Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles. (1st June 2018)

• Record Type: Journal Article
• Authors: Hongwen, He; Jinquan, Guo; Jiankun, Peng; Huachun, Tan; Chao, Sun
• Is Part Of: Energy. Volume 152(2018)
• Page Start: 95
• Page End: 107
• Publication Date: 2018-06-01
• Subjects: Global driving cycle -- Traffic information -- Tensor completion -- Dynamic programming -- EDPS -- PHEV; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.03.061
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a global driving cycle construction method based on the real-time traffic information, which can realize online optimal energy management for plug-in hybrid electric vehicles (PHEVs). The construction method is mainly divided into three parts: the construction of velocity segments database; the construction of real-time traffic information tensor model database, and the construction of real-time global driving cycle. For the acquisition of the real-time traffic information, a two-step completion method is adopted to obtain the complete and accuracy traffic information; for the driving cycle construction, the velocity segment database, the road section velocity and the Markov transfer matrix with Monte Carlo are used to generate velocity segments which constitute the global driving cycle. With the updated real-time traffic information, the global driving cycle is reconstructed which further reflect the real-time road condition. The efficient dynamic programming (DP) algorithm is applied to realize online energy management in PHEVs. Its simulation shows that the fuel efficiency improves by at least 19.83% compared with charge depleting and charge sustain (CDCS) control strategy. Finally, the economy driving pro system (EDPS) is presented in this paper, and it contributes 5.79% fuel efficiency compared with non-EDPS. Highlights: Tensor model based global driving cycle dynamic reconstruction method. Missing traffic information completion improves driving cycle precision. Economy Driving Pro System for the Plug-in Hybrid Electric Vehicle.