This paper introduces a loss-minimizing Model Predictive Control (MPC) strategy for induction motors in electric vehicle applications designed to track a specified speed reference. The proposed control incorporates three key features that enhance efficiency and minimize losses. Firstly, an inverter selection vector strategy minimizes electromagnetic torque ripple, additional inverter switching frequency, and computational cost. Secondly, every element in the proposed control is based on the induction motor model, including consideration for iron losses. Thirdly, the MPC stator flux reference is optimized for total electric loss minimization, given any electromagnetic torque and mechanical speed reference, with no additional computational cost. The loss-minimizing function is derived from the induction motor model and accounts for all motor losses, including iron losses. Its straightforward implementation and pre-computed algebraic form ensure easy integration into various systems while reducing real-time computational overhead. The proposed control is tested and compared to a classical MPC through dynamic case studies, demonstrating satisfactory results in reducing total electric losses and electromagnetic torque ripple. During testing for electric vehicle applications within relevant standardized urban driving cycles, the proposed control showcases excellent energy efficiency results, reducing total electric losses by 49% compared with classical MPC.

Airborne wind energy systems (AWESs) offer a promising route to high-altitude wind harvesting, but their commercialization remains limited by the challenge of converting highly dynamic tethered flight power into stable electrical energy. While most research has focused on flight trajectories and control, the mechanical-to-electrical conversion stage requires further experimental validation. This paper introduces a validated electrical test bench emulator and a torque-ripple-optimized model predictive control (MPC) strategy, evaluated using two real AWES flight datasets. The emulator reproduces variable tether forces and reeling dynamics under optimal figure-eight crosswind flight. Two DC-bus topologies are compared: a separated bus that accurately mimics AWES storage dynamics (≈ 98 % fidelity) but demands 45 %–55 % more battery capacity and a common bus that recirculates energy, reducing storage needs by two-thirds. When realistic storage dynamic emulation is required, the separated-bus configuration is the only suitable option. The proposed MPC ensures precise generator speed and torque regulation, achieving torque-tracking root mean squared errors (RMSEs) below 0.11 % (Dataset 1) and 0.14 % (Dataset 2) and speed-tracking RMSEs of 0.44 % and 0.82 %, respectively. Overall energy efficiencies reach 82 % with Dataset 1 and 60 % with Dataset 2, with peak instantaneous efficiencies of 93 % and 88 %. Permanent magnet synchronous generators (PMSGs) outperform induction machines (IMs) by 4 % in Dataset 1 and up to 20 % in Dataset 2, with instantaneous gains of 2 %–10 % at high power. Off-nominal operation degrades cycle efficiency and drives higher battery cycling even in a common-bus setup, highlighting the importance of correct machine dimensioning. However, when storage dynamics are not under study, the common-bus configuration is the most cost-effective option, requiring less hardware and imposing lower peak discharge stresses. These results establish electrical test bench emulators as essential platforms for systematic evaluation and optimization of AWES power conversion, informing both machine design and control strategies for scalable, efficient AWES deployment.

This article presents an innovative Energy Management System (EMS) for microgrids with Hybrid Energy Storage Systems (HESS), designed to optimize power dispatch, minimize premature degradation of components, and ensure DC bus voltage stability (±2%) under stochastic disturbances. The EMS, implemented in the MATLAB/Simulink Stateflow environment, adopts a multi-objective strategy to regulate the DC bus, manage current limits across the HESS, and compensate for ultra-fast fluctuations (<500 ms) in both generation and demand. Validation combines software-in-the-loop (SiL) simulations with experimental validationon a real prototype subjected to dynamic power cycles. Results demonstrate that the EMS maintains the DC bus voltage within ±1.7% of its nominal value, reallocates loads without exceeding the defined current limits for each storage element, and reduces stress on critical components by 20%. The hierarchical coordination between Li-ion batteries and supercapacitors extends storage system lifetime by prioritizing supercapacitors for fast transients and batteries for sustained power demands. Furthermore, the EMS handles energy surpluses through controlled export or dissipation, preventing overvoltages on the DC bus. Experimental validation confirmed the theoretical model, demonstrating the robustness of the EMS in both isolated and interconnected microgrids, as well as its effectiveness which enables the integration of intermittent renewable sources such as airborne wind energy systems.

Airborne Wind Energy Systems (AWES) offer a promising alternative to conventional wind turbines, but their commercialization is hindered by challenges in efficiently converting the highly dynamic mechanical power of tethered flight into stable electrical energy. While extensive research has focused on optimizing AWES flight trajectories and control strategies, the power conversion stage, which is critical for integrating AWES into electrical grids, is relatively under-researched. To bridge this gap, reliable and flexible electrical test bench emulators are needed to replicate AWES dynamics under controlled conditions, enabling systematic evaluation and optimization of power electronics and control strategies. This paper presents a validated electrical test bench emulator and a torque ripple-optimized Model Predictive Control (MPC) strategy designed to enhance the performance of AWES ground station generators. The proposed emulator accurately reproduces the mechanical-electrical interactions of a real AWES by simulating the variable tether forces and reeling dynamics encountered during optimal crosswind flight. Two electrical topologies are introduced: a separated DC bus configuration that closely mimics real AWES energy storage dynamics and a common DC bus topology that minimizes battery requirements for extended control testing. The proposed MPC strategy ensures precise generator speed and torque regulation, achieving less than 1 % root mean square error (RMSE) in torque tracking while optimizing energy efficiency. Using experimental flight data, the test bench demonstrates an overall energy efficiency exceeding 80 % and peak conversion efficiencies up to 93 %, with Permanent Magnet Synchronous Generators (PMSG) outperforming Induction Machines (IM) by 2–6 % in instantaneous efficiency. These findings establish electrical test benches as essential for AWES development, offering a scalable platform for optimizing power conversion and control, which advances AWES as a viable renewable energy technology.

Efficiency-oriented control of induction motor drives reduces rotor flux at partial load, but the resulting reduction in speed-loop gain degrades closed-loop bandwidth under fixed-gain control. The maximum torque per ampere (MTPA) constraint allows an exact resolution of this trade-off. Enforcing MTPA as a structural control law reduces the conventional two-input field-oriented-control plant to a single-input single-output system. Its linearized transfer function is a second-order plant with a real zero at twice the rotor-flux pole frequency. A gain-scheduled proportional–integral controller, whose gain is inversely proportional to the stator-current magnitude, cancels the operating-point dependence and maintains constant crossover frequency and phase margin without a flux observer or look-up table. Four strategies are compared in simulation and experimentally on an 18.5 kW induction machine under airborne-wind-energy pumping cycles: standard field-oriented control, fixed-gain MIMO-MTPA, the proposed SISO-MTPA, and loss minimization. SISO-MTPA is the only strategy that improves both net harvested energy and speed tracking relative to the FOC baseline while remaining undominated: its energy is within 10% of the MTPA-optimal strategies, its speed-error RMSE is approximately one third of theirs, and its maximum deviation is over 60% lower. Against an industrial drive under high-power generation, it reduces speed-tracking RMSE by 47% with comparable generated energy.

Efficiency-oriented control of induction motor drives reduces rotor flux at partial load, but the resulting reduction in speed-loop gain degrades closed-loop bandwidth under fixed-gain control. The maximum torque per ampere (MTPA) constraint allows an exact resolution of this trade-off. Enforcing MTPA as a structural control law reduces the conventional two-input field-oriented-control plant to a single-input single-output system. Its linearized transfer function is a second-order plant with a real zero at twice the rotor-flux pole frequency. A gain-scheduled proportional–integral controller, whose gain is inversely proportional to the MTPA operating-point current, cancels the operating-point dependence and maintains constant crossover frequency and phase margin without an additional flux observer or look-up table. Four strategies are compared in simulation and experimentally on an 18.5 kW induction machine under airborne-wind-energy pumping cycles: standard field-oriented control, fixed-gain MIMO-MTPA, the proposed SISO-MTPA, and loss minimization. SISO-MTPA is the only strategy that improves both net harvested energy and speed tracking relative to the FOC baseline while remaining undominated: its energy is within 10% of the MTPA-optimal strategies, its speed-error RMSE is approximately 1.7 times lower, and its maximum deviation is over 40% lower. Against an industrial drive under high-power generation, it reduces speed-tracking RMSE by 47% with comparable generated energy.