An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information. (1st January 2020)

• Record Type: Journal Article
• Authors: Lei, Zhenzhen; Qin, Datong; Hou, Liliang; Peng, Jingyu; Liu, Yonggang; Chen, Zheng
• Is Part Of: Energy. Volume 190(2020)
• Publication Date: 2020-01-01
• Subjects: Adaptive equivalent consumption minimization strategy (A-ECMS) -- Plug-in hybrid electric vehicles (PHEVs) -- Reference state of charge (SOC) trajectory -- Traffic information; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116409
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Energy management strategies play an important role in performance optimization of plug-in hybrid electric vehicles (PHEVs), and can be further improved by incorporating external traffic information. Motivated by this, an adaptive equivalent consumption minimization strategy considering traffic information is proposed in this study to facilitate the effective energy management of PHEVs. First, the initial equivalent factors in terms of different initial state of charge (SOC) and driving distance are searched by genetic algorithm. Then, the simplified dynamic programming is leveraged to determine the optimal SOC trajectory according to the traffic information with fast calculation speed. The fuzzy controller is employed to regulate the equivalent factor dynamically, thus enabling effective tracking of the reference SOC trajectory. A hardware-in-the-loop simulation platform based on the virtual scene is developed to validate the performance of controller. Simulation and experimental results highlight that the proposed strategy can lead to less fuel consumption, compared to traditional equivalent consumption minimization strategy, thereby proving its feasibility. Highlights: The reference state of charge trajectory is planned by simplified dynamic programming. The equivalent factor is searched by genetic algorithm considering traffic information. Adaptive correction of the equivalent factor is achieved by fuzzy logic control.