## 1 Introduction
This random spectrum of output current distributed over a wide range makes the design of converter output filters quite difficult. It can be fatal for applications with limited space, such as traction drives [24]. The issue of variable switching frequency in FCS-MPC is particularly problematic for high-frequency power converters based on wide-bandgap devices, as increasing the sampling rate to achieve a high switching frequency is not always practical due to computational burden [25; 26]. Model predictive control (MPC) is a control methodology that has gained significant popularity in the field of power electronic converters due to its simplicity, adaptability, and powerful features [27; 28]. It effectively integrates constraints and handles non-linearities while providing a fast dynamic response by using a mathematical model for forecasting and a cost function for selecting optimal switching states [29]. MPC is an optimal control method based on numerical optimization, enabling simple design and implementation. It efficiently handles large systems with many control variables, including MIMO systems with input-output interactions [30]. Despite needing more calculations than traditional methods, advances in microprocessors support its use [30]. For electric vehicle applications, typically using digital systems, a sampled MPC approach is favored [30]. Specifically, this paper focuses on Finite-Control-Set Model Predictive Control (FCS-MPC), a class of MPC that takes advantage of the intrinsically discrete nature of power converters [31]. The core principle of FCS-MPC involves three key elements: a discrete-time model of the system, the finite number of possible switching states of the converter, and a cost function [28]. At each sampling interval, the controller uses the system model to predict the future behavior of the relevant variables (such as currents or voltages) for every possible switching state available in the converter's finite set. For a two-level VSI, this corresponds to eight distinct switching states. Each prediction is then evaluated by a cost function, which quantifies the error between the predicted variable and its desired reference value. The switching state that minimizes this cost function is then selected and applied directly to the converter in the subsequent sampling period, a process that is repeated at every cycle [32]. The benefits of MPC include its fast transient response, the ease with which it can handle non-linearities, and the ability to manage multivariable control objectives within a single control loop [27; 33]. Multiple control objectives, such as output current tracking, voltage regulation, and reductions in switching commutations, can be systematically achieved by including the corresponding weighted terms in the cost function [34]. However, a well-known drawback of the conventional FCS-MPC strategy is that by selecting only one optimal voltage vector for the entire duration of the sampling interval, it inherently operates at a variable switching frequency [23]. This variability leads to a wide-band harmonic spectrum in output voltage and current, which can complicate the design of output filters and may pose the risk of exciting resonances in the system, particularly in converters with \(LCL\) filters [17; 19]. An MPC method to reduce switching losses forecasts losses for each switching state and incorporates these predictions into the objective function [35]. The ability of FCS-MPC to handle multiple control objectives in a single computational stage by solving a multicriterion optimization in real time is a key advantage [36]. Although FCS-MPC offers the potential to optimize switching power losses by directly manipulating semiconductor switching states [19], its conventional form directly generates switching signals without a dedicated modulation stage.