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.2018.03.061Additional 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
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53001063
- Subject category
- S32: ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION;
- Descriptors DEI
- ALGORITHMS; DYNAMIC PROGRAMMING; ECONOMY; ENERGY EFFICIENCY; ENERGY MANAGEMENT; HYBRID ELECTRIC-POWERED VEHICLES; MARKOV PROCESS; MONTE CARLO METHOD
- Descriptors DEC
- CALCULATION METHODS; EFFICIENCY; ELECTRIC-POWERED VEHICLES; MANAGEMENT; MATHEMATICAL LOGIC; STOCHASTIC PROCESSES; VEHICLES
Optional Information
- Copyright
- Copyright (c) 2018 Elsevier Ltd. All rights reserved.