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

Yuanjian Zhang, Yonggang Liu, Yanjun Huang, Zheng Chen, Guang Li, Wanming Hao, Geoff Cunningham, Juliana Early

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number120631
JournalEnergy
Volume228
Early online date15 Apr 2021
DOIs
Publication statusEarly online date - 15 Apr 2021

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