Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Basit Öğe Kaydı
dc.authoridBicer, Emre/0000-0002-9871-4102
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridDOGAN, FERDI/0000-0002-9203-697X
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.contributor.authorOyucu, Saadin
dc.contributor.authorDogan, Ferdi
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorBicer, Emre
dc.date.accessioned2024-10-26T18:07:59Z
dc.date.available2024-10-26T18:07:59Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods' performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.
dc.description.sponsorshipEuropean Union's Horizon Europe Research and Innovation Program under the Next Generation of Multifunctional, Modular and Scalable Solid State Batteries System (EXTENDED); Battery Research Group at Sivas University of Science and Technology
dc.description.sponsorshipThe research was conducted collaboratively by the MOBILERS team at Sivas Cumhuriyet University and the Battery Research Group at Sivas University of Science and Technology. The authors also acknowledge Halil Sar & imath; for his English proofreading and editing assistance.
dc.identifier.doi10.3390/app14062306
dc.identifier.issn2076-3417
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85190277691
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/app14062306
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29762
dc.identifier.volume14
dc.identifier.wosWOS:001191774300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectmachine learning
dc.subjectstate of health
dc.subjectLi-ion
dc.subjectbattery
dc.subjectelectrical vehicle
dc.titleComparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu