Development of machine learning methods in hybrid energy storage systems in electric vehicles

dc.creatorTzu-Chia, Chen
dc.creatorAlazzawi, Fouad Jameel Ibrahim
dc.creatorGrimaldo Guerrero, John William
dc.creatorChetthamrongchai, Paitoon
dc.creatorDorofeev, Aleksei
dc.creatorAras masood, Ismael
dc.creatorAhmed, Dr. Alim Al Ayub
dc.creatorAkhmadeev, Ravil
dc.creatorLatipah, Asslia Johar
dc.creatorAbu Al-Rejal, Hussein
dc.date2022-04-05T12:48:49Z
dc.date2022-04-05T12:48:49Z
dc.date2022
dc.date.accessioned2023-10-03T19:52:48Z
dc.date.available2023-10-03T19:52:48Z
dc.identifier1024-123X
dc.identifierhttps://hdl.handle.net/11323/9115
dc.identifierhttps://doi.org/10.1155/2022/3693263
dc.identifier10.1155/2022/3693263
dc.identifier1563-5147
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9172922
dc.descriptionThe hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.
dc.format8 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherHindawi Publishing Corporation
dc.publisherUnited States
dc.relationMathematical Problems in Engineering
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dc.relation2022
dc.rightsCopyright © 2022 Tzu-Chia Chen et al.
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.hindawi.com/journals/mpe/2022/3693263/
dc.subjectMachine learning
dc.subjectHybrid energy
dc.subjectElectric vehicles
dc.subjectStorage systems
dc.titleDevelopment of machine learning methods in hybrid energy storage systems in electric vehicles
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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