Search Results (73)

Electric motors play a decisive role in electric vehicles by converting electrical energy into mechanical motion across various drivetrain components. However, failures in these motors can interrupt the motor function, with approximately 40% of these failures stemming from bearing issues. Key contributors to bearing degradation include shaft voltage, bearing current, and electric discharge material spalling current, especially in motors powered by inverters or variable frequency drives. This review explores the tribological and vibrational aspects of bearing currents, analyzing their mechanisms and influence on electric motor performance. It addresses the challenges faced by electric vehicles, such as high-speed operation, elevated temperatures, electrical conductivity, and energy efficiency. This study investigates the origins of bearing currents, damage linked to shaft voltage and electric discharge material spalling current, and the effects of lubricant properties on bearing functionality. Moreover, it covers various methods for measuring shaft voltage and bearing current, as well as strategies to alleviate the adverse impacts of bearing currents. This comprehensive analysis aims to shed light on the detrimental effects of bearing currents on the performance and lifespan of electric motors in electric vehicles, emphasizing the importance of tribological considerations for reliable operation and durability. The aim of this study is to address the engineering problem of bearing failure in inverter-fed EV motors by integrating electrical, tribological, and lubrication perspectives. The novelty lies in proposing a conceptual link between lubricant breakdown and damage morphology to guide mitigation strategies. The study tasks include literature review, analysis of bearing current mechanisms and diagnostics, and identification of technological trends. The findings provide insights into lubricant properties and diagnostic approaches that can support industrial solutions. Full article

Thermal control in rolling mills motors is gaining importance as more and more hard-to-deform steel grades are rolled. The capabilities of diagnostics monitoring also expand as digital IIoT-based technologies are adopted. Electrical drives in modern rolling mills are based on synchronous motors with frequency regulation. Such motors are expensive, while their reliability impacts the metallurgical plant output. Hence, developing the on-line temperature monitoring systems for such motors is extremely urgent. This paper presents a solution applying to synchronous motors of the upper and lower rolls in the horizontal roll stand of plate mill 5000. The installed capacity of each motor is 12 MW. According to the digitalization tendency, on-line monitoring systems should be based on digital shadows (coordinate observers) that are similar to digital twins, widely introduced at metallurgical plants. Modern reliability requirements set the continuous temperature monitoring for stator and rotor windings and iron core. This article is the first to describe a method for calculating thermal loads based on the data sets created during rolling. The authors have developed a thermal state observer based on four-mass model of motor heating built using the Simscape Thermal Models library domains that is part of the MATLAB Simulink. Virtual adjustment of the observer and of the thermal model was performed using hardware-in-the-loop (HIL) simulation. The authors have validated the results by comparing the observer’s values with the actual values measured at control points. The discrete masses heating was studied during the rolling cycle. The stator and rotor winding temperature was analysed at different periods. The authors have concluded that the motors of the upper and lower rolls are in a satisfactory condition. The results of the study conducted generally develop the idea of using object-oriented digital shadows for the industrial electrical equipment. The authors have introduced technologies that improve the reliability of the rolling mills electrical drives which accounts for the innovative development in metallurgy. The authors have also provided recommendations on expanded industrial applications of the research results. Full article

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments. Full article

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment. Full article

The electric drive is strategically placed in the power industry. It is exposed to wear and tear, defects, and constructional damage, as is any technical device. An information–analytical system is presented in this work. It performs the tasks of monitoring, diagnostics, general assessment of technical condition, and continuous assessment of energy and mechanical efficiency of the electric drive based on the analysis of immediate values of currents and voltages. The system modules are finished products with practical application, which are supported by experimental validation. This article contains a detailed description of the methods implemented in the system development, as well as a description of the laboratory bench and equipment used in our experiments. The information–analytical system is shown and proved on the basis of a fault reconstruction example with electric drive misalignment. According to the obtained results, recommendations for preventive control and proposals for development in this direction are formulated. Full article

As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance. Full article

Photovoltaic (PV) systems are pivotal to renewable energy generation, yet their efficiency and reliability are often compromised by operational faults and environmental variability. Accurate modeling of PV module behavior under diverse conditions is critical for forecasting energy yields and diagnosing performance anomalies. However, detecting subtle defects such as microcracks in operational modules remains challenging due to minimal observable differences between healthy and compromised systems. This study introduces an adaptive strategy to model the electrical characteristics of PV modules under dynamic operating conditions. Central to this approach is the extraction of key parameters, notably series resistance and shunt resistance, which act as diagnostic indicators of module health. Leveraging the seven-parameter double-diode model (DDM), we analyzed both functional and faulty modules to isolate deviations in their electrical signatures. By correlating these parameter shifts with specific defects, the proposed framework enables rapid, non-invasive health assessments of PV installations. This research aimed to establish a robust system for proactive maintenance, optimizing energy output and prolonging module lifespan. Such advancements hold significant potential for enhancing the sustainability and cost-effectiveness of solar energy systems, driving progress toward global renewable energy goals. Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

Polyvinylidene fluoride (PVDF) polymer films, renowned for their exceptional piezoelectric, pyroelectric, and ferroelectric properties, offer a versatile platform for the development of cutting-edge micro-scale functional devices, enabling innovative applications ranging from energy harvesting and sensing to medical diagnostics and actuation. This paper presents an in-depth review of the material properties, fabrication methodologies, and characterization of PVDF films. Initially, a comprehensive description of the physical, mechanical, chemical, thermal, electrical, and electromechanical properties is provided. The unique combination of piezoelectric, pyroelectric, and ferroelectric properties, coupled with its excellent chemical resistance and mechanical strength, makes PVDF a highly valuable material for a wide range of applications. Subsequently, the fabrication techniques, phase transitions and their achievement methods, and copolymerization and composites employed to improve and optimize the PVDF properties were elaborated. Enhancing the phase transition in PVDF films, especially promoting the high-performance β-phase, can be achieved through various processing techniques, leading to significantly enhanced piezoelectric and pyroelectric properties, which are essential for diverse applications. This concludes the discussion of PVDF material characterization and its associated techniques for thermal, crystal structure, mechanical, electrical, ferroelectric, piezoelectric, electromechanical, and pyroelectric properties, which provide crucial insights into the material properties of PVDF films, directly impacting their performance in applications. By understanding these aspects, researchers and engineers can gain valuable insights into optimizing PVDF-based devices for various applications, including energy-harvesting, sensing, and biomedical devices, thereby driving advancements in these fields. Full article

This paper focuses on the latest advancements in diagnosing faults in Permanent Magnet Synchronous Machines (PMSMs), with particular attention paid to demagnetization, inter-turn short circuits (ITSCs), and eccentricity faults. As PMSMs play an important role in electric vehicles, renewable energy systems and aerospace applications, ensuring their reliability is more important than ever. This work examines widely applied methods like Motor Current Signature Analysis (MCSA) and flux monitoring, alongside more recent approaches such as time-frequency analysis, observer-based techniques and machine learning strategies. These methods are discussed in terms of strengths/weaknesses, challenges and suitability for different operating conditions. The review also highlights the importance of experimental validations to connect theoretical research with real-world applications. By exploring potential synergies between these diagnostic methods, the paper outlines ways to improve fault detection accuracy and machine reliability. It concludes by identifying future research directions, such as developing real-time diagnostics, enhancing predictive maintenance and refining sensor and computational technologies, aiming to make PMSMs more robust and fault-tolerant in demanding environments. In addition, the discussion highlights how partial demagnetization or ITSC faults may propagate if not diagnosed promptly, necessitating scalable and efficient multi-physics approaches. Finally, emphasis is placed on bridging theoretical advancements with industrial-scale implementations to ensure seamless integration into existing machine drive systems. Full article

The aim of our study is to review the new vehicle diagnostic requirements that support economical and environmentally friendly operation. Vehicle technology is undergoing continuous and significant changes. At the same time, it is not enough to develop energy-efficient and environmentally friendly technologies; they must be operated in proper technical conditions and with proper driving techniques. Accordingly, new, innovative procedures are constantly needed for the economical and environmentally friendly operation of vehicles, and it is important to emphasize that vehicle diagnostics must also follow these changes! The practical applications of our publication and our research focus on several areas. This research is particularly important in the case of public transport vehicles and transport fleets. An important practical aspect is that large transport companies also achieve significant cost savings and, at the same time, contribute to environmentally friendly transport. The publication represents a new direction in vehicle diagnostics and research and development; this is the ECO-Diagnostics discussed in the material. ECO-Diagnostics is a procedure that takes into account both ecological and economic factors during vehicle diagnostic tests. Vehicle diagnostics, as an independent, professional, and scientific field, began to develop in the 1970s. This field of research experiences a paradigm shift, on average, every 20 years. Today, an epochal shift is taking place, with the development and spread of alternative propulsion systems (e.g., electric, hydrogen, or gas) and autonomous vehicles being the main areas of focus. The changes in vehicle technology must be followed by vehicle diagnostics too. Some of the already-known diagnostic methods (e.g., for internal combustion engines) can be included in this category, but new methods are also needed to enable the economical and environmentally friendly operation of vehicles. These facts make it important and urgent to define and research this area. Research in this area is particularly important for public transport vehicles and transport fleets. It is not enough to develop energy-efficient and environmentally friendly technologies: they must be operated in the right technical condition and with the right driving techniques for the intended purpose. This will help large transport companies to achieve significant cost savings and contribute to the environmentally friendly transport of passengers and goods. A major new area in vehicle diagnostics needs to be introduced and expanded. ECO-Diagnostics is a new category that has not been used before, and it also marks a new area of research and development. The article presents the basics of categorization and supports them with its own research results and application examples. As an introduction, a systematic overview of vehicle diagnostics as a whole is also provided. This is important (and novel) as no such systematic overview is available in the technical and scientific literature. The new category should also be included in this scheme. In parallel with the development of vehicles and diagnostic procedures, the methods and their context covered by the umbrella term ECO-Diagnostics (in ecological and economic terms) should, of course, be constantly expanded. Artificial intelligence can play an important role in this process. In the future, there will be a strong demand for the development of procedures in the field of ECO-Diagnostics. For both economic and environmental reasons, it is urgent and important to research and develop procedures in this category. This fact will also influence the work of researchers in the future. Full article

This study investigates highway pavement subsidence along Road 431, Israel, using an integrated geophysical framework that combines Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). These methods address the limitations of standalone techniques by correlating surface subsidence patterns with subsurface anomalies. InSAR identified surface subsidence rates of up to −2.7 cm/year, pinpointing subsidence hotspots, while GPR detected disintegrated fill layers and air voids, and ERT revealed resistivity anomalies at depths of 50–100 m linked to karstic cavities and water infiltration. Validation through borehole drilling confirmed structural heterogeneity, specifically identifying karstic voids in limestone layers and weathered chalk layers that align with the geophysical findings. The findings highlight the complex interplay of geological and hydrological processes driving ground instability, exacerbated by groundwater fluctuations. This study demonstrates the novelty of combining surface and subsurface monitoring methods, offering a detailed diagnostic framework for understanding and mitigating geotechnical risks in transportation infrastructure. Full article

Microfluidic technology has emerged as a multidisciplinary field, integrating fluid dynamics, electronics, materials science, etc., enabling precise manipulation of small volumes of fluids and particles for various bio-applications. Among the forms of energy integrated into microfluidic systems, electric fields are particularly advantageous for achieving precise control at the microscale. This review focuses on the design and fabrication of microelectrodes that drive electrokinetic phenomena, dielectrophoresis (DEP) and electroosmotic flow (EOF), key techniques for particle and fluid manipulation in microfluidic devices. DEP relies on non-uniform electric fields to manipulate particles based on their dielectric properties, while EOF utilizes uniform electric fields to generate consistent fluid flow across microchannels. Advances in microelectrode fabrication, including photolithography, soft lithography, and emerging non-cleanroom techniques, are discussed. Additionally, the review explores innovative approaches such as rapid prototyping, contactless electrodes, and three-dimensional structures, along with material considerations like conductive polymers and carbon composites. The review discusses the role of microelectrodes in enhancing device functionality, scalability, and reliability. The paper also identifies challenges, including the need for improved fabrication reproducibility and multifunctional integration. Finally, potential future research directions are proposed to further optimize DEP- and EOF-based microsystems for advanced biomedical and diagnostic applications. Full article

The movement of a wheeled vehicle is a non-regular dynamic process characterized by a large number of states that depend on the movement conditions. This movement involves a large number of situations where elastic tires skid and slip against the base surface. This reduces the efficiency of movement as useful mechanical energy of the electromechanical drive is spent to overcome the increased skidding and slipping. Complete sliding results in the loss of control over the vehicle, which is unsafe. Processes that take place immediately before such phenomena are of special interest as their parameters can be useful in diagnostics and control. Additionally, such situations involve adverse oscillatory processes that cause additional dynamic mechanical and electrical loading in the electromechanical drive that can result in its failure. The authors provide the results of laboratory road research into the emergence of self-oscillatory phenomena during the rolling of a wheel with increased skidding on the base surface and a low traction factor. This paper reviews the methods of designing an anti-slip control system for wheels with an oscillation damping function and studies the applicability and efficiency of the suggested method using mathematical simulation of the virtual vehicle operation in the Matlab Simulink software package. Using the self-oscillation suppression algorithm in the control system helps reduce the maximum amplitude values by 5 times and average amplitudes by 2.5 times while preventing the moment operator from changing. The maximum values of current oscillation amplitude during algorithm changes were reduced by 2.5 times, while the current change rate was reduced by 3 times. The reduction in the current-change amplitude and rate proves the efficiency of the self-oscillation suppression algorithm. The high change rate of the current consumed by the drive’s inverters may have a negative impact on the remaining operating life of the rechargeable electric power storage system. This impact increases with the proximity of its location due to the low inductance of the connecting lines and the operating parameters, and the useful life of the components of the autonomous voltage inverters. Full article

Electrical motors and drives are the unseen forces driving our modern world, powering everything from electric vehicles to industrial machinery. The efficiency, precision, and sustainability of these systems are very important. Unexpected motor failures can cause major disruptions, risk human lives, and cause costly downtime. This research aims to improve the efficiency and performance of three-phase synchronous machines using Artificial Intelligence (AI) strategies. The research uses real-time data and optimization techniques to explore advanced diagnostic techniques, fault diagnosis, fault tolerance, and condition monitoring schemes to enhance safety, reliability, and performance in electric synchronous operations. Experimental results demonstrated that AI-driven methods achieve higher fault detection accuracy than traditional techniques. The findings highlight the potential of AI in improving the reliability of industrial synchronous motors. Full article