To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems.

This paper proposes a finite-time extended state observer (FTESO) integrated with model predictive control (MPC) for high-performance control of permanent magnet synchronous motors (PMSMs). A disturbance-aware predictive model is constructed by incorporating lumped disturbances into the PMSM current equations, addressing load fluctuations and parameter uncertainties. The FTESO, designed with nonlinear gains and Lyapunov stability, ensures rapid disturbance estimation and is embedded into a feedforward-compensated MPC with a composite cost function considering current error and voltage increment. Simulations show that under sudden load disturbances, FTESO-MPC achieves faster recovery and a smaller steady-state error than LESO-MPC; when inductance triples, FTESO-MPC maintains smooth convergence, whereas LESO-MPC exhibits oscillations with d-axis current peaks near 200 A. Under resistance or flux variations, FTESO-MPC sustains stable regulation with less ripple, confirming its superior tracking accuracy and robustness compared with LESO-MPC.