Comparative Evaluation for Torque Control Strategies of Interior Permanent Magnet Synchronous Motor for Electric Vehicles

Abstract

This paper presents a detailed analysis and comparative investigation for the torque control techniques of interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs). The study involves the field-oriented control (FOC), direct torque control (DTC), and model predictive direct torque control (MPDTC) techniques. The control aims to achieve vehicle requirements that involve maximum torque per ampere (MTPA), minimum torque ripples, maximum efficiency, fast dynamics, and wide speed range. The MTPA is achieved by the direct calculation of reference flux-linkage as a function of commanded torque. The calculation of reference flux-linkage is done online by the solution of a quartic equation. Therefore, it is a more practical solution compared to look-up table methods that depend on machine parameters and require extensive offline calculations in advance. For realistic results, the IPMSM model is built considering iron losses. Besides, the IGBTs and diodes losses (conduction and switching losses) in power inverter are modeled and calculated to estimate properly total system efficiency. In addition, a bidirectional dc-dc boost converter is connected to the battery to improve the overall drive performance and achieve higher efficiency values. Also, instead of the conventional PI controller which suffers from parameter variation, the control scheme includes an adaptive fuzzy logic controller (FLC) to provide better speed tracking performance. It also provides a better robustness against disturbance and uncertainties. Finally, a series of simulation results with detailed analysis are executed for a 60 kW IPMSM. The electric vehicle (EV) parameters are equivalent to Nissan Leaf 2018 electric car.

Improvement DTC for Induction Motor Drives Using Modern Speed Controllers Tuning by PSO Algorithm

“…The expressions of the stator voltages allow us to calculate, in real time and at any time, the flux and torque magnitudes, using Eqs. ( 5) and (6).…”

“…Recently, artificial intelligence and nonlinear techniques such as fuzzy logic and controllers based on high-order sliding mode theory have received increasing attention in solving complex and practical problems; they are widely applied in the electrical machine domain [6][7][8].…”

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles

“…The cost function g can be extended by adding the current magnitude limitation to avoid overcurrent. This term is defined as follows in Equation (29) [ 32 ]: …”

“…Fuzzy logic and artificial neural networks are also used as artificial intelligence controllers [ 29 , 30 ]. Authors in [ 31 , 32 , 33 , 34 ] have proposed a Model Predictive Direct Torque Control strategy that is based on predicting the control variables such as flux and torque while minimizing the error of the predicted control variables; this improves control accuracy while maintaining the control system’s simplicity.…”

Direct Torque Control for Permanent Magnet Synchronous Motor Using Golden Eagle Optimized ANFIS

“…The following key issues arise as a result of this control strategy: torque ripple, unregulated moving regularity, and susceptibility to resistance fluctuation. Artificial intelligence can help tackle these issues and improve induction machine DTC [6].…”