Search Results (8,126)

This work introduces two complementary surface electromyography (sEMG) datasets for hand gesture recognition. Signals were collected from 40 healthy subjects aged 18 to 40 years, divided into two independent groups of 20 participants each. In both datasets, subjects performed five hand gestures. Most of the gestures are the same, although the exact set and the order differ slightly between datasets. For example, Dataset 2 (DS2) includes the simultaneous flexion of the thumb and index finger, which is not present in Dataset 1 (DS1). Data were recorded with three bipolar sEMG sensors placed on the dominant forearm (flexor digitorum superficialis, extensor digitorum, and flexor pollicis longus). A battery-powered acquisition system was used, with sampling rates of 1000 Hz for DS1 and 1500 Hz for DS2. DS1 contains recordings performed at a constant moderate force, while DS2 includes three force levels (low, medium, and high). Both datasets provide raw signals and pre-processed versions segmented into overlapping windows, with clear file structures and annotations, enabling feature extraction for machine learning applications. Together, they constitute a large-scale standardized sEMG resource that supports the development and benchmarking of gesture and force recognition algorithms for rehabilitation, assistive technologies, and prosthetic control. Full article

Telecommunication Base Transceiver Stations (BTSs) require a resilient and sustainable power supply to ensure uninterrupted operation, particularly during grid outages. Thus, this paper proposes an Adaptive Model Predictive Control (AMPC)-based Energy Management System (EMS) designed to optimize energy dispatch and demand response for a BTS powered by a renewable-based microgrid. The EMS operates under two distinct scenarios: (a) non-grid outages, where the objective is to minimize grid consumption, and (b) outage management, aiming to maximize BTS operational time during grid failures. The system incorporates a dynamic weighting mechanism in the objective function, which adjusts based on real-time power production, consumption, battery state of charge, grid availability, and load satisfaction. Additionally, a demand response strategy is implemented, allowing the BTS to adapt its power consumption according to energy availability. The proposed EMS is evaluated based on BTS loss of transmitted data under different renewable energy profiles. Under normal operation, the EMS is assessed regarding grid energy consumption. Simulation results demonstrate that the proposed AMPC-based EMS enhances BTS resilience. Full article

This paper describes the modeling, simulation, and experimental validation of a Hybrid supercapacitor–battery Energy Storage System (HESS) for photovoltaic (PV) modules under partial shading. The system is intended to provide an uninterruptible power supply for a DC primary load. The Hybrid Power System (HPS) architecture includes a DC/DC boost converter with a Maximum Power Point Tracking (MPPT) algorithm that optimizes photovoltaic (PV) energy extraction. Furthermore, two bidirectional DC–DC converters are dedicated to the battery and supercapacitor subsystems to allow the bidirectional power flow within the HPS. The proposed HESS is evaluated through MATLAB/Simulink simulations and experimentally validated on a prototype using real-time hardware based on the dSPACE DS1104. To optimize power flow within the HPS, two energy management strategies are implemented: the Thermostat-Based Method (TBM) and the Filter-Based Method (FBM). The results indicate that the thermostat-based strategy provides better battery protection under shading conditions. Indeed, with this approach, the battery can remain in standby for 300 s under total permanent shading (100%), and for up to 30 min under dynamic partial shading, thereby reducing battery stress and extending its lifetime. Full article

With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this study, aging models for power batteries in electric passenger vehicles and electric trucks are established. A time-of-use electricity price model and an economic model considering battery aging costs are constructed. Two scenarios were established for daily use and V2G operation. The impacts of different scenarios and charging/discharging patterns on battery life and user profit are analyzed. The results indicate that the additional V2G discharging process increases the cyclic aging rate of EV batteries. Within the studied parameter ranges, the cyclic aging rate increased by 5.89% for electric passenger vehicles and 3.72% for electric trucks, respectively. Additionally, the initial V2G revenue may struggle to cover early-stage battery aging costs, but the subsequent slowdown in degradation may eventually offset these costs. With appropriate charging and discharging strategies, the maximum revenue per year reaches 18,200 CNY for electric trucks and 5600 CNY for electric passenger vehicles. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios. Full article

Energy storage systems (ESSs) have recently emerged as a common solution for mitigating the variability of intermittent renewable energy sources. A major challenge linked to ESSs is their expense. This study focuses on investigating techniques to decrease the size of an ESS while maintaining its performance levels. Data were gathered from a grid-connected 2MW PV system in Malaysia over multiple days, with numerous variables showing considerable hour-to-hour variations from hour to hour due to solar irradiation. A Python code was created to examine the impact of different ESS sizes on power grid stabilization utilizing the power conservation technique. The suggested ESS size derived from the program outcomes was evaluated utilizing a hybrid ESS, incorporating a vanadium redox battery (VRB) as the high-energy-density component and supercapacitors (SCs) as the high-power-density component. The effects of altering the output period lengths were examined. The result must stay consistent for at least five minutes as the minimum required duration. The findings show that an ESS capacity of approximately 10% of the overall produced power can meet the above duration requirement. A straightforward test was employed in the system to assess the power generation level in the upcoming time period. Simulink was employed to model the produced system, and the outcomes met the ESS requirements, enhancing efficiency and extending the battery lifespan. Full article

Substations’ auxiliary systems support the station’s operational loads and are crucial for grid security, often requiring backup power to ensure uninterrupted operation. A new alternative for this backup power supply is a microgrid composed of photovoltaic (PV) generation and storage. This paper proposes an electric–hydrogen microgrid as backup power supply for substation auxiliary systems. This microgrid ensures power supply during emergencies, provides clean and stable energy for daily operations, and enhances environmental friendliness and profitability. Firstly, using a 220 kV substation as an example, the construction principles of the proposed backup power microgrid are introduced. Secondly, operation strategies under different scenarios are proposed, considering time-sharing tariffs and different weather conditions. Following this, the capacity configuration optimization model of the electric–hydrogen microgrid is proposed, incorporating critical thresholds for energy reserves to ensure system robustness under fault conditions. Finally, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and a sensitivity analysis is performed on hydrogen market pricing to evaluate its impact on the system’s economic feasibility. The results indicate that the proposed electric–hydrogen microgrid is more economical and provides better fault power supply time than battery-only power supply. With the development of hydrogen energy storage technology, the economy of the proposed microgrid is expected to improve further in the future. Full article

This study presents a dynamic energy management algorithm (DEMA) designed for hybrid battery–ultracapacitor systems in uninterruptible power supply (UPS) applications. The proposed algorithm aims to enhance power reliability and extend battery life by dynamically coordinating energy flow between the battery and ultracapacitor under various operating modes. A single-phase UPS system was modeled and simulated in MATLAB/Simulink (Matlab R2025a version), and subsequently validated through experimental tests using an energy analyzer and an oscilloscope. The DEMA identifies and manages five operating modes, ensuring smooth transitions between grid-connected and backup states. During sudden load variations, particularly at a 1500 W step change, the ultracapacitor effectively supports the battery by supplying transient power, thereby reducing current stress and preventing deep discharge. Both simulation and experimental results confirm that the proposed algorithm maintains stable DC bus voltage, improves dynamic response, and achieves optimal energy utilization across all modes. The developed hybrid UPS control approach demonstrates high reliability and can be effectively implemented in critical load systems requiring uninterrupted power and enhanced battery longevity. Full article

To achieve the coordinated optimization of economic and low-carbon objectives in integrated energy systems, this study develops a synergistic scheduling model combining electric vehicle clusters (V2G) with Power-to-Gas and Carbon Capture and Storage (P2G–CCS) technologies. The system integrates renewable generation (wind and solar) with conventional units, forming an integrated pathway for carbon capture and utilization through the P2G–CCS process. A virtual battery model is adopted to aggregate electric vehicles, whose flexibility is characterized by frequency regulation capacity constraints. Both battery degradation cost and V2G revenue are incorporated into a unified framework to assess the economic feasibility of EV participation. To address the stochastic and volatile nature of renewable generation, typical scenarios are generated through Monte Carlo sampling and scenario reduction for scheduling optimization. Case study results reveal that EVs achieve peak shaving and valley filling through off-peak charging and peak discharging, reducing the total system cost by 5.2%, with V2G revenue offsetting nearly 91% of degradation cost. The coordinated P2G–CCS operation shows remarkable carbon reduction potential, decreasing carbon trading and sequestration costs by approximately 46%. Overall, the proposed model effectively enhances both the economic and environmental performance of the integrated energy system, providing practical guidance for its low-carbon optimal operation. Full article

This paper presents an optimal-design methodology for small-scale Integrated Energy Systems (IESs) that couple electricity and heat in distributed networks. A hybrid reliability assessment integrates probabilistic state enumeration with scenario-based simulation. Mathematically, the design is cast as a stochastic, reliability-driven ranking: time-sequential Monte Carlo (MC) produces estimators of Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Self-Sufficiency Rate (SSR), which are normalized and combined into a Composite Reliability Index (CRI) that orders candidate siting/sizing options. The case study is the D-campus microgrid with Photovoltaic (PV), Combined Heat and Power (CHP), Fuel Cell (FC), Battery Energy Storage Systems (BESSs), and Heat Energy Storage Systems (HESSs; also termed TESs), across multiple siting and sizing scenarios. Results show consistent reductions in LOLP and EENS and increases in SSR as distributed energy resource capacity increases and resources are placed near critical nodes, with the strongest gains observed in the best-performing configurations. The CRI also reveals trade-offs across intermediate scenarios. The operational concept of the campus Energy Management System (EMS), including full operating modes and scheduling logic, is developed to maintain a design focus on reliability-driven decision making. Probability-based formulations, reliability metrics, and the sequential MC setup underpin the proposed ranking framework. The proposed method supports Distributed Energy Resource (DER) sizing and siting decisions for reliable, autonomy-oriented IESs. Full article

To build robust condition monitoring solutions, it is important to identify signals that capture relevant information. However, how a degradation affects a given part of machinery might not be clear at the beginning. As a result, exploration measurement campaigns collecting large amounts of data are needed for initial evaluation. Vibration signals are typical examples of such data. Although, for explorative measurement campaigns, the battery-powered wireless node brings extra flexibility in terms of positioning the sensor at the desired location and facilitates retrofitting, the limited energy posed by them is the major downside. Sending high-sampled data over wireless channels is costly energy-wise if all samples are to be sent. When multiple sensor nodes transmit real-time measurement data concurrently over a wireless channel, the risk of channel saturation increases significantly. Avoiding this requires identifying an optimal balance between sampling time, transmission duration, and payload size. This can be done by processing and compressing data before transmission, on the sensor node close to the data acquisition and later reconstructing the received samples on the central node. In this paper, we analyze two compression mechanisms to ensure a good compression ratio and still allow good signal reconstruction for later analysis. We study two approaches, one based on the Fast Fourier Transform and one on Singular Value Decomposition, and discuss the pros and cons of each variant. Full article

The Internet of Things (IoT) has rapidly evolved into a central component of today’s technological landscape, enabling seamless connectivity and communication among a vast array of devices. It underpins automation, real-time monitoring, and smart infrastructure, serving as a foundation for Industry 4.0 and paving the way toward Industry 5.0. Despite the potential of IoT systems to transform industries, these systems face a number of challenges, most notably the lack of processing power, storage space, and battery life. Whereas cloud and fog computing help to relieve computational and storage constraints, energy limitations remain a severe impediment to long-term autonomous operation. Among the threats that exploit this weakness, the Denial-of-Sleep (DoSl) attack is particularly problematic because it prevents nodes from entering low-power states, leading to battery depletion and degraded network performance. This research investigates machine-learning (ML) and deep-learning (DL) methods for identifying such energy-wasting behaviors to protect IoT energy resources. A dataset was generated in a simulated IoT environment under multiple DoSl attack conditions to validate the proposed approach. Several ML and DL models were trained and tested on this data to discover distinctive power-consumption patterns related to the attacks. The experimental results confirm that the proposed models can effectively detect anomalous behaviors associated with DoSl activity, demonstrating their potential for energy-aware threat detection in IoT networks. Specifically, the Random Forest and Decision Tree classifiers achieved accuracies of 98.57% and 97.86%, respectively, on the held-out 25% test set, while the Long Short-Term Memory (LSTM) model reached 97.92% accuracy under a chronological split, confirming effective temporal generalization. All evaluations were conducted in a simulated environment, and the paper also outlines potential pathways for future physical testbed deployment. Full article

Cross-seasonal and cross-regional operations make it inevitable for low-temperature cycling of lithium–ion batteries, which accelerates battery aging and induces large inconsistency between batteries in the battery pack. This causes slight overcharging. However, the influence of long low-temperature cycling on the following slight overcharging aging and aging mechanism under multi aging path is not studied clearly. This affects the function of the battery management system (BMS), including state of health (SOH) prediction, state of charge estimation, etc. This work takes 18,650-type batteries as the study objects. Battery aging at low temperature (−10 °C) and slight overcharging (4.4 V) aging after low-temperature cycling are studied in this work. Hybrid pulse power characteristic, incremental capacity analysis, scanning electron microscope, and X-ray diffraction are used to reveal the aging mechanisms. The results indicate that a negative electrode degradation affects the cycle life of batteries more compared to a positive electrode, and the primary aging mechanisms are “dead lithium” and electrolyte decomposition. Compared to low-temperature cycling, slight overcharging is the lower stress factor. Cycling at low stress factor suppresses aging of battery cycled at high stress factor. When the SOH of battery is near 90%, lithium plating growing at low temperature is consumed after slight overcharging cycling. The generated products suppress further lithium plating. When the SOH is near 80%, although lithium plating is consumed, it also grows continuously. Slight overcharging causes more transition metal dissolution and graphite exfoliation. When SOH is near 90%, thermal management strategies should operate to control operation temperature of battery to avoid further low-temperature cycling. The results in this work are important to battery design and battery management system development. Full article

The purpose of this study is to monitor the battery performance of plug-in hybrid electric vehicles (PHEVs) on a chassis dynamometer using the US06, NEDC, and EPA highway driving cycles. The chassis dynamometer simulates vehicle operation and driving conditions and allows for precise simulation of pre-defined driving cycles, including simulations of acceleration, deceleration, stopping, and re-acceleration on the road. In the case of the US06 driving cycle, the results for (EV mode) compared with energy consumption during electric testing revealed a consistent decrease in the SOC (state of charge) due to the rapid response of the electric motor distribution to the changing power, as well as electric power fluctuations during driving conditions. Under the NEDC, the test results for electric power (EV) compared with energy consumption during electric testing revealed that the SOC gradually decreased at the start of the test due to low driving speeds. Towards the end, at around 800 s, an increase in driving speed resulted in a noticeable drop in SOC. The electric power varied during the driving cycle in this test due to the motor’s rapid response to changes in power distribution while driving. For the EPA Highway driving cycle test, the test results for electric power (EV) compared with energy consumption during continuous electric testing indicated a gradual decrease in the SOC at first due to low driving speeds. As the driving speed increased after about 300 s, the SOC rapidly decreased. Because of the motor’s quick response to changes in the power distribution while driving, the electric power varied according to the driving cycle. Full article

This paper presents the validation of a voltage balancing converter for a bipolar DC microgrid designed to ensure reliable operation in both grid-connected and islanded modes. This microgrid includes unipolar constant power loads (CPL), a unipolar Battery Energy Storage System (BESS), and local PV generation. The BESS converter employs a V–I droop strategy using only inductor current feedback, reducing sensing requirements while maintaining plug-and-play capability and ensuring smooth transitions between connected and islanded modes. In such a microgrid, the voltage balancing converter regulates the differential voltages under severe unbalanced load conditions and during transients caused by changes in unipolar loads and sources. The experimental results validate the voltage balancing strategy across various scenarios in a small-scale prototype. The results show tight voltage regulation under unbalanced conditions, and smooth transitions during load transients and unintentional islanding, even if there is no dc voltage source in one of the poles of the bipolar dc bus. For both conditions, the imbalance between the unipolar voltages is less than 0.5% of the total bipolar voltage. Full article

The rapid development of Internet of Things (IoT) devices has raised challenges in hardware security, as these devices often transmit sensitive data. Physically Unclonable Functions (PUFs) provide a promising approach to address such security concerns. However, high power consumption limits the efficiency of PUFs and reduces battery life in IoT devices, making low-power operation essential while generating secure keys. Adiabatic logic offers a method to reduce energy dissipation in CMOS circuits. By combining these concepts, adiabatic-based PUFs utilize both CMOS process variations and adiabatic logic principles to achieve low-power operation while maintaining high security. In this paper, a low-power 6-transistor (6T) adiabatic PUF circuit is designed and evaluated through simulation using 0.18 μm CMOS process. The simulation is performed under three body-bias conditions, where the PMOS transistor body is connected to Vdd, Vpc, or the source, and the results show that the proposed PUF achieves high key metrics including reliability and uniqueness close to their ideal values. In addition, it achieves an energy dissipation of 15.10 fJ/Cb-cycle per bit, reducing energy dissipation by over 60% compared to the conventional quasi-adiabatic design. Furthermore, by reducing the number of transistors compared to the conventional ultra-low-power design, the proposed circuit achieves a smaller implementation area. Full article