Loss model based controller of fuzzy DTC driven induction motor for electric vehicles using optimal stator flux

Abstract

Adoption of electric vehicles (EVs) is playing a major role in achieving green energy; however, this suffers from energy density range anxiety. Induction motors (IM) are designed to operate most efficiently under rated conditions. However, for EV applications, it operates at partial load at a constant speed, which reduces its efficiency when operated with nominal flux. In the long run, a lot of battery energy is consumed, which increases the running cost of the vehicle. This paper presents an energy efficient motor drive for EV applications with the objective of reducing battery energy consumption by an optimal reference flux. The optimal value of the stator flux reduces the difference between constant and variable losses, which improves the efficiency and driving range of the vehicle. The optimal flux selection strategies used in the current literature are mostly based on a search control approach, which has convergence issues, is highly complex, and is difficult to implement. In this paper, the Teamwork optimization algorithm (TOA) is used for optimal flux selection under a dynamic driving scenario. The method is examined with realistic EV data under various operating conditions and validated using standard drive cycles. The analysis of efficiency, energy savings, and power input confirms the superiority of the proposed method over other existing methods.