Predictive control of commercial e-vehicle using a priori route information

The driving range of the vehicle is usually an issue due to the limited energy storage capacity of the acu-pack. Thus, the e-vehicle control towards energy consumption decrease is of extreme importance. The known information about route properties can be used to plan torque/braking profile in an optimal way. Several approaches are compared. The first is design approach based on model predictive control (MPC) in combination with prior (before the trip starts) dynamic optimisation, the other is model-predictive control using hard limits based on route shape analyses and legal limits. The classical, optimised PID control is used as reference driver. A detailed driving range estimation model of a Fiat Doblo e-vehicle is the basis, including the main e-vehicle subsystem 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. The model calibration is based on real vehicle measurements.

• Record URL:
  • https://doi.org/10.1504/IJPT.2018.090362
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17424267
• Supplemental Notes:
  • Copyright © 2018 Inderscience Enterprises Ltd.
• Authors:
  • Steinbauer, Pavel
  • Husák, Josef
  • Pasteur, Florent
  • Denk, Petr
  • Macek, Jan
  • Šika, Zbynek
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 53-71
• Serial:
  • International Journal of Powertrains
  • Volume: 7
  • Issue Number: 1-3
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1742-4267
  • EISSN: 1742-4275
  • Serial URL: https://www.inderscience.com/jhome.php?jcode=ijpt

Subject/Index Terms

• TRT Terms: Commercial vehicle operations; Control systems; Electric vehicles; Energy conservation; Routes and routing; Vehicle range
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01672321
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 19 2018 9:33AM