Search Results (203)

Accurately predicting the capacity degradation of lithium-ion batteries is crucial for ensuring the reliability and safety of electric vehicles and energy storage systems. However, existing methods—including those based on physical principles, deep learning, and traditional machine learning—all face challenges in balancing accuracy, computational efficiency, and adaptability to non-linear aging dynamics. This study proposes a new framework that combines multi-scale data preprocessing and a divide-and-conquer strategy to address these limitations. Firstly, a hybrid Wavelet–SG filter is applied to suppress noise, and a set of specialized XGBoost micro models is trained, with each model predicting capacity for a specific cycle, enabling precise trajectory prediction at different aging stages. The evaluation on the Toyota-MIT-Stanford dataset (118 batteries under different operating protocols) shows that this method achieves an average MAPE of 1.16% and a maximum of no more than 2.5% on the unfamiliar protocol test set. In terms of accuracy, it achieves performance comparable to CNN, LSTM, and CNN-LSTM benchmarks. Importantly, its parallel architecture enables fast inference (400 milliseconds on CPU), making it suitable for edge deployment in battery management systems. The model also has interpretability consistent with physical laws and can autonomously capture stage-dependent degradation mechanisms. This work provides a reliable, efficient, and interpretable solution for real-world battery health monitoring. Full article

To address insufficient dynamic reactive power support during large-scale new energy grid connection, synchronous condensers are widely used in centralized new energy delivery. As a special rotating electrical machine, their operational stability is critical to new energy power stations’ safe operation. Targeting practical application scenarios (synchronous condenser power delivery and new energy grid-connected systems with synchronous condensers), this paper establishes a simplified Heffron–Phillips model for their static stability analysis by integrating their actual operating characteristics into the traditional model. Specific electromagnetic torque component expressions are derived to reflect static stability. Mechanistically, it reveals the correlation between excitation system gain and torque, their impact on static synchronous stability, and obtains critical gain parameters for positive damping. Influencing factors are determined, and essential differences in additional torque characteristics between synchronous condensers and generators are clarified. Simulation models of the two systems verify the conclusions, providing theoretical support for engineering applications and a reference for practical parameter setting. Full article

The Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) has gained significant attention as a core power source for next-generation industrial sectors, including electric vehicles, wind turbines, robot joints, and drone propulsion motors, due to its high power density from a short axial length and large radial dimensions. Despite these structural advantages, cogging torque caused by magnetic interaction between the stator teeth and permanent magnets remains a critical drawback, inducing noise and vibration. While conventional Soft Magnetic Composite (SMC) core methods facilitate 3D flux paths, they suffer from low magnetic permeability, insufficient mechanical strength, and manufacturing complexity. To address these issues, this study proposes a stepped structure model utilizing electrical steel sheets to effectively reduce cogging torque. This structure features radial stacking of identical electrical steel sheets with varying widths, where each layer’s center is incrementally shifted in the rotational direction. This configuration achieves an effect analogous to continuous skewing without specialized 3D machining. To validate the proposed design, 3D Finite Element Analysis (FEA) was conducted. Results demonstrate that the peak-to-peak cogging torque was reduced to approximately 86% of the conventional model’s value, while maintaining the back-EMF reduction rate within 5%. By presenting a novel skewing technique, this research provides a practical alternative for high-precision and high-power AFPMSM. Full article

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable. Full article

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

Amorphous soft magnetic materials (AMMs) have demonstrated significant advantages in electric machines due to their low core losses, high permeability, high tensile strength, and superior energy efficiency at high operating frequencies. Despite these benefits, their adoption in electric vehicle (EV) motors remains limited. This review explores the key technological, economic, and industrial barriers preventing the widespread use of AMMs in EV applications. An overview of the AMM fundamentals, including the material composition, manufacturing processes, and recent advancements, is first presented. To quantitatively assess their potential in traction applications, a numerical study is conducted on two 5.5 kW synchronous reluctance machines with identical geometries, employing AMM and conventional silicon steel stators, respectively. The machines are compared in terms of electromagnetic torque and efficiency, highlighting the impact of AMM properties on machine performance. These results are discussed alongside the findings from the existing literature to evaluate the core loss reduction, electromagnetic behavior, mechanical robustness, and thermal considerations. Special attention is given to the emerging commercial applications of AMMs in EV motors, which have only recently begun to materialize. Finally, the study highlights the gap between academic research and industrial implementation and identifies critical research areas needed to accelerate AMM adoption. Full article

This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η² ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X^¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ², Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. Full article

The emergence of new hard and extra-hard materials has led to the development of new technologies capable of processing them, known as unconventional technologies. Electrical discharge machining (EDM) is a very common unconventional technology in the manufacturing industry, used to process special materials. The primary benefit is the ability to machine various complex shapes at a reduced cost. This study addressed the use of intensive machining regimes that would enhance productivity while also maintaining a high quality of the resulting surface. The experimental setup was designed according to a D-optimal response surface method, and the results were statistically processed using ANOVA. The results revealed that it is possible to achieve both high productivity and good surface quality, but it was also found that increasing the processing parameters is feasible only to a certain extent. Full article

The shift toward decentralized energy systems and the rapid growth of renewable integration have brought renewed attention to the Multi-Area Economic Dispatch (MAED) problem. Unlike single-area dispatch, which focuses only on local balance, MAED must also coordinate inter-area exchanges, respect regional operating limits, and ensure overall reliability. This paper reviews both MAED and its dynamic extension, the Multi-Area Dynamic Economic Dispatch (MADED). The review examines core objectives—cost minimization, emission reduction, and renewable utilization—and surveys a wide range of solution methods. These include classical mathematical programming, metaheuristic and hybrid approaches, and more recent advances based on machine learning and reinforcement learning. Special emphasis is placed on uncertainty-oriented models that address demand variability, market dynamics, and renewable fluctuations. The discussion also highlights the role of Distributed Energy Resources (DERs), Energy Storage Systems (ESSs), and Demand Response (DR) in improving system flexibility and resilience. Despite notable progress, research gaps remain, including limited treatment of uncertainty, insufficient integration of DR, oversimplified modeling of electric vehicles, and the marginal role of reliability. To address these issues, a research agenda is proposed that aims to develop more adaptive, scalable, and sustainable dispatch models. The insights provided are intended to support both academic research and practical applications in the planning and operation of interconnected grids. Full article

Accurate modeling of Brushless DC (BLDC) motors is crucial for the multi-domain simulation of complex electromechanical systems like electric torque tools, especially when high fidelity is required for Model-Based Design (MBD) and controller validation. Standard BLDC models often employ simplifications that may not capture critical operational details. This paper presents a comparative analysis of four distinct BLDC motor simulation models: two based on ready-to-use MATLAB/Simulink/Simscape Electrical library blocks (Specialized Power Systems/Electrical Machines/Permanent Magnet Synchronous Machine and Electromechanical/Permanent Magnet/BLDC) and two custom models developed by the authors at AGH University. The models are evaluated based on their structure, underlying equations, and performance in simulating typical operational scenarios of an electric torque tool. Key assessment criteria include the ability to implement realistic (e.g., tabulated, non-ideal) back-EMF (electromotive force) profiles, incorporate cogging torque, model commutation effects, and flexibility for modification. Simulation results indicate that while all models can be suitable for basic control design, the custom-developed models offer greater flexibility and fidelity in representing detailed motor phenomena such as irregular back-EMF waveforms and cogging torque, making them better suited for advanced, high-precision applications. Conversely, standard library models, particularly the one underlying the PMSM block, exhibit limitations in custom back-EMF implementation. This study concludes by recommending models based on specific application requirements and outlines directions for future enhancements, including thermal modeling and iron loss representation. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and more farmers have decided to switch to autonomous (or semi-autonomous) field robots. In the past decade, an increasing number of robots has filled the market of agricultural machines all over the world. These machines can easily cover long and repetitive tasks, while operators can be employed in other jobs inside the farms. This paper reviews the current state-of-the-art of autonomous robots for agricultural operations, dividing them into categories based on main tasks, to analyze their main characteristics and their fields of applications. Seven main tasks were identified: multi-purpose, harvesting, mechanical weeding, pest control and chemical weeding, scouting and monitoring, transplanting and tilling-sowing. Field robots were divided into these categories, and different characteristics were analyzed, such as engine type, traction system, application field, safety sensors, navigation system, country of provenience and presence on the market. The aim of this review is to provide a global view on agricultural platforms developed in the past decade, analyzing their characteristics and providing future perspectives for next robotic platforms. The analysis conducted on 59 field robots, those already available on the market and not, revealed that one fifth of the platforms comes from Asia, and 63% of all of them are powered by electricity (rechargeable batteries, not solar powered) and that numerous platforms base their navigation system on RTK-GPS signal, 28 out of 59, and safety on LiDAR sensor (12 out of 59). This review considered machines of different size, highlighting different possible choices for field operations and tasks. It is difficult to predict market trends as several possibilities exist, like fleets of small robots or bigger size platforms. Future research and policies should focus on improving navigation and safety systems, reducing emissions and improving level of autonomy of robotic platforms. Full article

To explore possible design solutions for induction motors, we designed and tested a three-phase small-power induction motor with a can-type rotor and a stationary internal ferromagnetic core, a design not previously described in the technical literature. This three-phase motor combines certain features of a reliable solid-rotor motor, a two-rotor layer motor, and a motor in which the rotating thin aluminium layer is separated from the stationary inner ferromagnetic core. The motor prototype was based on a mass-produced, small-power, three-phase squirrel-cage motor. Its operating properties and characteristics were tested, highlighting its potential application as a special-purpose drive or a very interesting case for teaching purposes in laboratories of electrical machines. Measurements confirmed theoretical predictions and enabled the formation of a motor equivalent circuit with shunt and series branch parameters, among which magnetization reactance and rotor resistance varied with rotational speed. The main advantages of the motor are its simple rotor construction, low rotational speed, low-rotor inertia and good dynamics, as well as reliable operation across the entire range of useful torque from no-load to short-circuit conditions, without the risk of overheating. Full article

This review article provides an introductory overview of organic bioelectronics, focusing on the creation of electrical devices that use specialized carbon-based semiconducting materials to interact successfully with biological processes. These organic materials demonstrate flexibility, biocompatibility, and the capacity to carry both electrical and ionic impulses, making them an ideal choice for connecting human tissue with electronic technology. The review study examines diverse materials, such as the conductive polymers Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and Polyaniline (PANI), along with critical devices like organic electrochemical transistors (OECTs), which are exceptionally efficient for sensitive biosensing applications. Significant applications include implanted neural interfaces for the brain and nerves, wearable health monitoring, tissue engineering scaffolds that facilitate tissue repair, and sophisticated drug delivery systems. The review acknowledges current challenges, including long-term stability and safety, while envisioning a future where these technologies revolutionize healthcare, human–machine interaction, and environmental monitoring via continuous multidisciplinary innovation. Full article