1016/j.geits.2024.100177
https://doi.org/10.1016/j.geits.2024.100177
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score 13.232414

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