Search Results (390)

Electrical discharge machining (EDM) demonstrates significant advantages in machining difficult-to-cut materials, particularly those with high hardness and brittleness, owing to its thermally driven material removal mechanism in which the arc plasma serves as the heat source. However, machining performance varies markedly across different workpiece materials. Such differences are likely attributable to the coupled effects of arc plasma characteristics, which may vary with tool–workpiece material combinations, and the thermophysical properties of the workpiece. Nevertheless, the mechanisms underlying this coupling remain poorly understood. In this study, arc plasma characteristics and material removal behavior under different material combinations were investigated using arc plasma and thermo-hydrodynamic simulation models. Under positive polarity, a copper tool electrode was paired with 304 stainless steel, Ti-6Al-4V, and Inconel 718 workpieces, while copper and tungsten electrodes were compared using a 304 stainless steel workpiece. Simulation results show that material combinations significantly affect anode heat flux and energy distribution, with 304 stainless steel exhibiting the highest heat flux and Inconel 718 receiving the largest energy distribution ratio. Crater depth correlates strongly with heat flux magnitude, while crater diameter is jointly determined by heat flux radius and melt flow dynamics, with the selected cathode material exerting only minor influence. High-speed imaging and crater morphology measurements validate the simulation results, confirming model reliability. These findings provide theoretical guidance for process optimization in EDM. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

The Eddy Covariance station provides observations of agrometeorological variables and surface energy fluxes, collected from 2019 to 2022, in a citrus orchard located in the Souss-Massa plain, Morocco. The present dataset comprises measurements recorded via a set of aboveground and subsurface sensors. The aboveground setup consistently measures air temperature, relative humidity, wind speed, net radiation, and precipitation. Additionally, the subsurface setup continuously tracks soil temperature, moisture, and electrical conductivity at depths from 5 to 80 cm, along with soil heat flux. Moreover, these setups enable the measurement of turbulent fluxes (sensible and latent heat). Given the limited availability of long-term agrometeorological data in semi-arid regions of the Mediterranean, this paper addresses a critical data gap by providing a reliable agrometeorological dataset. The latter consists of two types of data: 30 min interval files and high-frequency files (20 Hz, i.e., one measurement every 50 ms). The processing of this data involved Card Convert, MATLAB EC-Pack, and Excel, with data quality control performed by removing outliers and excluding nighttime fluxes. The dataset is organized in a table and provided in a .csv format with standard metadata. It is designed for a wide range of applications, including evapotranspiration modeling, satellite product validation, agroclimatic monitoring, determining crop irrigation requirements, precision irrigation planning, and water management. Additionally, the dataset can be reused for crop and hydrological model calibration, as well as soil moisture and crop stress prediction using machine learning algorithms. Full article

The electrical drive and the electrical motor share the same housing in Integrated Motor Drives (IMDs), which directly affects the reliability of failure-prone components in such a system. Existing studies are often conducted without considering system-level electrothermal reliability interactions in IMDs. This paper proposes a framework for electrothermal modeling for reliability analysis of IMDs during the early design phase. The framework is based on a back-to-back converter as an emulation platform adaptable to different high-speed electrical machines through software reconfiguration alone. It follows two stages: first, it converts the real-world mission profile, including high-speed operation, into load current commands and motor power loss. Secondly, the thermal network modeling accounts for thermal coupling between the components and the motor, which affects the junction temperature and the hot-spot temperature of the DC link capacitor. The parameters of the thermal network of the electrical motor can be the result of a multiphysics simulation or a real available motor. This framework enables reliability assessment considering motor thermal effects in the early design phase without requiring a physical motor prototype, while providing a fast and cost-effective approach for reliability evaluation. The experimental tests are performed to validate an electrothermal modeling framework capable of thermal modeling and reliability analysis. In addition, the reliability analysis of the power device is carried out by doing the simulation results using data from a real motor, selected for integrated power converter applications. The results demonstrate that the thermal interaction between the motor and the electrical drive causes an $11.5\%$ reduction in the predicted $B_{10}$ lifetime compared with the non-integrated configuration. The non-integrated configuration exhibits approximately 20,000$\mathrm{km}$ longer lifetime than the integrated configuration, highlighting the importance of considering motor-drive thermal coupling in IMD reliability assessment. Full article

With the increasing complexity of electrical and electronic (E/E) components in modern powertrains, functional safety requires more systematic assessment methods. This paper presents a simulation-based approach for automated Hazard Analysis and Risk Assessment (HARA) of battery electric vehicles in accordance with ISO 26262. The method combines operational-situation parameters, including vehicle speed, road surface, vehicle gap, and road inclination, to define a structured set of hazardous events. Severity, Exposure, and Controllability are evaluated using rule-based criteria, including a dedicated Controllability rule set for longitudinal motion hazards. Quantitative erroneous acceleration and deceleration thresholds associated with different ASILs are derived using a bisection search algorithm, enabling quantifiable and testable safety goals. Comparison with manual HARA reveals systematic biases: low speed does not necessarily imply improved Controllability due to shorter vehicle gaps, while Severity may be underestimated at low speeds because of high instantaneous electric-machine torque. The maximum ASIL is often identified similarly by simulation and experts, whereas lower-ASIL hazardous events may lack consistency and coverage in manual HARA. As a practical application, the approach can be integrated into existing automotive safety workflows as a HARA support tool, improving the consistency of lower-ASIL events while allowing engineers to focus on maximum ASIL cases. Full article

High-performance sensorless operation in multiphase electric drives requires speed estimation techniques capable of providing fast dynamic response, reduced oscillatory behavior, and low implementation complexity. In this context, a sliding-mode observer (SMO) based on an exponential reaching law (ERL) is proposed for rotor speed estimation in asymmetrical six-phase induction machines operating under indirect rotor field-oriented control. Unlike conventional SMO implementations, the proposed approach avoids auxiliary low-pass filtering (LPF) stages by employing an ERL-based adaptive gain mechanism, thereby preventing the phase delay and bandwidth reduction commonly associated with LPF-based observers. As a result, the proposed observer preserves fast transient dynamics, attenuates chattering near the sliding surface, and improves the smoothness of the estimated signals. The proposed technique is particularly suitable for multiphase drive applications, where sensorless operation reduces hardware complexity and improves system reliability by eliminating mechanical speed sensors and associated wiring. A Lyapunov-based stability analysis is presented to demonstrate the convergence properties of the observer and discuss the influence of the ERL parameters on the estimation dynamics. Simulation and experimental results obtained on a real-time test bench validate the digital implementation of the proposed SMO + ERL, demonstrating improved transient tracking, smoother estimated signals, stable low-speed operation, satisfactory speed reversal performance, and effective operation under loaded conditions. Full article

Four-wheel-drive electric vehicles equipped with independently controllable driving, braking, and steering actuators provide additional degrees of freedom for reducing electric-machine and tire-loss energy. This paper presents a real-time longitudinal–steering coordinated torque-vectoring framework for a vehicle driven by four in-wheel motors. A model-predictive active-front-steering controller coordinates the front-wheel steering angle and external yaw moment to reduce steering resistance and lateral tire-slip loss. A reduced inter-axle propulsion problem is analyzed using the Karush–Kuhn–Tucker conditions, and its speed-dependent switching threshold is calibrated offline by particle swarm optimization. Regenerative braking is allocated by an Energy-Optimized Distribution curve subject to ideal-distribution and regulatory constraints. For general positive-torque operation, sequential quadratic programming distributes the four wheel torques by considering motor input power, tire-slip energy, total torque, yaw-moment demand, and actuator limits. Hardware-in-the-loop results under the United States high-acceleration driving cycle and a double-lane-change maneuver show that the proposed strategy reduces energy consumption relative to uniform and tire-utilization-based torque-vectoring strategies. Full article

High-speed electric machines enhance power density and eliminate the need for a gearbox in waste heat recovery microturbine systems. However, existing designs often suffer from high manufacturing costs and complex cooling requirements. This study presents the development, experimental validation, and comparative analysis of three high-speed machine designs. First, a lower-speed induction machine prototype, constructed using standardized components, was tested at an operating speed of 13,000 rpm. This prototype enabled experimental validation of the numerical model used for loss calculations. Experimental results showed total losses of 7.89 kW, closely matching the simulated value of 7.75 kW at an output power of 93.1 kW, i.e., an efficiency of 92.19%. Building on these findings, two smaller machine prototypes were developed: one featuring an induction squirrel-cage rotor and the other employing a surface-mounted permanent magnet rotor topology. Both machines were designed and evaluated using finite element analysis and conjugate heat transfer simulations. Their performance was analyzed under both sinusoidal and pulse-width-modulated voltage supply conditions. At an operating speed of 14,000 rpm, the permanent magnet machine outperformed the induction machine, achieving 63.2 kW of mechanical power and an efficiency of 96.21%, while operating at lower temperatures. In comparison, the induction machine delivered 52.4 kW of mechanical power with an efficiency of 94.64%. The primary novelty and contribution of this work lie in the implementation of a two-pole machine architecture capable of achieving an output power of 100 kW at operating speeds between 20,000 and 25,000 rpm. Compared with similar solutions reported in the literature, the proposed machines feature a simplified bearing arrangement and a more straightforward liquid-cooling system. These characteristics have the potential to reduce manufacturing costs and simplify maintenance during operation. Full article

Slotless permanent magnet synchronous motors (PMSMs) have recently attracted significant attention for robotic and high-speed machine applications because they eliminate cogging torque and reduce iron loss through the absence of stator teeth. However, removing the stator teeth increases the effective airgap, resulting in extremely low synchronous inductance and introducing electrical challenges such as reduced control stability and increased current harmonics during pulse-width modulation (PWM) operation in inverter drive systems. In this paper, the current harmonic characteristics of a slotless PMSM are investigated with respect to switching frequency. Based on the mathematical model, the effects of switching frequency on a slotless PMSM drive system employing a MOSFET inverter are analyzed. In addition, an experimental setup, including a 700 W slotless PMSM and a MOSFET inverter, is implemented to evaluate the current harmonic characteristics under different switching frequencies of 25 kHz, 50 kHz, and 99 kHz. The total harmonic distortion of current at 99 kHz is reduced by approximately 6.73%p and 3.57%p compared with operation at 25 kHz and 50 kHz, respectively. The experimental results demonstrate the influence of PWM operation on the current characteristics of the slotless PMSM and verify the importance of high switching frequency operation for improving current performance. Full article

A novel method for controlling the speed of interior permanent magnet synchronous motors (IPMSMs), known as the current-sensing-based dynamic direct voltage control method under the maximum torque per ampere (MTPA) concept, is introduced. This technique achieves precise tracking of machine velocity by determining the optimal combination of voltage amplitude and angle for each specific motor velocity and current/load condition. Unlike previous studies, this approach takes into account the transient model of the machine, resulting in improved accuracy during dynamic operating conditions compared with existing methods in the literature. Moreover, a comparative analysis is conducted involving different direct voltage MTPA speed drive approaches: the current-sensing dynamic direct voltage control (CS-DDVC) methodology, the simplified DDVC technique, and the static direct voltage MTPA control strategy. The well-known field-oriented control method is also included in the analysis. The dynamic methodology employs two tuning parameters to achieve the same MTPA objective while eliminating transient effects. Experimental results and quantitative assessment demonstrate that the proposed MTPA control methodology is a highly effective strategy, offering a respectable alternative to existing MTPA methods for driving IPMSMs. It enables the operation of IPMSMs under MTPA working conditions with high efficiency, making it suitable for a wide range of industrial applications. Experimental results demonstrate a reduction in speed dip from 120 rpm to 50 rpm at full load application and a 128% improvement in IAE compared to conventional DVC. From an engineering design perspective, the proposed control framework simplifies the drive-system architecture by eliminating cascaded current-control loops while maintaining effective transient dynamic performance suitable for embedded electric vehicle applications. Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Conventional photodetectors and image sensors deliver high-fidelity digital outputs but face a growing data-movement bottleneck: the energy and latency cost of transferring raw pixel streams to off-chip memory and processors increasingly dominates over both sensing and computation in modern machine-vision pipelines. An emerging response is the neuromorphic photodetector, a class of optoelectronic device that converts incident light into an electrical signal while simultaneously storing, modulating, and pre-processing that signal in a manner inspired by biological synapses and retinas. Over the past decade, demonstrations have spanned at least eight material platforms—organic semiconductors, organic–carbon-nanotube hybrids, perovskite and perovskite hybrids, metal oxides (including ultra-wide-bandgap and printable variants), wide-bandgap III-nitrides and 4H-SiC, two-dimensional materials, photo-memristors, and silicon CMOS in-sensor compute architectures—and have been realised through four distinct architectural families: phototransistor synapses, photo-memristors, heterojunction in-sensor compute, and linear photovoltaic neural networks. Here, we provide a quantitative cross-platform benchmark across forty in-scope articles, identify persistent photoconductivity as a near-universal device-physical substrate underlying synaptic functionality, characterise the responsivity–speed–energy trade-off structure observed across platforms, and present a critical assessment of energy-reporting practice in the field. We further identify three best-practice exemplars from three independent material platforms that converge on operating biases of 0.01–0.1 V and energies of 0.07–0.8 fJ per event, and we propose a unified reporting framework to enable meaningful cross-platform benchmarking of next-generation neuromorphic photodetectors. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

With the rapid development of big data and artificial intelligence, motor fault diagnosis has become increasingly important in the fields of intelligent transportation and new energy vehicles. As a core component of the electric vehicle drive system, the operating condition of an asynchronous AC motor is directly related to the safety and reliability of the vehicle powertrain. Once a motor fault occurs, it may lead to powertrain failure, thereby causing traffic accidents, financial losses, and even threats to human life, particularly under high-speed driving conditions where the safety risks are more severe. Therefore, the timely and accurate diagnosis of motor faults in electric vehicles, especially autonomous vehicles, is of great practical significance. To effectively capture the fault-related features embedded in the vibration and voltage signals of asynchronous AC motors and efficiently perform fault diagnosis, this paper introduces a fault-diagnosis model that integrates the exponential moving average (EMA), a two-dimensional multi-scale convolutional neural network (2DMSCNN), and the Squeeze-and-Excitation Network (SENet) mechanism. Experimental validation based on a publicly available asynchronous AC motor fault-diagnosis dataset demonstrates that, compared with traditional machine learning models and ensemble learning methods, the proposed EMA-2DMSCNN-SENet model achieves higher diagnostic accuracy and stronger robustness. Full article

The explosive growth of electric vehicle (EV) charging infrastructure is increasingly straining power distribution networks, but the at-scale behavioral heterogeneity of charging stations remains poorly understood. In this study, we implement an unsupervised machine learning approach based on real data (encompassing 32,057 EV charging stations in the publicly available dataset of the Republic of Korea) to discover hidden load concentration patterns. We applied K-means clustering (k = 6) with the k-means++ initialization method to seven station-level features, which yielded six behavioral archetypes that were further evaluated using four supervised classifiers (Decision Tree, Logistic Regression, Random Forest, and XGBoost), all achieving an F1 macro ≥ 0.994 and ROC-AUC ≥ 0.999. The SHAP analysis revealed that geographic variables mainly explain the differentiation among low-use slow-charging sub-clusters, whereas operational variables such as session frequency, output capacity, charger type, and charging speed are decisive for the load-relevant C3 and C5 archetypes. We introduced three new grid load metrics: cluster load contribution, load imbalance coefficient of variation (CV = 1.1247), and the hidden load effect. Results indicate that the high-power fast cluster (C5) and high-use slow cluster (C3) combine to contribute 66.7% of the network station load score-based load while representing only 19.2% of stations. Under the station load score proxy assumption, C3 demonstrates 14.4% greater per-station utilization intensity than C5 (293.6 vs. 256.7), challenging the notion that fast chargers are the key source of infrastructure pressures. These insights provide actionable guidance for demand-side management approaches. Full article