Performance Assessment of the BLDC Motor in EV Drives using Nonlinear Model Predictive Control - Ubare, P.; Sonawane, D. N. (Engineering, Technology & Applied Science Research, 2022)
In this paper, the Nonlinear Model Predictive Control (NMPC) technique is proposed for the control of BrushLess Direct Current (BLDC) motors to address the problem of over-excitation, specifically in Electric Vehicle (EV) applications. This over-excitation increases the overall energy consumption of the machine and eventually reduces the vehicle’s driving range. The developed NMPC incorporates a nonlinear model of the BLDC motor with EV load and obtains the optimal current through the optimal voltage applied to the machine to regulate the motor torque. The proposed NMPC is compared with three conventional control techniques, the Field-Oriented Control (FOC), the Direct Torque Control (DTC), and the hybrid (the combination of DTC and FOC) control. It is observed from the simulation results that the proposed NMPC controller is more energy efficient while maintaining performance. This paper also discusses the selection of the motor based on the specified vehicle requirements. This has been done by matching the vehicle speed-torque characteristic curve with the motor’s one.