Review of battery state estimation methods for electric vehicles - Part I: SOC estimation

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dc.contributor.authorDemirci O.
dc.contributor.authorTaskin S.
dc.contributor.authorSchaltz E.
dc.contributor.authorAcar Demirci B.
dc.date.accessioned2024-07-22T08:01:21Z
dc.date.available2024-07-22T08:01:21Z
dc.date.issued2024
dc.description.abstractThis study presents a comprehensive review of State of Charge (SOC) estimation methods for Lithium-Ion (Li-Ion) batteries, with a specific focus on Electric Vehicles (EVs). The growing interest in EVs and the need for efficient battery management have driven advancements in SOC estimation techniques. Various approaches, including data-driven techniques, advanced filtering methods, and machine learning algorithms have been explored to enhance SOC estimation accuracy. The integration of artificial intelligence and hybrid models has shown promising results in improving SOC estimation performance. However, challenges remain in dealing with non-linear battery behavior, temperature variations, and diverse operating conditions. Researchers are continuously studying to improve the robustness and adaptability of SOC estimation methods to address these challenges. The primary objective of this study is to provide an up-to-date summary of the latest advancements in SOC estimation, offering insights into innovative approaches and developments in this field. All existing SOC methods, their advantages, challenges, and usage rates have been comprehensively examined with a specific focus on EV battery management systems. As the EV market continues to expand, accurate SOC estimation will remain essential for optimal battery management and overall EV performance. Future research will focus on refining existing algorithms, exploring new data-driven techniques, and integrating advanced sensor technologies to achieve real-time and reliable SOC estimation in EVs. © 2024 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.est.2024.111435
dc.identifier.issn2352152X
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11409
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectCharging (batteries)
dc.subjectElectric vehicles
dc.subjectIons
dc.subjectLearning algorithms
dc.subjectLithium compounds
dc.subjectLithium-ion batteries
dc.subjectMachine learning
dc.subjectBattery Management
dc.subjectBattery management system
dc.subjectData driven technique
dc.subjectElectric vehicle
dc.subjectEstimation methods
dc.subjectEstimation techniques
dc.subjectFiltering method
dc.subjectLithium ions
dc.subjectState estimation methods
dc.subjectState-of-charge estimation
dc.subjectBattery management systems
dc.titleReview of battery state estimation methods for electric vehicles - Part I: SOC estimation
dc.typeReview

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