Search Results (988)

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Kidding in goats is a highly significant event with major economic implications and strong impacts on the welfare of both the offspring and the mothers. Monitoring the process is extremely demanding, as it is impossible to predict precisely when it will occur. For this reason, the automatic detection of kidding has the potential to generate substantial productivity gains while also improving animal well-being. Artificial intelligence techniques based on accelerometry data have been explored for identifying the event, but these approaches typically rely on data loggers, which cannot trigger real-time alerts or assistance. Embedding detection mechanisms directly into wearable devices enables much faster identification and supports energy-efficient operations. However, this approach also introduces considerable challenges, particularly due to the strict constraints of wearable devices in terms of weight, cost, and battery life. The present work documents the development of a real-time, automatic kidding-detection mechanism in which the detection workload is distributed between the collar and an edge device. System evaluation demonstrated the feasibility of this distributed architecture, confirming that both components can cooperate effectively to achieve reliable detection. The system achieved a Matthews Correlation Coefficient performance of 0.91, highlighting the robustness and practical viability of the proposed solution. Full article

Thermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control. Full article

Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is essential to prevent miscuts and maintain power-communication functionality. However, detecting small, twisted OPGW segments among visually similar ground wires is challenging, particularly given the computational and energy constraints of edge-based UAV platforms. We propose OPGW-DETR, a lightweight detector based on the D-FINE framework, optimized for low-power operation to enable reliable detection. The model incorporates two key innovations: multi-scale convolutional global average pooling (MC-GAP), which fuses spatial features across multiple receptive fields and integrates spectrally motivated features for enhanced fine-grained representation, and a hybrid gating mechanism that dynamically balances global and spatial features while preserving original information through residual connections. By enabling real-time inference with minimal energy consumption, OPGW-DETR addresses UAV battery and bandwidth limitations while ensuring continuous detection capability. Evaluated on a custom OPGW dataset, the S-scale model achieves 3.9% improvement in average precision (AP) and 2.5% improvement in AP₅₀ over the baseline. By mitigating misidentification risks, these gains improve communication reliability. As a result, uninterrupted grid monitoring becomes feasible in low-power UAV inspection scenarios, where accurate detection is essential to ensure communication integrity and safeguard the power grid. Full article

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users. Full article

Self-powered sensors, leveraging their integrated energy harvesting–signal sensing capability, effectively overcome the bottlenecks of traditional sensors, including reliance on external power resources, high maintenance costs, and challenges in large-scale distributed deployment. As a result, they have become a major research focus in fields such as flexible electronics, smart healthcare, and human–machine interaction. This paper reviews the core technical paths of six major types of self-powered sensors developed in recent years, with particular emphasis on the working principles and innovative material applications associated with frictional charge transfer and electrostatic induction, pyroelectric polarization dynamics, hydrovoltaic interfacial streaming potentials, piezoelectric constitutive behavior, battery integration mechanism, and photovoltaic effect. By comparing representative achievements in fields closely related to self-powered sensors, it summarizes breakthroughs in key performance indicators such as sensitivity, detection range, response speed, cyclic stability, self-powering methods, and energy conversion efficiency. The applications discussed herein mainly cover several critical domains, including wearable medical and health monitoring systems, intelligent robotics and human–machine interaction, biomedical and implantable devices, as well as safety and ecological supervision. Finally, the current challenges facing self-powered sensors are outlined and future development directions are proposed, providing a reference for the technological iteration and industrial application of self-powered sensors. Full article

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates the establishment of complex sensor networks to enable detailed multi-parameter monitoring of items. Despite these advancements, challenges remain in item-level sensing, data analysis, and the management of power consumption. To mitigate these shortcomings, this study presents a holistic AI-assisted, semi-passive RFID-integrated multi-sensor system designed for robust food quality monitoring. The primary contributions are threefold: First, a compact (45 mm ∗ 38 mm) semi-passive UHF RFID tag is developed, featuring a rechargeable lithium battery to ensure long-term operation and extend the readable range up to 10 m. Second, a dedicated IoT cloud platform is implemented to handle big data storage and visualization, ensuring reliable data management. Third, the system integrates machine learning algorithms (LSTM) to analyze sensing data for real-time food quality assessment. The system’s efficacy is validated through real-world experiments on food products, demonstrating its capability for low-cost, long-distance, and intelligent quality control. This technology enables low-cost, timely, and sustainable quality assessments over medium and long distances, with battery life extending up to 27 days under specific conditions. By deploying this technology, quantified food quality assessment and control can be achieved. Full article

Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies. Full article