Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC...
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my.uniten.dspace-251442023-05-29T16:06:57Z Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques Hannan M.A. Lipu M.S.H. Hussain A. Ker P.J. Mahlia T.M.I. Mansor M. Ayob A. Saad M.H. Dong Z.Y. 7103014445 36518949700 57208481391 37461740800 56997615100 6701749037 26666566900 7202075525 56608244300 State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions. � 2020, The Author(s). Final 2023-05-29T08:06:57Z 2023-05-29T08:06:57Z 2020 Article 10.1038/s41598-020-61464-7 2-s2.0-85081926043 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081926043&doi=10.1038%2fs41598-020-61464-7&partnerID=40&md5=115a4af10b3e38c0fb7558ab581a49ab https://irepository.uniten.edu.my/handle/123456789/25144 10 1 4687 All Open Access, Gold, Green Nature Research Scopus |
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State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions. � 2020, The Author(s). |
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7103014445 Hannan M.A. Lipu M.S.H. Hussain A. Ker P.J. Mahlia T.M.I. Mansor M. Ayob A. Saad M.H. Dong Z.Y. |
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Hannan M.A. Lipu M.S.H. Hussain A. Ker P.J. Mahlia T.M.I. Mansor M. Ayob A. Saad M.H. Dong Z.Y. |
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Hannan M.A. Lipu M.S.H. Hussain A. Ker P.J. Mahlia T.M.I. Mansor M. Ayob A. Saad M.H. Dong Z.Y. Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
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Hannan M.A. |
title |
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
title_short |
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
title_full |
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
title_fullStr |
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
title_full_unstemmed |
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques |
title_sort |
toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques |
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Nature Research |
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2023 |
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1806424440763842560 |