Hierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios

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dc.authoridZhang, Fengqi/0000-0001-9811-2593
dc.authoridLangari, Reza/0000-0001-7900-5186
dc.contributor.authorPeng, Jinghui
dc.contributor.authorZhang, Fengqi
dc.contributor.authorCoskun, Serdar
dc.contributor.authorHu, Xiaosong
dc.contributor.authorYang, Yalian
dc.contributor.authorLangari, Reza
dc.contributor.authorHe, Jinsong
dc.date.accessioned2025-03-17T12:25:38Z
dc.date.available2025-03-17T12:25:38Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractConnected and automated vehicle technology via vehicle-to-everything communication, can assist in improving energy efficiency for hybrid electric vehicles (HEVs). In particular, information about the timing of traffic lights and surrounding vehicles can be exchanged between traffic vehicles and in conjunction with vehicle state information, to improve the fuel economy of HEVs significantly. To this end, we propose a multi-lane hierarchical optimization (MLHO) algorithm based on a predictive control framework. The dynamic behaviors of the surrounding vehicles are first predicted, and then the traffic light information (e.g., signal phasing and timing) and vehicles' state information are utilized in the design. MLHO is a two-level strategy wherein a multi-lane speed planning method for a host vehicle is formulated to plan the optimal speed and lane-change behaviors by considering vehicle power demand, driving comfort, and safety in the upper level. In the lower level, dynamic programming is adopted to devise energy management by tracking the optimal speed. Simulation results under real routes using the traffic simulation software Simulation of Urban Mobility show that the fuel economy of MLHO is improved by 32% on average compared to speed profile driven by a human driver model. In addition, traffic efficiency is enhanced significantly, i.e., different traffic occupancy results on the road indicate that the proposed MLHO is less affected by the traffic flow density. With different traffic densities, the maximum fuel consumption difference under the three considered scenarios is only 0.645L/100km.
dc.description.sponsorshipNational Key Research and Development Program of China [2021YFE0193800]; National Natural Science Foundation of China [51905419]; State Key Laboratory Research Project [SKLMT-ZZKT-2022M09]; Talent Plan Project of Chongqing [cstc2021ycjh-bgzxm0295]
dc.description.sponsorshipThis work was supported in part by the National Key Research and Development Program of China under Grant2021YFE0193800, in part by the National Natural Science Foundation ofChina under Grant 51905419, in part by the State Key Laboratory ResearchProject under Grant SKLMT-ZZKT-2022M09, and in part by the Talent PlanProject of Chongqing under Grant cstc2021ycjh-bgzxm0295. The Associate Editor for this article was C. Lv.(Jinghui Peng and Fengqi Zhang contributedequally to this work.)
dc.identifier.doi10.1109/TITS.2023.3305491
dc.identifier.endpage14188
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85170557880
dc.identifier.scopusqualityQ1
dc.identifier.startpage14174
dc.identifier.urihttps://doi.org/10.1109/TITS.2023.3305491
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1790
dc.identifier.volume24
dc.identifier.wosWOS:001060565100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Intelligent Transportation Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectEnergy management
dc.subjecthybrid electric vehicle
dc.subjectspeed planning
dc.subjectmodel predictive control
dc.subjectconnected vehicles
dc.subjectsignal phase and timing
dc.titleHierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios
dc.typeArticle

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