Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information

This paper proposes a two-layer structure Internet-distributed energy management strategy (ID-EMS) for connected plug-in hybrid electric vehicles (PHEVs) to deal with two significant challenges in the real-time global optimization process. One is that the traffic flow information from the commercial...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 25; H. 12; S. 20032 - 20042
Hauptverfasser:	Zhang, Yi, Xu, Shiyu, Song, Yize, Qi, Wenjie, Guo, Qiang, Li, Xu, Kong, Linli, Chen, Jiahao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2024
Schlagworte:	Batteries Cloud computing Connected plug-in hybrid electric vehicles driving condition prediction Energy management Fuel cells Fuels Heuristic algorithms internet-distributed energy management strategy Optimization Prediction algorithms real-time performance analysis Real-time systems State of charge vehicle in the loop simulation
ISSN:	1524-9050, 1558-0016
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Abstract	This paper proposes a two-layer structure Internet-distributed energy management strategy (ID-EMS) for connected plug-in hybrid electric vehicles (PHEVs) to deal with two significant challenges in the real-time global optimization process. One is that the traffic flow information from the commercial intelligent transportation system (ITS) is insufficient to accurately predict future driving conditions, which is alleviated by introducing the computer vision-based detection method for traffic flow density. The other is the conflict between global optimality and real-time capability, which the algorithm complexity analysis solves. Namely, the maximum problem size of the global optimization under a given computing power is derived to ensure real-time capability. Finally, an Internet-distributed vehicle-in-the-loop (ID-VIL) simulation platform is introduced to evaluate the proposed ID-EMS's feasibility through an on-road driving experiment. Some extreme conditions, such as heavy calculation load and network failure, are also tested.
AbstractList	This paper proposes a two-layer structure Internet-distributed energy management strategy (ID-EMS) for connected plug-in hybrid electric vehicles (PHEVs) to deal with two significant challenges in the real-time global optimization process. One is that the traffic flow information from the commercial intelligent transportation system (ITS) is insufficient to accurately predict future driving conditions, which is alleviated by introducing the computer vision-based detection method for traffic flow density. The other is the conflict between global optimality and real-time capability, which the algorithm complexity analysis solves. Namely, the maximum problem size of the global optimization under a given computing power is derived to ensure real-time capability. Finally, an Internet-distributed vehicle-in-the-loop (ID-VIL) simulation platform is introduced to evaluate the proposed ID-EMS's feasibility through an on-road driving experiment. Some extreme conditions, such as heavy calculation load and network failure, are also tested.
Author	Xu, Shiyu Li, Xu Qi, Wenjie Song, Yize Guo, Qiang Chen, Jiahao Zhang, Yi Kong, Linli
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SubjectTerms	Batteries Cloud computing Connected plug-in hybrid electric vehicles driving condition prediction Energy management Fuel cells Fuels Heuristic algorithms internet-distributed energy management strategy Optimization Prediction algorithms real-time performance analysis Real-time systems State of charge vehicle in the loop simulation
Title	Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information
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