Electric vehicles’ energy consumption estimation with real driving condition data

The use of electric vehicles (EVs) is viewed as an attractive option to reduce CO2 emissions and fuel consumption resulted from transport sector, but the popularization of EVs has been hindered by the cruising range limitation and the charging process inconvenience. Energy consumption characteristics analysis is the important foundation to study charging infrastructures locating, eco-driving behavior and energy saving route planning, which are helpful to extend EVs’ cruising range. From a physical and statistical view, this paper aims to develop a systematic energy consumption estimation approach suitable for EV actual driving cycles. First, by employing the real second-by-second driving condition data collected on typical urban travel routes, the energy consumption characteristics analysis is carried out specific to the microscopic driving parameters (instantaneous speed and acceleration) and battery state of charge (SOC). Then, based on comprehensive consideration of the mechanical dynamics characteristics and electric machine system of the EVs, a set of energy consumption rate estimation models are established under different operation modes from a statistical perspective. Finally, the performance of proposed model is fully evaluated by comparing with a conventional energy consumption estimation method. The results show that the proposed modeling approach represents a significant accuracy improvement in the estimation of real-world energy consumption. Specifically, the model precision increases by 25.25% in decelerating mode compared to the conventional model, while slight improvement in accelerating and cruising mode with desirable goodness of fit.

• Record URL:
  https://doi.org/10.1016/j.trd.2015.10.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1361920915001625
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Rui
  • Yao, Enjian
• Publication Date: 2015-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 177-187
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 41
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Electric vehicles; Energy consumption; Estimation theory; Kinetic energy; Microscopic traffic flow; Regenerative braking
• Uncontrolled Terms: Real world data
• Subject Areas: Energy; Highways; Vehicles and Equipment; I15: Environment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01583903
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 23 2015 8:12AM