Published June 2018 | Version v1
Journal article

Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles

  • 1. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing, 100081 (China)
  • 2. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081 (China)

Description

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. 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.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.energy.2018.03.061

Additional details

Identifiers

DOI
10.1016/j.energy.2018.03.061;
PII
S0360544218304699;

Publishing Information

Journal Title
Energy (Oxford)
Journal Volume
152
Journal Page Range
p. 95-107
ISSN
0360-5442
CODEN
ENEYDS

INIS

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Copyright
Copyright (c) 2018 Elsevier Ltd. All rights reserved.