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

Simple item page
dc.contributor.authorDemirci, O
dc.contributor.authorTaskin, S
dc.contributor.authorSchaltz, E
dc.contributor.authorDemirci, BA
dc.date.accessioned2024-07-18T11:51:10Z
dc.date.available2024-07-18T11:51:10Z
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 nonlinear 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.
dc.identifier.issn2352-152X
dc.identifier.other2352-1538
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/4662
dc.language.isoEnglish
dc.publisherELSEVIER
dc.subjectOF-CHARGE ESTIMATION
dc.subjectLITHIUM-ION BATTERIES
dc.subjectELECTROCHEMICAL IMPEDANCE SPECTROSCOPY
dc.subjectCOULOMB COUNTING METHOD
dc.subjectREAL-TIME STATE
dc.subjectMANAGEMENT-SYSTEM
dc.subjectHEALTH ESTIMATION
dc.subjectNEURAL-NETWORKS
dc.subjectKALMAN FILTER
dc.subjectONLINE STATE
dc.titleReview of battery state estimation methods for electric vehicles - Part I: SOC estimation
dc.typeReview

Files

Collections

Web of Science Koleksiyonu