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Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly extract spatial and temporal degradation features from charge-cycle voltage and current measurements. Residual and inter-path connections enhance gradient flow and feature fusion, while a three-channel preprocessing strategy aligns cycle lengths and isolates padded regions, improving learning stability. Operating end-to-end, the model eliminates the need for handcrafted features and does not rely on discharge data or temperature measurements, enabling practical deployment in minimally instrumented environments. The model is evaluated on the NASA battery aging dataset under two scenarios: Same-Battery Evaluation and Leave-One-Battery-Out Cross-Battery Generalization. It achieves average RMSE values of 1.26% and 2.14%, converging within 816 and 395 epochs, respectively. An ablation study demonstrates that the dual-path design, ConvLSTM units, residual shortcuts, inter-path exchange, and preprocessing pipeline each contribute to accuracy, stability, and reduced training cost. With only 4913 parameters, the architecture remains robust to variations in initial capacity, cutoff voltage, and degradation behavior. Edge deployment on an NVIDIA Jetson AGX Orin confirms real-time feasibility, achieving 2.24 ms latency, 8.24 MB memory usage, and 12.9 W active power, supporting use in resource-constrained battery management systems.

In recent years, switched reluctance motors have emerged as a promising option for various applications due to their low manufacturing cost, rare-earth-free construction, and mechanical robustness. However, their widespread adoption is often limited by high torque ripple and acoustic noise. To address these challenges, this paper presents a comparative study of the acoustic noise performance of an 18/12 switched reluctance motor under various current control techniques. This comparison offers valuable insight into the motor’s vibroacoustic characteristics, which is essential for optimizing SRM performance, particularly in applications where noise reduction is critical. Dynamic simulations of an SRM are carried out in MATLAB/Simulink, and multi-physics analyses are performed in ANSYS Workbench. The multi-physics modeling includes electromagnetic, modal, and harmonic response analyses for four current control techniques evaluated across different operating speeds under light-load conditions. The simulation results are validated experimentally using an actual motor mounted on a dynamometer setup. The corresponding acoustic signatures for each control technique are presented as 2D plots of equivalent radiated power from simulations and sound power level from experimental tests. In addition, experimental waterfall diagrams are provided for each control technique.

This paper presents a speed sensorless control of a five-phase PMSM in healthy operation and under the Open-Phase Fault on any phase of the machine. The solution is recommended for mission-critical applications requiring high reliability capacities, such as Aerospace applications. An adapted Active Fault Tolerant Control is proposed with the aim of obtaining electromechanical torque as close as possible to that normally developed by a machine working in healthy condition. In instances of a loss of power to one phase of the machine, a reconfiguration of the control law is performed to ensure the continuity of service and to maintain acceptable control performances without requiring a hardware rearrangement of the power architecture. The motor rotation speed and position, required for the Field Oriented Control (FOC) of the stator currents, are estimated using a Back-Electromotive Forces (Back-EMF) observer based on a mathematical model of the motor and implemented in the stator diphase reference frame. Different electrical models that describe the behavior of the five-phase machine in the normal and degraded operations are given. Experimental results on a 1.25 kW synchronous PM machine are shown to confirm the effectiveness of the motor control.

The adoption of synchronous optimal pulse-width modulation (SOPWM) in two-level voltage-source inverters (2L-VSIs) offers low switching-to-fundamental-frequency ratio (SFR) operation while maintaining reduced current total harmonic distortion (THD). Despite these advantages, the performance of SOPWM is highly sensitive to signal noise in the modulation index and reference voltage angle. To prevent degradation, conventional PI controllers are conservatively tuned with slow dynamic response, which limits overall system performance. Finite control set model predictive control (FCS-MPC) integrated with SOPWM offers a promising solution, combining the fast dynamic response of FCS-MPC with the optimal steady-state performance of SOPWM. Nevertheless, the intricate tuning of weighting factors in FCS-MPC presents a significant challenge, particularly in balancing between enhanced harmonic performance and fast dynamic response. This paper introduces a simplified FCS-MPC approach that eliminates the need for complex weighting factor tuning while retaining the excellent dynamic performance of FCS-MPC and ensuring the low current THD achieved by SOPWM under steady-state conditions. The efficacy of the proposed method is validated through MATLAB/Simulink (R2023b) simulations and experimental results.

This article is focused on an original control approach applied to a transportation system that includes a polymer electrolyte membrane fuel cell (PEMFC) as the main energy source and supercapacitors (SC) as the energy storage backup. To interface the SC with the DC bus of the embedded network, a two-port bidirectional DC-DC converter was used. To control the system and ensure its stability, a reduced-order mathematical model of the network was developed through a nonlinear control approach employing a differential flatness algorithm, which is an attractive and efficient solution to make the system stable by overcoming the dynamic issues generally met in the power electronics networks of transportation systems. The design and tuning of the system control were not linked with the equilibrium point at which the interactions between the PEMFC main source, the SC energy storage device, and the loads are taken into consideration by the proposed control law. Besides this, high dynamics in the load power rejection were accomplished, which is the main contribution of this article. To verify the effectiveness of the developed control law, a small-scale experimental test rig was realized in the laboratory and the control laws were implemented in a dSPACE 1103 controller board. The experimental tests were performed with a 1 kW PEMFC source and a 250 F 32 V SC module as an energy storage backup. Lastly, the performances of the proposed control strategy were validated based on real experimental results measured during driving cycles, including motoring mode, ride-though, and regenerative braking mode.

This paper aims to focus on the smooth output of DC-DC buck converters in wireless power transfer systems under input perturbations and load disturbances using the high-order sliding mode controller (HOSM) and HOSM with super-twisting differentiator (HOSM + STD). The proposed control approach needs only measurement of converter output voltage. Theoretical analysis and design procedures, as well as the super-twisting differentiator of the proposed controller are presented in detail with the prescribed convergence law of high-order sliding modes. Comparisons of both simulation and experimental results among conventional proportional-integral (PI) control, traditional sliding mode control (SMC), HOSM and HOSM + STD under various test conditions such as steady state, input voltage perturbations and output load disturbances, are presented and discussed. The results demonstrate and validate the effectiveness and robustness of the proposed control method.

This paper presents a new online method based on low frequency signal injection to estimate the stator resistance of a Wound Rotor Synchronous Machine (WRSM). The proposed estimator provides a parameter-free method for estimating the stator resistance, in which there is no need to know the values of the parameters of the machine model, such as the stator and rotor inductances or the rotor flux linkage. In this method, a low frequency sinusoidal current is injected in the d axis of the stator current to produce a sinusoidal flux in the stator. In this paper, it is shown that the phase difference between the generated sinusoidal flux and the injected sinusoidal current is related to the stator resistance mismatch. Using this phase difference, the stator resistance is estimated. To validate the proposed model-free estimator, simulations were performed with Matlab Simulink and the results were compared with the extended Kalman filter observer. Finally, experimental tests, under different conditions, were performed to estimate the stator resistance of a WRSM.

In electrical power converter systems, the presence of an LC input filter can efficiently reduce the Electromagnetic Interference (EMI) effect, and at the same time protect the converter and the load from being impacted by sharp input impulse voltages. However, for transportation applications, the weight and size limitations of input LC filters for power converters have to be taken into consideration. The reduction of LC filter size may impair the system stability margin and dynamic response. In serve cases, the system may even become unstable. Thus, in order to ensure the system stability while minimizing the input LC filter size, the implementation of a stabilizer for the system control is needed. In this paper, a novel digital stabilizer design method is proposed for a boost power converter with a small input LC filter. The proposed method is based on input filter inductance current measurements and DSP (Digital Signal Processor) -based digital stabilizer design. Simulation and experimentation confirm the validity of the proposed approach.

This paper presents the utilization of differential flatness techniques from nonlinear control theory to permanent magnet assisted (PMa) synchronous reluctance motor (SynRM). The significant advantage of the proposed control approach is the potentiality to establish the behavior of the state variable system during the steady-state and transients operations as well. The mathematical models of PMa-SynRM are initially proved by the nonlinear case to show the flatness property. Then, the intelligent proportional-integral (iPI) is utilized as a control law to deal with some inevitable modeling errors and uncertainties for the torque and speed of the motor. Finally, a MicroLab Box dSPACE has been employed to implement the proposed control scheme. A small-scale test bench 1-KW relying on the PMa-SynRM has been designed and developed in the laboratory to approve the proposed control algorithm. The experimental results reflect that the proposed control effectively performs high performance during dynamic operating conditions for the inner torque loop control and outer speed loop control of the motor drive compared to the traditional PI control.

The cascaded connection of power converters in a DC microgrid may cause instabilities. Indeed, power converters operating as external loads exhibit constant power load (CPL) behaviors. In this study, the design of the feedback controller of a multi–cell interleaved fuel cell (FC) step–up power circuit is based on the adaptive Hamiltonian control law. It includes two integral terms to confirm that there is no steady-state error in the DC bus voltage, and to guarantee the current balancing of each input inductor current. The design confirms that the desired equilibrium point is (locally) asymptotically stable by using the Lyapunov stability proof. The control approach is validated via digital simulations and experimental tests performed with a 2500 W FC converter supplied by an FC/reformer size of 2500 W and 50 V.