An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles

被引:19
|
作者
Zhang, Yuanjian [1 ]
Liu, Yonggang [2 ,3 ]
Huang, Yanjun [4 ]
Chen, Zheng [5 ,6 ]
Li, Guang [6 ]
Hao, Wanming [7 ]
Cunningham, Geoff [1 ]
Early, Juliana [1 ]
机构
[1] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AG, Antrim, North Ireland
[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[3] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
[4] Tongji Univ, Sch Automot Studies, Shanghai 12476, Peoples R China
[5] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China
[6] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England
[7] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450003, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金; 英国工程与自然科学研究理事会; 欧盟地平线“2020”;
关键词
Optimal control strategy; Plug-in hybrid electric vehicle (PHEV); Internet of vehicles (IoVs); Mobile edge computing (MEC); sequential quadratic programming (CPSO-SQP); Alternative iterative optimization algorithm; (AIOA); ENERGY MANAGEMENT STRATEGY; EDGE; SQP;
D O I
10.1016/j.energy.2021.120631
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper presents an approach to the design of an optimal control strategy for plug-in hybrid electric vehicles (PHEVs) incorporating Internet of Vehicles (IoVs). The optimal strategy is designed and implemented by employing a mobile edge computing (MEC) based framework for IoVs. The thresholds in the optimal strategy can be instantaneously optimized by chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP) in the mobile edge computing units (MECUs). The vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are adopted in IoV to collect traffic information for a CPSO-SQP based optimization and transmit the optimized control commands to vehicle from MECUs. To guarantee real-time optimal performance, the communication delay in V2V and V2I is decreased via an alternative iterative optimization algorithm (AIOA) approach. The simulation results demonstrate the superior performance of the novel optimal control strategy for PHEV with 9% improvement, compared with the original strategy. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:13
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