Thermally degraded speed estimation of traction machine drive in electric vehicle

Seyd Muhammad Nawazish Ali, Md. Jahangir Hossain, Dong Wang, M. A. Parvez Mahmud*, Vivek Sharma, Muhammad Kashif, Abbas Z. Kouzani

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of H∞ control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up.
Original languageEnglish
JournalIET Electric Power Applications
Volume16
Issue number12
Pages (from-to)1464-1475
Number of pages12
ISSN1751-8660
DOIs
Publication statusPublished - Dec 2022

Keywords

  • electric vehicle
  • genetic algorithms
  • induction machine drive
  • linear parameter varying control
  • thermally degraded speed

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