Energy management strategy based on GIS information and MPC for a heavy-duty dual-mode power-split HEV

Hui Liu, Xunming Li, Weida Wang, Yang Wang, Lijin Han, Wei Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

The control performance of energy management strategy (EMS) in heavy-duty dual-mode power-split hybrid electric vehicles (PSHEV) are highly dependent on the forecasted velocity and battery state of charge (SOC) planning. In this paper, a model predictive control (MPC)-based energy management strategy is proposed, in which the predicted velocity and SOC trajectory is regarded as reference signal. The velocity predictor is designed based on radial basis function neural network (RBF-NN), and the battery SOC trajectory is planned using the road grade information from Geographic Information System (GIS). The proposed strategy is verified by a Matlab/simulink model. The results indicate that the fuel economy of PSHEV is improved by considering velocity prediction and SOC trajectory planning.

Original languageEnglish
Title of host publicationICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages380-385
Number of pages6
ISBN (Electronic)9781538670668
DOIs
Publication statusPublished - 11 Jan 2019
Event3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018 - Singapore, Singapore
Duration: 18 Jul 201820 Jul 2018

Publication series

NameICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics

Conference

Conference3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
Country/TerritorySingapore
CitySingapore
Period18/07/1820/07/18

Keywords

  • EMS
  • gIS
  • hEV
  • mPC
  • sOC trajectory planning

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