## 1 Introduction  
Among various types of motor drives for EVs, a permanent magnet synchronous motor (PMSM) drive is attractive due to its commercial merits, such as its high efficiency and high power density [1, 2, 3, 4]. In terms of PMSM drive technologies, traditional control techniques mainly consist of vector control (VC) and direct torque control (DTC) [5, 6]. A PMSM drive relying on VC can perform comparably in dynamic characteristics to a direct current machine drive, while requiring complex coordinate transformation and significantly depending on precise machine parameters. In addition, a DTC drive suffers from the drawback of extensive calculation and greater real-time requirements, although it is simpler in structure. In comparison to the aforementioned techniques, model predictive control (MPC) possesses key features, namely, quick responsiveness, multi-objective evolutionary capability, and a simple principle [7, 8, 9], with this method having gained significant interest in recent years. On the other hand, the three-level, neutral-point-clamped (3L-NPC) inverter has been applied to motor drives [10], owing to its superiorities in voltage distortion, semiconductor stress, and switching frequency [11, 12, 13, 14]. The aforementioned control techniques have been actively extended to a 3L-NPC-powered PMSM drive. When traditional methods are chosen, an additional control loop is required to keep neutral-point voltage (NPV) balance, complicating the overall control system. When aiming to eliminate the additional control loop, MPC is undoubtedly the most feasible option because of its effectiveness in terms of solving such an optimization problem with multi-objectives. Regardless of the control strategies employed and the inverter's topologies, installing a position sensor is normally required and is essential for an EV application. As such, once the sensor or the connecting cable breaks down, the machine, and hence the vehicle, will be out of control. For this reason, integrating a sensorless position control as an alternative option is necessary to ensure the safety of the EV when the position sensor is faulty. Conventionalsensorless position control methods can be partitioned into two methodologies for different speed ranges: a back-electromotive force (BEMF)-based scheme for mid- and/or high-speed regions, and a magnetic saliency-based scheme for the low-speed region. For example, in [15], a sensorless control was created based on an improved high-frequency injection (HFI) and mainly functions to estimate motor position information as well as realize predictive current control. In this manner, the control performance and the accuracy of the position estimation are highly contingent on the injected signal frequency. Although this method can be used at an injection frequency up to a quarter of the PWM switching frequency, it also suffers from high-frequency torque ripples, and an unfavorable control performance when a high speed is required. Since the machine used for an EV application is usually required to operate in the whole speed range in practice, some blended position estimation strategies combining the above two methodologies have been recognized as future methods of meeting these requirements. A previous study [16] developed a virtual HFI method, where the high-powered sensorless control is associated with the precise automatic tracking performance of maximum-torque-per-ampere (MTPA) by constructing a virtual \(q\)-axis inductance. Additionally, in [17], a quadratic extended back-electromotive force (QBEMF) model was developed for a universal full-speed sensorless control. By incorporating the injection-based method with model-based position estimations, it enables QBEMF to work as a self-demodulator. To be specific, this scheme estimates the rotor position using diverse HFI voltages at low speed, while, as the rotor speed increases, the position estimation is still accomplished by the same observer avoiding any injections.