Model predictive control for active vibration suppression of hybrid electric vehicles during mode transition

Ying Huang*, Jian Wang, Yunpeng Yue, Long Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

To realize a rapid and comfortable mode transition process from pure electric mode to hybrid mode (E-H), this study proposes an innovative control strategy which combines open-loop control with model predictive control to regulate the E-H process for a P2.5 configuration hybrid electric vehicle. Firstly, a detailed vehicle longitudinal dynamic model is established and the mode transition process is divided into four phases to reveal the control problems. Based on this model, a strategy combining open-loop control with model predictive control is developed. The open-loop control is adopted before the clutch is locked in order to speed up the transition process and limit the vehicle jerk. The model predictive control (MPC) is adopted after the clutch is locked to actively suppress the vibration caused by the abrupt change of clutch torque at the moment of clutch lock-up. Finally, simulation and hardware-in-the-loop test demonstrate that the proposed strategy for mode transition can achieve both switching rapidity and riding comfort. The algorithm robustness is also discussed and the signal transmission delay influence caused by controller area network (CAN) is studied.

Original languageEnglish
Pages (from-to)2819-2830
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Volume237
Issue number12
DOIs
Publication statusPublished - Oct 2023

Keywords

  • HIL test
  • Mode transition
  • P2.5 configuration hybrid electric vehicle
  • abrupt change of clutch torque
  • model predictive control

Fingerprint

Dive into the research topics of 'Model predictive control for active vibration suppression of hybrid electric vehicles during mode transition'. Together they form a unique fingerprint.

Cite this