Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28672
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dc.contributor.authorRen, X-
dc.contributor.authorLai, CS-
dc.contributor.authorTaylor, G-
dc.date.accessioned2024-04-02T12:35:44Z-
dc.date.available2024-04-02T12:35:44Z-
dc.date.issued2023-08-30-
dc.identifierORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438-
dc.identifierORCiD: Gareth Taylor https://orcid.org/0000-0003-0867-2365-
dc.identifier.citationRen, X., Lai, C.S. and Taylor, G. (2023) 'A Critical Evaluation of Eco-Driving Strategies for Connected Autonomous Electric Vehicles at Signalized Intersections', 58th International Universities Power Engineering Conference, UPEC 2023, Dublin, Ireland, 30 August - 01 September, pp. 1 - 6. doi: 10.1109/UPEC57427.2023.10294498.en_US
dc.identifier.isbn979-8-3503-1683-4 (ebk)-
dc.identifier.isbn979-8-3503-1684-1 (PoD)-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/28672-
dc.description.abstractSignalized intersections are significant spots of energy consumption because of frequent stop-and-go behavior. Eco-driving aims to reduce energy usage by optimizing driving behavior. Researchers have reviewed optimization-based method while lack of them reviewed the learning-based approaches. This work critically reviewed two different types of approach. In addition, one well-known rule-based car-following model and two state-of-the-art optimization-based and learning-based methods are selected to test in a signalized intersections environment with the metrics of energy consumption, travelling time and algorithm execution time. The experiment results show that the travelling time of three algorithms are similar, while the energy consumption of the learning-based method and optimization-based method are 30.72% and 51.82% less than that of the rule-based method respectively. However, due to algorithm execution time, the optimization-based method is not suitable to be used in real-time.en_US
dc.format.extent1 - 6-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information.-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectconnected autonomous electric vehiclesen_US
dc.subjecteco-driving strategyen_US
dc.subjectsignalized intersectionsen_US
dc.titleA Critical Evaluation of Eco-Driving Strategies for Connected Autonomous Electric Vehicles at Signalized Intersectionsen_US
dc.typeConference Paperen_US
dc.identifier.doihttps://doi.org/10.1109/UPEC57427.2023.10294498-
dc.relation.isPartOf58th International Universities Power Engineering Conference, UPEC 2023-
pubs.publication-statusPublished-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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