Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

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dc.authoridWang, Lihua/0000-0003-2962-6667
dc.authoridpang, hui/0000-0001-7550-8376
dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.contributor.authorZhang, Fengqi
dc.contributor.authorWang, Lihua
dc.contributor.authorCoskun, Serdar
dc.contributor.authorPang, Hui
dc.contributor.authorCui, Yahui
dc.contributor.authorXi, Junqiang
dc.date.accessioned2025-03-17T12:25:16Z
dc.date.available2025-03-17T12:25:16Z
dc.date.issued2020
dc.departmentTarsus Üniversitesi
dc.description.abstractHybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.
dc.description.sponsorshipNational Natural Science Foundation of China [51905419]; Natural Science Basic Research Program of Shaanxi [2019JQ-503]; Fundamental Research Fund for the Central Universities of China [300102229514, 300102229502]
dc.description.sponsorshipThis research was funded by National Natural Science Foundation of China (Grant No.51905419) and Natural Science Basic Research Program of Shaanxi (Grant No.2019JQ-503), and the Fundamental Research Fund for the Central Universities of China (grant No. 300102229514 and No.300102229502).
dc.identifier.doi10.3390/en13133352
dc.identifier.issn1996-1073
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85089913041
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/en13133352
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1591
dc.identifier.volume13
dc.identifier.wosWOS:000550372900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofEnergies
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectHybrid Electric Vehicles (HEVs)
dc.subjectenergy management strategies (EMSs)
dc.subjectdriving cycle prediction
dc.subjectoptimization
dc.titleEnergy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook
dc.typeReview

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