Cascaded Speed Control of a PMSM for Electric Vehicles Using Non-Linear Model Predictive Control Incorporating GA-Tuned PI Current Control

Abstract

As the world's transition to sustainable transport accelerates, Electric Vehicles (EVs) are now a vital solution to reduce carbon emissions and fossil fuel dependence. However, their energy efficiency and performance often degrade under varying load and road conditions. This problem of EV performance has to be optimized through the proper use of advanced motor types and the implementation of efficient control strategies. Among various motor types, the Permanent Magnet Synchronous Motor (PMSM) is chosen due to its small size, reliability, and high efficiency, which are ideal for EV applications. The Interior Permanent Magnet Synchronous Motor (IPMSM) is chosen in particular due to its greater torque density and efficiency at low speeds, which are critical for urban and dynamic driving conditions. For speed control of the IPMSM in this thesis, Nonlinear Model Predictive Control (NMPC) is applied due to its ability to control complex system dynamics and constraints effectively while resulting in high energy efficiency as well as optimal performance. Unlike other traditional control algorithms, NMPC facilitates better handling of nonlinearities and input-output constraints, and therefore it is very suitable for EVs under different driving conditions. This research involved intensive examination of Hyundai Ioniq's resistive forces, power requirements, and torque requirements in the selection of proper motor parameters. NMPC in outer loop speed control and a Proportional-Integral (PI) controller whose parameters are tuned with the aid of a Genetic Algorithm (GA) for inner current control is employed as the control strategy. This control method is contrasted with a linear MPC for speed control and a GA tuned PI controller for current control. In the comparison, NMPC achieves better performance with zero steady-state error and higher efficiency, while LMPC shows a steady-state error of 1.5469. The control and drive systems were simulated and implemented using MATLAB software.