Energy-efficient speed control of electric vehicles: linear matrix inequality approach

Mohammad Veysi, Jamshid Aghaei, Mokhtar Shasadeghi, Reza Razzaghi, Behrooz Bahrani, Daniel Joseph Ryan

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2 Citations (Scopus)


In this paper, a stable fuzzy controller in the form of linear matrix inequalities (LMIs) is designed for an electric vehicle (EV). The proposed controller evaluates the stabilization of the EV speed with robust disturbance rejection approach throughout both transient and steady states and in the presence of external disturbances and parametric uncertainties. In order to achieve an efficient control, EV's battery voltage, as control input, and EV speed, as system output, are constrained. The proposed control strategy is based on Takagi-Sugeno (T-S) fuzzy model and the parallel distributed compensation (PDC) fuzzy controller. Proposed controller can be practically implemented due to low computational load of control input that is owing to the offline process of obtaining the feedback gains, very small amplitude of control input and low power consumption. To evaluate the performance of the designed controller, simulations in five steps are conducted on an EV equipped with a brushed direct current (BDC) motor as a case study in MATLAB simulation environment. Ultimately, the real-time digital simulation results using real-time digital power system simulator (RTDS) confirm the efficiency of the proposed input-output constrained robust disturbance rejection stable fuzzy controller (IOCRDRSFC) in stabilizing the EV speed. The simulation results obtained from MATLAB and the RTDS confirm the energy-efficient and robust performance of the proposed controller in quick stabilization of the EV speed in the presence of all structured and unstructured uncertainties.

Original languageEnglish
Pages (from-to)10469-10483
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Issue number10
Publication statusPublished - Oct 2020


  • Electric vehicle (EV)
  • input-output constrained robust disturbance rejection stable fuzzy control (IOCRDRSFC)
  • linear matrix inequality (LMI)
  • parallel distributed compensation (PDC)
  • Takagi-Sugeno (T-S) fuzzy model

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