Review of battery state estimation methods for electric vehicles-Part II: SOH estimation

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dc.contributor.authorDemirci O.
dc.contributor.authorTaskin S.
dc.contributor.authorSchaltz E.
dc.contributor.authorAcar Demirci B.
dc.date.accessioned2024-07-22T08:01:13Z
dc.date.available2024-07-22T08:01:13Z
dc.date.issued2024
dc.description.abstractState of Health (SOH) significantly determines the performance and durability of EV batteries, with Battery Management System (BMS) playing a crucial role in enhancing their efficiency and operational cycle life. This comprehensive review, the second part of our series on Battery State Estimation Methods for Electric Vehicles, provides an in-depth exploration of SOH estimation methods. SOH, which encompasses a battery's overall health, capacity, and aging characteristics, plays a fundamental role in making informed decisions, conducting proactive maintenance, and ensuring the safe and reliable operation of EVs. Diverse SOH estimation methods, ranging from data-driven to model-based approaches, address the multifaceted challenges associated with battery aging, including electrochemical processes, temperature variations, usage patterns, and external factors. In recent years, data-driven methods, especially those rooted in machine learning and artificial intelligence, have gained prominence. These methods facilitate the discovery of complex models and correlations, encompassing battery degradation and using datasets to train algorithms. Machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL), have shown significant promise in estimating SOH by learning from historical data and adapting to varying operational conditions. The studies highlighted in this review demonstrate significant advancements in SOH estimation techniques, leading to improved accuracy, efficiency, and adaptability. These advances contribute to the development of more reliable BMSs for EVs and battery energy storage systems. © 2024 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.est.2024.112703
dc.identifier.issn2352152X
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11370
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectDeep learning
dc.subjectDigital storage
dc.subjectElectric vehicles
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectLithium-ion batteries
dc.subjectNeural networks
dc.subjectState estimation
dc.subjectSupport vector machines
dc.subjectBattery management system
dc.subjectCycle lives
dc.subjectElectric vehicle
dc.subjectElectric vehicle batteries
dc.subjectEstimation methods
dc.subjectOperational cycle
dc.subjectPerformance
dc.subjectState estimation methods
dc.subjectState of health
dc.subjectState of health estimation
dc.subjectBattery management systems
dc.titleReview of battery state estimation methods for electric vehicles-Part II: SOH estimation
dc.typeReview

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