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This paper provides an in-depth assessment of Active Magnetic Bearings (AMBs), which are electromagnetic bearings powered by power electronic components for high-speed equipment. The paper highlights the advantages of AMBs as frictionless bearings that utilize magnetic force to support the rotor, resulting in reduced wear, lower losses, and extended lifespan compared to classical ball bearings. AMBs are particularly well-suited for high-speed applications and demonstrate compatibility with both clean environments, such as renewable energy sectors, and challenging conditions like extreme pressures, temperatures, and harsh environments. However, controlling AMBs presents difficulties due to their nonlinear and inherently unstable nature. The paper focuses on examining the benefits and drawbacks of AMBs, including fault analyses. It further emphasizes recent advancements in power electronics supervision, including quick-switching power components, power conversion devices, and advanced control techniques, which have significantly expanded the potential applications of AMBs across various industries. Additionally, the paper explores the electromagnetic setups, electrical converters, sensors, and optimization techniques employed in AMBs, providing valuable insights into their construction, fault-finding methods, control strategies, and ongoing and future research trends.

Abstract As electric vehicles gain popularity, there has been a lot of interest in supporting their continued development with the aim of enhancing their dependability, environmental advantages, and charging efficiency. The scheduling of navigation and charging for electric vehicles is among the most well-known research topics. For optimal navigation and charging scheduling, the coupled network state between the transportation and power networks must be met; moreover, the scheduling outcomes might significantly impact these networks. To address climate challenges, relying only on fossil fuel-based infrastructure for electric car charging is insufficient. Consequently, Multi-Energy Integrated EV charging stations have emerged as a workable solution that seamlessly integrates grid power, renewable energy sources—particularly solar energy—and EV charging needs. The enhanced grey wolf optimised (GWO) ANFIS controller for Maximum Power Point Tracking (MPPT), standby battery systems, solar power, neural network-integrated grids, and sophisticated control algorithms like PID controller are all proposed in this article as energy-efficient charging terminals for electric vehicles. Moreover, authors had considered four conditional case study and with the help of MATLAB/Simulink 2018a software, the design is thoroughly examined and assessed, providing a viable route for an efficient and sustainable EV charging infrastructure.

Abstract In this work, a new kind of charge scheduling algorithm is proposed by utilizing the War Strategy Optimization (WSO) algorithm. The strategies used in the war such as attack, defense, assigning soldiers to take positions are the inspiration to this algorithm. The proposed WSO algorithm is validated in a constructed geographic area which consists of Six starting/destination points, sixteen nodes, and twelve charging stations. In terms of waiting time and charging cost, the experimental results show that the WSO method much improves over current methods. The average waiting time and average charging cost of EVs are validated in MATLAB, with different considerations such as different number of EVs varied from 25 to 100, and different number of charging piles varied from 1 to 4. The WSO algorithm specifically lowered charging costs by up to 13.67% compared to the same and waiting time by up to 83.25% relative to the First Come First Serve algorithm. Comparatively to the Chaotic Harris Hawk Optimization and Harris Hawk Optimization algorithms, the WSO method demonstrated declines in waiting time by 11.17% and 39.09%, respectively, and declines in charging costs by 3.61% and 12.45%, respectively. Especially in situations with limited charging infrastructure, these findings show that the WSO algorithm may improve the efficiency and cost-effectiveness of EV charging management systems. For real-world EV charging management systems, the method's capacity to efficiently allocate EVs among charging stations, lower waiting times, and lower charging costs makes it a potential solution.

This comprehensive review covers the latest EV technologies, charging methods, and optimization strategies. Electric and hybrid vehicles are compared, explaining their operation and effects on energy, efficiency, and the environment. The review covers new EV charging technologies. Conductive charging (CC), the most popular method due to its simplicity and cost, is tested. Wireless power transfer (WPT) systems, which charge without cables, and their integration into urban infrastructure are also examined. Battery swap stations (BSS), which quickly recharges depleted batteries, are also reviewed for their viability and adoption issues. Electric vehicle standards like charging rate and system configuration are covered in this paper. These standards simplify electric mobility across regions and manufacturers by ensuring charging infrastructure and vehicle technology compatibility. The review evaluates algorithms and mathematical models that maximize efficiency, reduce costs, and improve charging resource accessibility. Urban planners and policymakers need these optimization strategies to improve EV infrastructure. Article ends with research ideas. It identifies knowledge gaps and suggests research to improve EV technologies and charging systems. To promote electric vehicle adoption and innovation, the recommendations address technical and socioeconomic barriers. Researchers, engineers, and decision-makers use this review to develop and implement electric vehicle technologies and infrastructure.

The complexity of improving controllers for Active Magnetic Bearing Systems (AMBs), which are essential parts of fast electronic transport networks such as electric cars, aviation technology, and defense systems is examined in this research work. It evaluates AMBs’ robustness against abrupt harmonic disruptions using wavelet transform methodologies. Initially, predictive-based interpolation equations are used to create system controller matrices that forecast controlling gains for various eccentric rotor behaviors. Steady functioning is ensured by the non-linear AMB system’s assumed linearity inside this range. Furthermore, system dynamics are assessed about probable external defects by creating a modeled harmonic signal and using wavelet transformations in continuous and discrete realms. The reliability of the assessment method is highlighted by the low 1.15% variance across the original and recovered force signals at high rotor eccentricity, which is supported by suitable scalability and wavelet selection based on Fisher’s criterion. The computational findings provide insights and enhance the understanding of regulating the fluctuating actions of AMBs through these thorough investigations, stimulating improved reliability and effectiveness in an array of applications.

The deployment of renewable energy sources has become more frequent in power system networks over the last few years. The prevalence of global warming and some catastrophic climate changes is rising, along with the demand for intricate transport systems, as a result of rapid growth in civilization and modernization in culture. To fight this environmental issue associated with vehicle transmission, almost every nation is promoting electric vehicles (EV). In this article, a novel method for developing a sliding mode maximum power point tracking (MPPT) controller for photovoltaic (PV) systems operating in rapidly varying atmospheric circumstances is put forward. Further, the standard Perturb and observe (Pb&O) algorithm’s variable step is driven by the best sliding mode controller (SLMC) gains, which are determined using the Genetic Algorithm (GAO). Additionally, a PI controller, a grid employing current controlling topology, and an effective charging station constructed with GAO-optimized Sliding Mode-based reconfigurable step size Pb&O as an MPPT controller are executed and tested in MATLAB/Simulink for optimal control of power in the EV charging station. The main contribution of this study is to enhance the created controller’s tracking performance to reach the maximum power point (MPP) with negligible oscillation, low overshoot, minimum ripple, and excellent speed in conditions of air turbulence that change quickly, as well as ensure continuity in supply to the EV. Furthermore, the developed system as a whole shows good efficacy compared with other existing systems reviewed in the literature. Finally, this proposed strategy ensures continuity of power supply to the charging station even in uncertain weather conditions, as grid integration also plays a vital role in the overall demand.

In recent days the high penetration of EVs have an impact on the load demand and it will be increased further. This results in stress on the grid and proper energy management in the charging stations. Recent development also enables smart communication between EV user and charging station. A bidirectional converter-based charging station works on V2G and G2V modes for charging the EV battery and supports the grid or isolated power station when it is needed. In this paper, a brief discussion on the previous development of bidirectional conversion is presented. A bidirectional converter is modeled and simulated in Simulink. In G2V mode, the time of energy-based tariff is used for the cost calculation, and for V2G mode, the system monitoring of the EV charging parameters is presented where the pricing varies from 11.09 Rs. To 16.27 Rs. For an hour of power consumption using a 2.54 kW charger on a probabilistic model. For V2G operation, the inverter voltage and grid voltage in synchronization is presented. An IoT-based system is initiated in Simulink using thing speak. This put the data in the cloud and further the data is monitored and fetched from the cloud using a web application.

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.

Due to the intermittent and hazardous nature of lightning impulse, researchers rely on artificial impulse generators to study it. The Tesla coil is a commonly used device to generate artificial lightning. This paper presents a parametric analysis of the primary side coil of the Tesla transformer. The analysis employs seven key electrical properties: skin depth, current density, voltage distribution, Ohmic loss, electric field strength, charge density, and energy content. These parameters are used to evaluate the response of various coil types to high voltage impulses. The Ansys high-frequency electromagnetic solver is used to perform a comparative analysis of essential parameters. The results can aid lightning researchers and Tesla coil designers in determining the optimal designs for Tesla primary coils in the future. The analyzed data is validated using Tesla coil hardware, which is used as an artificial lightning producer to test the feasibility of a lightning energy harvester.

An active magnetic bearing (AMB) is an evolving research topic in the bearing industry. Compared to the conventional approach, AMB is more advantageous and has different industrial applications. The design of this bearing needs to be improvised to get more optimized performance. This leads us to focus on the better-designed structure of the actuator, which plays an essential role in AMB performance. Thus in this manuscript, we have designed and compared the I-type and U-type actuators’ performances in terms of their resistance, inductance profile, and force profile to show the characteristics of these actuators. These designed actuators are simulated in Ansys Maxwell software, and compared with the simulation results. Further, these results are verified using hardware results, and the tested actuators are used to design two different AMB systems. One current controller is designed and simulated to achieve the stable levitated position of the rotor. This rotor is rotated by inserting an AC magnet in the system and finally, a rotational system is attained, and its speed is observed by the tachometer for both AMB systems. Both the proposed AMB systems are simple and compact in structure as these are having single coil and single power amplifier. These axial AMB systems are used in many high-speed applications as they are having features like less installation space, less cost and more room for heat dissipation.

This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certain disadvantages to using petrol or diesel, such as poor fuel economy and pollution-causing exhaust gas emissions. Furthermore, there are some limitations with existing available work, and the merger of these different optimisation techniques will be advantageous for achieving optimal performance. To address them, the purpose of this research is to create an efficient energy management approach by combining SVM, KNN, and the Naive Bayes technique. Additionally, by combining these classifier techniques better performing EMS is developed. Using the proposed features, the optimisation approach’s performance accuracy is increased. Furthermore, these individual classifiers comprising of SVM, KNN & Naïve Bayes is giving accuracy percentage of 96%, 92% & 94% respectively. Finally, after combining these three classifiers we have achieved an accuracy percentage of 98%.

This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and Whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications.

Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar power is entirely unpredictable since solar power generation is dependent on the intensity of solar irradiation and solar panel temperature. Further, these parameters are weather dependent and thus intermittent in nature. To conquer intermittency, power converters play an important role in solar power generation. Generally, photovoltaic systems will eventually suffer from a decrease in energy conversion efficiency along with improper stability and intermittent properties. As a result, the maximum power point tracking (MPPT) algorithm must be incorporated to cultivate maximum power from solar power. To make solar power generation reliable, a proper control technique must be added to the DC–DC power converter topologies. Furthermore, this study reviewed the progress of the maximum power point tracking algorithm and included an in-depth discussion on modern and both unidirectional and bidirectional DC–DC power converter topologies for harvesting electric power. Lastly, for the reliability and continuity of the power demand and to allow for distributed generation, this article also established the possibility of integrating solar PV systems into nanogrids and picogrids in a sustainable environment. The outcome of this comprehensive survey would be of strong interest to the researchers, technologists, and the industry in the relevant field to carry out future research.

Abstract This paper presents a low‐voltage ride‐through technique for large‐scale grid tied photovoltaic converters using instantaneous power theory. The control strategy, based on instantaneous power theory, can directly calculate the active and reactive component of currents using measured grid voltage and currents and generate inverter switching pulses based on the formulated reference current values and thus helping to improve the dynamic response when voltage sag takes place. The dynamic response of the proposed model has been compared with both proportional‐integral and fuzzy current controllers to judge their suitability. The proposed strategy can provide both active and reactive power support dynamically during grid side fault. The proposed active reactive current control based technique shows better dynamic response compared to existing techniques. The proposed method is tested through appropriate simulation on a practical system to show the effectiveness of the proposed control method.

This proposed work demonstrates the illustration of dynamic performance intensification of a Permanent Magnet Synchronous Motor (PMSM) employed by a PWM controlled inverter which synchronizes with the rotor movement intimation. PMSM are widely hired in electric vehicles since it possesses better dynamic response, improved torque-speed property, and reduced noise, energy-efficient and power factor in comparison with traditional motors. In the present work, it is observed that by few modifications of the position control strategy as good as sinusoidal stator currents response generates less torque ripples. The mathematical model for PMSM is derived from park’s transformation. Further, a maiden attempt is taken to introduce the performance indicator ’sensor angle’ to estimate the rotor position in this strategy. From the established model, the various dynamic behavior of the drive system is determined analytically without and under various load disturbances. Additionally, a particle swarm optimization (PSO) technique is adopted to optimize the performance of the proposed dynamic strategy. An efficient speed control strategy by the variation of DC bus voltage is achieved which is equivalent to the armature voltage control of the conventional dc machine. Further, efficient and simple control circuitry of the voltage source inverter (VSI) is obtained in this strategy. To verify the efficacy of the proposed algorithm, necessary tests are carried out in a real-time setup. Therefore, an improved control strategy obtained from the simulation and an experimental approach meets the dynamic behavior employed in light weight electric vehicles.

In the rotating machinery sector, active magnetic bearing (AMB) has drawn great attention due to its benefits over the conventional bearing system. The high-speed technology is enhanced by AMBs, which also reduce maintenance costs and eliminate friction loss. This paper presents a unique, simpler, efficient design and hardware implementation for high-speed applications using two-coil I-type active magnetic bearings. To maintain the 10 mm air gap between the actuator and the rotor, two categories of controllers have been designed for the proposed system to control the position and another for detecting the coil current through the power amplifier. The AMB system is incorporated into a 3D finite element model for determining magnetic properties. The magnetic analysis is then carried out under various situations, and the attractive force characteristics have been evaluated for this suggested system to check the performance of the multicoil AMB system along with the stability analysis. The system is designed and simulated in MATLAB Simulink and implemented in hardware to validate the different outputs viz. position response and current response. Finally, an AC magnet is designed to rotate the rotor after the levitation, and a higher speed of 19,643 rpm is achieved in comparison to conventional bearings, which can be utilized in different industrial applications.

Filled skutterudite compounds have gained attention recently as an innovative platforms for studying intriguing low-temperature superconducting properties. Regarding the symmetry of the superconducting gap, contradicting findings from several experiments have been made for LaRu4As12 and its isoelectronic counterpart, LaOs4As12. In this vein, we report comprehensive bulk and microscopic results on LaOs4As12 utilizing specific heat analysis and muon-spin rotation/relaxation (μSR) measurements. Bulk superconductivity with TC = 3.2 K was confirmed by heat capacity. The superconducting ground state of the filled-skutterudite LaOs4As12 compound is found to have two key characteristics: superfluid density exhibits saturation type behavior at low temperature, which points to a fully gapped superconductivity with gap value of 2Δ/kBTC = 3.26; additionally, the superconducting state does not show any sign of spontaneous magnetic field, supporting the preservation of time-reversal symmetry. These results open the door for the development of La-based skutterudites as special probes for examining the interplay of single- and multiband superconductivity in classical electron–phonon systems.

We report the finding of a novel pairing state in a newly discovered superconductor Na2Cr3As3. This material has a non-centrosymmetric quasi-one-dimensional crystal structure and is superconducting at TC∼ 8.0 K. We find that the magnetic penetration depth data suggests the presence of a nodal line pz-wave pairing state with zero magnetic moment using transverse-field muon-spin rotation (TF-μSR) measurements. The nodal gap observed in Na2Cr3As3 compound is consistent with that observed in isostructural (K,Cs)2Cr3As3 compounds using TF-μSR measurements. The observed pairing state is consistent with a three-band model spin-fluctuation calculation, which reveals the Sz=0 spin-triplet pairing state with the sinkz pairing symmetry. The long-sought search for chiral superconductivity with topological applications could be aided by such a novel triplet Sz=0p-wave pairing state.

Pongamia pinnata (L.) seed oil is effective for its insecticidal and larvicidal activities. However, its low aqueous solubility, high photosensitivity, and high volatility restrict its application for the control of agricultural pests. Encapsulation can be an effective technique to overcome such hindrances. Therefore, P. pinnata oil (PO) was extracted from its seeds and analyzed for karanjin content (3.18%) by GC-MS/MS as the marker compound. Micro-encapsulation (MC) of PO was prepared by interfacial polymerization between isocyanates and polyamine and tested for insecticidal and larvicidal activities. Bioassay of the developed formulations was tested in-vitro against 2nd instar larvae of Bombyx mori (Bivoltine hybrid) and in-vivo insecticidal bio-efficacy was tested against aubergine aphid (Aphis gossypii G.) and whitefly (Bemisia tabaci G.). Various properties of micro-capsules viz., stability, size, oil content and release kinetics were examined. Average diameter of capsules (1 μm) with Zeta potential (-16 mV) was indicated by the Dynamic Light Scattering (DLS) instrument. Existence of PO in the microcapsules was confirmed by an optical microscopic study. Spectroscopic analysis revealed 87.4% of PO was encapsulated in polyurea shell. The shelf-life (T10), half-life (T50), and expiry-life (T90) of polyurea coated capsules were 11.4, 75.3 and 250.0 h, respectively. Polyurea coated PO capsule formulation showed evidence of in-vitro toxicity against 2nd instar larvae of B. mori (LC50 = 1.1%; LC90 = 5.9%). The PO formulation also exhibited 67.0–71.8% and 62.4–74.8% control of aphid and whitefly population in aubergine at 4.0% dose following 7–14 days after application. The study unveiled its significance in developing controlled release herbal formulations of P. pinnata as an alternative to harmful conventional synthetic insecticides for crop protection.