Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions

A novel framework is presented in which the energy consumption of an electric vehicle (EV) or the zero-emissions range of a plug-in hybrid electric vehicle (PHEV) may be predicted over a route. The proposed energy prediction framework employs a neural network and may be used `off-line' to better estimate the real-world range of the vehicle, or 'on-line' integrated within the vehicle's energy management control system. The authors propose that this approach provides a more robust representation of the energy consumption of the target EVs compared to standard legislative test procedures. This is particularly pertinent for vehicle fleet operators that may use EVs within a specific environment, such as inner-city public transport, or the use of urban delivery vehicles. Experimental results highlight variations in EV range in the order of 50% when different levels of traffic congestion and road type are included in the analysis. The ability to estimate the energy requirements of the vehicle over a given route is also a prerequisite for using an efficient charge blended control strategy within a PHEV. Experimental results show an accuracy within 20-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys.

• Record URL:
  https://doi.org/10.1049/iet-its.2012.0114
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Authors:
  • Shankar, Ravi
  • Marco, James
• Publication Date: 2013-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 138-150
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 7
  • Issue Number: 1
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Electric vehicles; Energy consumption; Energy storage devices; Mathematical prediction; Plug-in hybrid vehicles; Vehicle performance; Vehicle range
• Subject Areas: Energy; Highways; Vehicles and Equipment; I15: Environment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01484250
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 19 2013 8:32AM