Model predictive control of a power-split hybrid electric vehicle system with slope preview

K. Yu, H. Yang, T. Kawabe, X. Tan

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper presents a model predictive control approach for the energy management problem of a power-split hybrid electric vehicle system with slope information. The new features of this study are as follows. First, the vehicle speed model which includes the slope information is incorporated in the full-order model of the power-split hybrid electric vehicle system. Second, it examines the effect of selecting the high-efficiency area rather than the area near the engine optimal operation line using the continuously variable transmission. Third, it uses logarithm functions for the state constraints and the state variant control input constraints of the optimal control problem. By analyzing the configuration of the power-split hybrid electric vehicle system, we developed a full-order model. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results showed that the fuel economy was much better using the model predictive control approach than the ADVISOR (ADvanced VehIcle SimulatOR) rule-based approach in two cases. We conclude that the model predictive control approach is effective for the application of power-split hybrid electric vehicle systems energy management with slope information.

Original languageEnglish
Pages (from-to)305-314
Number of pages10
JournalArtificial Life and Robotics
Volume20
Issue number4
DOIs
Publication statusPublished - Dec 1 2015

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Model predictive control of a power-split hybrid electric vehicle system with slope preview'. Together they form a unique fingerprint.

Cite this