Open AccessComparative Evaluation for Torque Control Strategies of Interior Permanent Magnet Synchronous Motor for Electric Vehicles
Author(s) -
Hanaa Elsherbiny,
Mohamed Kamal Ahmed,
Mahmoud A. Elwany
Publication year - 2021
Publication title -
periodica polytechnica. electrical engineering and computer science
Language(s) - English
Resource type - Journals
eISSN - 2064-5279
pISSN - 2064-5260
DOI - 10.3311/ppee.16672
Subject(s) - control theory (sociology) , torque , flux linkage , direct torque control , electric vehicle , vector control , robustness (evolution) , computer science , stall torque , electronic speed control , engineering , voltage , power (physics) , induction motor , physics , control (management) , electrical engineering , biochemistry , chemistry , quantum mechanics , artificial intelligence , gene , thermodynamics
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 efciency 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.