Energy-efficient Nonlinear Model Predictive Control of BLDC Motor in Electric Vehicles

The maximum distance that can be traveled at a stretch is the major limitation of today’s electric vehicle (EV). This is due to the need for maximum current, torque, and, the total onboard energy storage, etc. The distance can be increased by efficiently using the available power resources. In this paper, we present a nonlinear model predictive control (NMPC) scheme for the control of brushless direct current (BLDC) motor in EV. A control-oriented nonlinear model of the BLDC motor with EV load is considered and used in the proposed NMPC scheme. The objective of the NMPC is to control the desired torque and speed of the motor by minimizing the energy with constraints on supplied current and maximum speed. The simulation results of BLDC motor control with EV and fixed mechanical load are presented. Further, the performance of NMPC is compared with the conventional direct torque control (DTC) scheme. Presented results show that NMPC improves energy savings by 19% in fixed-load and 13% with EV load as compared to DTC under provided conditions of speed reference.