Energy management and driving strategy for in-wheel motor electric ground vehicles with terrain profile preview

Yan Chen, Xiaodong Li, Christopher Wiet, Junmin Wang

Research output: Contribution to journalArticle

68 Scopus citations

Abstract

This paper presents a terrain-information- and actuator-efficiency- incorporated energy management and driving strategy (EMDS) for maximizing the travel distance of in-wheel motor, pure electric ground vehicles (EGVs). Minimization of energy consumption for a certain trip with terrain preview based on the operating efficiencies of in-wheel motors and a traffic model is essential to maximize the total travel distances of an EGV. Unlike conducting energy optimization under given vehicle speed profiles that are specified a priori in most literature, the optimally varied vehicle velocity and globally optimal in-wheel motor actuation torque distributions are simultaneously obtained to minimize the EGV energy consumption by employing the dynamic programming method for the first time. As a comparison, CarSim-matlab/Simulink co-simulation results based on a model predictive control design are displayed to not only validate that the energy optimization results from the EMDS design is a benchmark with the least power consumption, but also to show that the driving strategy derived from the EMDS can be potentially utilized as an energy-optimal speed reference for other real-time implementable methods.

Original languageEnglish (US)
Article number6657818
Pages (from-to)1938-1947
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume10
Issue number3
DOIs
StatePublished - Aug 2014
Externally publishedYes

Keywords

  • Driving strategy
  • dynamic programming
  • electric ground vehicle (EGV)
  • energy management
  • in-wheel motor
  • model predictive control (MPC)
  • terrain preview

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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