Power Requirements Research Articles

Approximation Design for Low Power Consumption in Digital Signal Processing Architecture: A Literature Review

Given the power demands of DSP operations in portable or embedded devices, especially in error-resilient applications such as the multimedia and image processing, approximation strategy is proposed as a viable solution. By deliberately reducing computational precision, various DSP components such as adders, multipliers, and compressors can be designed with lower power requirements. This review explores the optimization of these components at different design levels, including architectural and circuit levels. Specifically, this paper goes deeper into the design of approximate adders, compressors, and multipliers, essentially highlighting their impact on power efficiency and computational accuracy for image/video compression applications. Through analyzing and discussing the different designs, this paper elaborates the trade-offs between power savings and error rates, ultimately demonstrating the potential of approximation techniques in reducing power consumption without significantly compromising performance.

Battery-Type Transition Metal Oxides in Hybrid Supercapacitors: Synthesis and Applications

Hybrid supercapacitors (HSCs) have garnered growing interest for their ability to combine the high energy storage capability of batteries with the rapid charge–discharge characteristics of supercapacitors. This review examines the evolution of HSCs, emphasizing the synergistic mechanisms that integrate both Faradaic and non-Faradaic charge storage processes. Transition metal oxides (TMOs) are highlighted as promising battery-type electrodes owing to their notable energy storage potential and compatibility with various synthesis routes, including hydro/solvothermal methods, electrospinning, electrodeposition, and sol–gel processes. Particular attention is directed toward Ti-, Co-, and V-based TMOs, with a focus on tailoring their properties through morphology control, composite formation, and doping to enhance electrochemical performance. Overall, the discussion underscores the potential of HSCs to meet the growing demand for next-generation energy storage systems by bridging the gap between high energy and high power requirements.

Joint satellite platform and constellation sizing for instantaneous beam-hopping in 5G/6G Non-Terrestrial Networks

Existing research on resource allocation in satellite networks incorporating beam-hopping (BH) focuses predominantly on the performance analysis of diverse algorithms and techniques. However, studies evaluating the architectural and economic impacts of implemented BH-based technical solutions have not yet been addressed. Aiming to close this gap, this contribution quantifies the impact of BH and orbital parameters on the satellite platform and constellation size, considering specific traffic demand and service time indicators. The paper proposes a low complexity, instantaneous demand-based BH resource allocation technique, and presents a comprehensive analysis of LEO and VLEO scenarios using small platforms, lying on 5G/6G Non-Terrestrial Network (NTN) specifications. Given a joint set of traffic demand and time-to-serve indicators, and based on a feasible multibeam on-board antenna architecture, the paper compares the RF transmit power requirements in fixed and variable grid LEO schemes, and in VLEO with different minimum elevation angles, to assess the feasibility of these orbits. For a fixed minimum elevation and number of users, the RF transmit power and satellite platform requirements are significantly reduced when transitioning into lower altitudes with narrower satellite coverage areas. The relevant trade-off between the satellite platform and the size of the constellation required for global coverage is presented, to fulfill a set of traffic demand and time to serve indicators: approximately, 1156 3U satellites are required for VLEO constellation and 182 12U satellites for LEO. Once platform and constellation sizing trade-off is quantified, the paper estimates the economic costs for each of the deployments, showing a total cost of almost the double for the presented VLEO constellation compared to the LEO one. The article aims to provide system engineers and satellite operators with crucial information for satellite system design, dimensioning and cost assessment.

Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach

PurposeDespite increased awareness of sleep hygiene, over 80% of sleep apnea cases remain undiagnosed, underscoring the need for accessible screening methods. This study presents a method for detecting sleep apnea using data from the Apple Watch’s inertial measurement unit (IMU).MethodsAn algorithm was developed to extract seismocardiographic and respiratory signals from IMU data, analyzing features such as breathing and heart rate variability, respiratory dips, and body movements. In a cohort of 61 adults undergoing polysomnography, we analyzed 52,337 30-second epochs, with 12,373 (23.6%) identified as apnea/hypopnea episodes. Machine learning models using five classifiers (Logistic Regression, Random Forest, Gradient Boosting, k-Nearest Neighbors, and Multi-layer Perceptron) were trained on data from 41 subjects and validated on 20 subjects.ResultsThe Random Forest classifier performed best in per-epoch respiratory event detection, achieving an AUC of 0.827 and an F1 score of 0.572 in the training group, and an AUC of 0.831 and an F1 score of 0.602 in the test group. The model’s per-subject predictions strongly correlated with the apnea-hypopnea index (AHI) from polysomnography (r = 0.93) and identified subjects with AHI ≥ 15 with 100% sensitivity and 90% specificity.ConclusionUtilizing the widespread availability of the Apple Watch and the low power requirements of the IMU, this approach has the potential to significantly improve sleep apnea screening accessibility.

Design of Piezoelectric Energy Harvester Single Cantilever Beam for IoT applications and Micro-electronic devices

A few years back the power requirement of electronic devices was very high. But with the technological developments in the field of internet-based systems, the design of low-powered microelectronic devices, WSN and IoT devices became necessary. In these systems the size and the power requirement are low and in most situations the replacement of batteries is challenging. For these microelectronic and IoT devices the abundant energy harvester is very useful. Among different abundant energy resources, vibrational energy harvesting with piezoelectric cantilever beam energy harvesters is of interest. This research work presents the design and analysis of an energy harvester (EH) which contains a single piezoelectric cantilever beam that captures the vibrational energy of the suspension bridge. This approach ties the two things together by framing piezoelectric energy harvesting as a solution to the power challenges faced by low-powered devices, making the transition feel more natural and connected. The main challenge in the design was matching the resonance frequency of the bridge with a piezo EH which is around 2.5Hz to extract maximum power. To overcome this problem Eigen frequency analysis in COMSOL Multiphysics is done. The 3D geometry of single beam piezo EH is designed and analyzed in COMSOL Multiphysics solid works. In this research work a relationship is established between the geometrical parameters of the single beam piezo EH and Eigen frequency based on the first six Eigen frequency analyses in COMSOL Multiphysics. For fi nite element analysis(FEA) a piezo single beam harvester is vibrated by application of force which is equal to the vibrational force (0.98m/s<sup>2</sup>) in the suspension bridge. The force of (0.98 m/s²) is chosen because it avoids resonating with critical system components. The output from the harvester is achieved at a resonance frequency of 2.5Hz. The output from the piezo is very low 800 milli volts at 2.5Hz. The output results of piezo EH are also compared with a cantilever beam with a single-branch structure.

Minimum effective concentration 90 (EC90) of ropivacaine for Femoral nerve block: A biased-coin up-and-down sequential method study

Background and Aims: The femoral nerve (FN) is commonly blocked using ropivacaine to provide postoperative analgesia after knee surgery. However, the minimal required concentration has not yet been defined. The aim of this study was to estimate the minimal ropivacaine concentration required to achieve adequate analgesic FN block in 90% of cases (EC90). Methods: This study included 50 patients who were scheduled for knee ligament reconstruction under combined nerve block and general anaesthesia. The FN block was performed using 15 mL of ropivacaine with varying concentrations and considered adequate when associated with pain-free recovery. The sciatic, obturator, and lateral femoral cutaneous nerves were blocked to negate other knee pain generators, and their block success was confirmed. We used the biased-coin design up–down sequential method where the adequacy of an FN block altered the ropivacaine concentration used for the next block. The adequacy of the analgesic block or lack of it was analysed to calculate the analgesic EC90. The quadriceps motor power and morphine requirement were also recorded. Results: The recommended analgesic ropivacaine EC90 was 0.05% w/v. The associated quadriceps weakness and morphine requirement were minimal. Conclusion: FN block using ropivacaine 0.05% w/v may provide adequate analgesia in 90% of patients.

Capillary microfluidics for diagnostic applications: fundamentals, mechanisms, and capillarics

Microfluidic systems, especially those using capillary forces, have recently attracted considerable interest due to their potential to facilitate passive fluid management in portable diagnostic devices and point-of-care settings. These systems utilize capillary forces to autonomously regulate fluid flow, eliminating the requirement for external power and providing a more straightforward and economical option compared to active microfluidic systems. This review examines the fundamental concepts of capillary-driven microfluidics, emphasizing significant progress in the design of capillary pumps and valves, as well as the influence of surface tension, wettability, and the geometrical configurations of microchannels on the enhancement of fluid dynamics. Furthermore, the review explores other configurations, such as porous and solid substrates, to illustrate their potential for healthcare and biochemical applications. Moreover, the challenges related to managing flow rates and enhancing the reproducibility of devices are addressed, alongside recent innovations designed to overcome these challenges. Capillary systems offer an effective and reliable foundation for developing miniaturized diagnostic instruments, which hold significant potential across various domains, including biological research and environmental monitoring.

Operational Pitch Actuation Dynamics for Offshore Wind Turbines Ranging from 5 to 50 MW

ABSTRACTModern wind turbines have been continuously growing in size due to the increased power generation and reduced costs associated with larger rotors and more abundant wind resources offshore. In order to effectively implement pitch control on blades that are longer, more flexible, and heavier than ever before, modern electric pitch systems must provide enough torque to overcome blade pitch inertia and loads while providing suitable control response frequencies. Despite this need, there is limited published research on the sizing of such pitch systems at extreme scales. This study models peak pitching power and pitch actuator torque requirements in Regions 2 and 3 turbulent wind conditions. The developed model considers blade pitch response, pitching moments, pitch system dynamics, and blade aeroelasticity. The model is applied using an integrated wind turbine code used to simulate the turbine response of a 25‐MW offshore reference turbine with advanced pitch control under standardized turbulent wind conditions. The results show that the fastest pitch response requirements occur in Region 3 wind speeds just above the rated wind speed and that the peak pitch actuator torque requirements are correlated with maximum pitching moments. The model is extended to turbines ranging from 5 to 50 MW to develop a simplified scaling power law based on only the product of blade mass and mean chord length. This scaling law predicts maximum pitch actuator torque and maximum power consumed from pitch actuation based on results from computational simulations of multiple extreme‐scale reference turbines. This study provides useful insights for the design and sizing of pitch systems in large‐scale wind turbines.

The Cart-Pole Application as a Benchmark for Neuromorphic Computing

The cart-pole application is a well-known control application that is often used to illustrate reinforcement learning algorithms with conventional neural networks. An implementation of the application from OpenAI Gym is ubiquitous and popular. Spiking neural networks are the basis of brain-based, or neuromorphic computing. They are attractive, especially as agents for control applications, because of their very low size, weight and power requirements. We are motivated to help researchers in neuromorphic computing to be able to compare their work with common benchmarks, and in this paper we explore using the cart-pole application as a benchmark for spiking neural networks. We propose four parameter settings that scale the application in difficulty, in particular beyond the default parameter settings which do not pose a difficult test for AI agents. We propose achievement levels for AI agents that are trained with these settings. Next, we perform an experiment that employs the benchmark and its difficulty levels to evaluate the effectiveness of eight neuroprocessor settings on success with the application. Finally, we perform a detailed examination of eight example networks from this experiment, that achieve our goals on the difficulty levels, and comment on features that enable them to be successful. Our goal is to help researchers in neuromorphic computing to utilize the cart-pole application as an effective benchmark.

A novel harmonic resonance prevention measure for railway power conditioner–network–train interaction system

Abstract The integration of a large number of power electronic converters, such as railway power conditioner (RPC), introduces a series of problems, including harmonic interaction, stability issues, and wideband resonance, into the railway power supply system. To address these challenges, this paper proposes a novel harmonic resonance prevention measure for RPC–network–train interaction system. Firstly, a harmonic model, a parallel resonance impedance model, a series resonance admittance model, and a control stability model are each established for the RPC–network–train interaction system. Secondly, a comprehensive resonance impact factor (CRIF) is proposed to efficiently and accurately identify the key components affecting resonance, and to provide the selection results of optimization parameters for resonance prevention. Next, the initially selected parameters are constrained by the requirements of ripple current, reactive power and stability. Subsequently, the impedance parameters (control parameters and filter parameters) of the RPC are optimized with the objective of reshaping the parallel resonance impedance and series resonance admittance of the RPC–network–train interaction system, ensuring the output current harmonics of RPC meet standards to achieve resonance prevention, while ensuring the stable operation of the RPC. Finally, the proposed resonance prevention measure is verified under both light load and heavy load conditions using a simulation platform and a hardware-in-the-loop experimental platform.

Improvement of Aerodynamic Performance of Bilaterally Symmetrical Airfoil by Co-Flow Jet and Adaptive Morphing Technology

For a special bilaterally symmetric airfoil (BSA), this paper designs an active flow control scheme based on the Co-Flow Jet (CFJ) and adaptive morphing technology, and establishes a numerical simulation method which is suitable for simulating aerodynamic characteristics. The accuracy and effectiveness of the numerical method has been verified through benchmark cases. This study investigates the effects of jet intensity, suction slot position and angle, and deflection angles of the leading and TE flap on the aerodynamic performance parameters and flow field structure of the bilaterally symmetric airfoil. The results show that the adaptive morphing technology can significantly improve the equivalent lift coefficient and equivalent lift-to-drag ratio of the bilaterally symmetric airfoil, without obviously increasing the CFJ power consumption coefficient. Selecting an appropriate CFJ intensity can achieve a relatively high equivalent lift-to-drag ratio with a low compressor power requirement. Moving the suction slot rearward can increase the lift coefficient, and placing it on the trailing edge (TE) flap can more efficiently delay flow separation, reduce power consumption, and increase the equivalent lift-to-drag ratio. The suction slot angle has little effect on the lift coefficient, but a larger suction slot angle can enhance the equivalent lift-to-drag ratio. Increasing the TE flap deflection angle enhances both the lift coefficient and drag coefficient, as well as the power consumption coefficient at high angles of attack. But it has little effect on the maximum equivalent lift-to-drag ratio. Increasing the leading edge flap deflection angle can improve the maximum equivalent lift-to-drag ratio while increasing the angle of attack corresponding to it. Overall, choosing a CFJ and adaptive morphing parameters by considering different factors can enhance the aerodynamic performance of the bilaterally symmetric airfoil.

MAC-DNN: An Optimized Hardware Implementation of MAC Unit in Neuron Engine for Deep Neural Network Applications

Object detection and analysis using deep neural networks (DNNs) have become significant challenges due to their computational and power requirements. Hence, such computation is possible on general-purpose platforms like central processing units (CPUs), graphic processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). However, the development of high computational platforms is a critical challenge for efficient edge computing tasks like object detection, where power and bandwidth are low but faster and energy-efficient solutions are required. System-on-chip (SoC) designs are an optimistic solution for addressing these challenges. This study presents the power and delay-optimized Multiply-Accumulate (MAC) unit architecture for DNN and compares the parameters of 4-bit, 8-bit, 12-bit, and 16-bit MAC units. Vivado software has been employed to construct the MAC unit. It can carry out addition, accumulation, and multiplication operations. The design is analyzed and simulated using the Vivado High-Level Synthesis (HLS) tool, which is subsequently deployed on the Zybo Evaluation and Development Kit. The proposed approach outperforms the existing state-of-the-art models in terms of processing time and power for different precisions.

Enabling Power System Restoration from Offshore Wind Power Plants in the UK

This paper presents the findings from the initial phases of the SIF BLADE project, focused on demonstrating the capabilities of an offshore wind power plant (OWPP) for power system restoration (PSR). It provides an overview of PSR, highlighting its challenges and operational requirements, alongside the various scenarios considered in the project. The study includes a steady-state analysis to assess whether the OWPP can meet local network demands for both active and reactive power. Results indicate that the OWPP can operate within an envelope that covers all local power requirements. Additionally, electromagnetic transient (EMT) analysis was conducted to evaluate different percentages of grid-forming (GFM) converter penetration during the energisation process. These analyses aimed to determine compliance with transmission system operator (TSO) requirements. Findings demonstrate that all GFM penetration levels met the necessary TSO standards. Furthermore, a novel small-signal analysis was performed to identify the optimal percentage of GFM converters for enhancing system stability during block loading. The analysis suggests that for top-up scenarios, a GFM penetration between 20% and 40% is optimal, while for anchor scenarios, 40% to 60% GFM penetration enhances stability and robustness.

Study of Remote Hydraulic System Based on AMEsim Rotary Blowout Prevention Type Hydraulic Rotary Table

Turntable is one of the important supporting equipment in oil drilling and repairing operation, which is mainly used as the power to drive the square drill pipe, drive the drill pipe to drill or deal with the well accident when used to rotate the drill column. In this paper, we study the rotary blowout-proof hydraulic turntable, which can meet the general rotary power requirements and also realize the control of wellbore annulus blowout-proof seal during rotary operation. Study the hydraulic working principle of the drilling rotary blowout prevention hydraulic rotary table and draw the hydraulic schematic diagram, combined with the hydraulic schematic diagram to complete the AMEsim hydraulic system modeling. Briefly describe the construction idea of the hydraulic system model of the drilling rotary blowout prevention hydraulic rotary table and analyze and verify the simulation results.

Life Cycle Economic and Environmental Assessment for Emerging Heavy-Duty Truck Powertrain Technologies in China: A Comparative Study of Battery Electric, Fuel Cell Electric, and Hydrogen Combustion Engine Trucks.

Amid ambitious net-zero goals and growing demands for freight logistics, addressing the climate challenges posed by the heavy-duty truck (HDT) sector is an urgent and pivotal task. This study develops an integrated HDT model by incorporating vehicle dynamic simulation and life cycle analysis to quantify energy consumption, greenhouse gas (GHG) emissions, and total cost of ownership associated with three emerging powertrain technologies in various truck use scenarios in China, including battery electric, fuel cell electric, and hydrogen combustion engine trucks. The results reveal varying levels of economic suitability for these powertrain alternatives depending on required driving ranges and duty cycles: the battery electric for regional-haul applications, the hydrogen fuel cell for longer-haul and low-load driving conditions, and the hydrogen combustion engine to meet high power requirements. The GHG mitigation potential of these technologies, on the other hand, critically hinges on energy sources. All three powertrains could achieve over 55% GHG emission reductions if the power sector and hydrogen supply are substantially decarbonized by 2035. The analysis underscores the necessity for a broad policy approach on a life cycle basis to incorporate all powertrain technologies and all energy sources in a complementary manner for facilitating a low-carbon road freight future.

Thermal plasma technology applied to the inertization process of the inorganic fraction of sewage sludge generated from municipal wastewater treatment plant

Abstract This study investigates the thermal plasma pyrolysis process for inertizing the inorganic fraction of sewage sludge from municipal wastewater treatment plants. The aim is to assess its effectiveness in waste inertization. Lab-scale experiments were conducted to process the sludge thermally. Elemental composition analysis was done using x-ray fluorescence (XRF), thermogravimetry coupled with mass spectrometry (TGA-MS) and x-ray diffraction (XRD). The XRF analysis showed an initial composition of Si, Al, Fe, and Ca, corresponding to 86.8% of the inorganic matter of the sludge. TGA-MS analysis showed a significant mass loss between 200 and 650°C, corresponding to organic matter volatilization, methane conversion, and dehydrogenation of polymorphic silicon. XRD analysis revealed a dried sludge crystalline structure composed mainly by SiO2, CaCO3, and AlPO4, and after plasma treatment, the remaining composition of the slag was primarily SiO2 amorphous. Mass and energy balances, considering thermodynamic equilibrium and chemical reactions, are performed. The mass balance calculations identified the most probable composition of the sludge, and energy balance calculations determined a net energy requirement of 399 kWh for plasma inertization, with an additional 300 kWh to account for furnace losses. Solubility and leaching tests confirm the inert nature of the residue. Power requirements are estimated at 700 kW for processing 350 kg h−1 of decarbonized sludge. These findings are crucial for optimizing plasma inertization processes in wastewater treatment plants. This work presents a novel approach by combining a computational prediction for an industrial-scale plant with a direct experimental assessment of the plasma treatment process.

Numerical investigation on hydrothermal characteristics of microchannel heat sinks with PCM inserts for effective thermal management applications

PurposeThe purpose of the paper is to develop an efficient thermal management system, which effectively dissipate the heat generated from the electronic devices. The present paper focuses at the modeling of microchannel heat sinks (MCHSs) with phase change materials (PCMs) insets to deal with the fluctuating heat generated from the electronic components.Design/methodology/approachIn this paper, a novel approach is introduced to enhance the thermal performance of MCHSs through the integration of conjugate heat transfer and energy storage. Numerical investigations were conducted on six novel models of PCM-based hybrid MCHSs using ANSYS-FLUENT. The hydrothermal characteristics of six PCM-based hybrid MCHS models were analyzed and compared with an MCHS model without PCM.FindingsThe numerical model used for this study exhibited a good agreement with existing experimental and simulation results documented in the literature. The hybrid MCHS models developed in the present analysis showed superior thermal characteristics compared to MCHS without PCM. About 12% improvement in the thermal performance factor and a 7.3% reduction in thermal resistance were observed in the proposed MCHS models. A negligible influence of the PCM channel shape and aspect ratio (AR) was observed on the MCHS performance.Research limitations/implicationsAs the present work is a numerical investigation, the computational time and computational cost requirements are the main implication for the research.Practical implicationsHigh pumping power requirement and expensive manufacturing methods of the microfluidic devices are the main practical implications. Leakage problem is also a challenge for development of these heat sinks.Originality/valueThe surge in the heat generated by electronic components is a limiting factor for the conventional MCHSs. To accommodate the surge, researchers have explored energy storage methods using PCM-based passive MCHS but these are effective only during the phase change process. To address this limitation, novel PCM-based hybrid MCHSs, which combine convective heat transfer with energy storage capabilities, have been modeled in the present work. There is an ample opportunity for further exploration of hybrid MCHSs with PCM.

An Analysis of Components and Enhancement Strategies for Advancing Memristive Neural Networks.

Advancements in artificial intelligence (AI) and big data have highlighted the limitations of traditional von Neumann architectures, such as excessive power consumption and limited performance improvement with increasing parameter numbers. These challenges are significant for edge devices requiring higher energy and area efficiency. Recently, many reports on memristor-based neural networks (Mem-NN) using resistive switching memory have shown efficient computing performance with a low power requirement. Even further performance optimization can be made using engineering resistive switching mechanisms. Nevertheless, systematic reviews that address the circuit-to-material aspects of Mem-NNs, including their dedicated algorithms, remain limited. This review first categorizes the memristor-based neural networks into three components: pre-processing units, processing units, and learning algorithms. Then, the optimization methods to improve integration and operational reliability are discussed across materials, devices, circuits, and algorithms for each component. Furthermore, the review compares recent advancements in chip-level neuromorphic hardware with conventional systems, including graphic processing units. The ongoing challenges and future directions in the field are discussed, highlighting the research to enhance the functionality and reliability of Mem-NNs.

Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading

The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need for innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy in the continuous extrusion of CP-Titanium Grade 2, a material vital for structural resilience in critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced to forecast power requirements with high precision. The analysis utilized a published dataset that comprises theoretical, numerical, and experimental power calculations as a robust foundation for validation and comparison. A visualization highlighted the influence of process parameters, such as feedstock temperature and extrusion wheel velocity, on structural performance to align with the thematic focus of resilient material design. The ANN-SGD model achieved an RMSE of 0.9954 and a CVRMSE of 11.53% which demonstrated significant improvements in prediction accuracy compared to traditional approaches. By achieving superior alignment with experimental results, the model validated its efficacy as a reliable and efficient tool for understanding and optimizing complex manufacturing processes. This research emphasizes the potential of ML to revolutionize material processing for extreme conditions and contribute to the broader goals of structural resilience and sustainable manufacturing.

A Modular and Scalable Approach to Hybrid Battery and Converter Integration for Full-Electric Waterborne Transport

This paper presents a flexible and scalable battery system for maritime transportation, integrating modular converters and hybrid battery technologies that are effectively implemented in real-world scenarios. The proposed system is realized with modular DC-DC converters, which do not require complex design and control or a high number of components and combine high-power (HP) and high-energy (HE) battery cells to optimize the energy and power requirements of vessel operations without oversizing the energy storage system. Moreover, the modular design ensures flexibility and scalability, allowing for easy adaptation to varying operational demands. In particular, the system topology, control mechanisms, and communication protocols are explained in this paper. The concept has been validated through simulations and real-scale laboratory tests, demonstrating its effectiveness. Key results highlight the system’s ability to maintain the DC bus voltage while operating at high efficiency (ranging from 97% to 98%) under different load conditions, supported by reliable and demanding real-time communication using the EtherCAT standard. This real-time capability has been validated, and related results are presented in this paper, showing a synchronization accuracy below 200 ns between two modules and a stable control at a cycle time of 400 µs. This approach offers a promising solution for reducing greenhouse gas emissions in the maritime industry, aligning with global sustainability goals.