Observer-Aided Adaptive MPC for Battery Energy Reduction in PMSM Electric-Vehicle Drives

Efficient battery usage is crucial for extending the driving range and improving the overall efficiency of battery electric vehicles (BEVs). This paper presents an energy-efficient adaptive linear model predictive control (AL-MPC) strategy for a permanent-magnet synchronous motor (PMSM) drive. Unlike the conventional linear predictive model, which cannot represent the highly nonlinear electrical behavior of the PMSM plant inherent in BEVs, the proposed controller employs a real-time adaptive model continuously updated with the estimated traction torque and stator reactance. These updates allow the predictive model to reflect the actual motor dynamics more accurately. A moving-average-filtered third-order generalized integrator (MAF-TOGI) observer provides noise-resistant estimates of the electromagnetic torque and reactance, enabling accurate current reference tracking and precise realization of the maximum torque-per-ampere (MTPA) control. Consequently, battery energy consumption is reduced, and system efficiency is enhanced. Simulation results demonstrate smoother torque response, improved speed tracking, and a 7.4% reduction in battery energy use compared with a conventional linear MPC baseline under identical driving conditions.