Search Results (622)

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins. Full article

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

In wireless power transfer (WPT) systems, magnetically coupled wireless power transfer has become a major research focus due to its advantages such as long transmission distance, strong tolerance to misalignment, and high power transfer capability. It is also widely applied in vehicle wireless power transfer systems. From the perspective of practical engineering applications, this paper investigates the problem of system parameter variations caused by changes in inductance and load, in combination with magnetically coupled structures. During actual system operation, misalignment of the coupling mechanism leads to variations in mutual inductance, while the load resistance may also fluctuate. These parameter changes result in alterations to the overall output characteristics of the system, which are detrimental to stable system operation. Moreover, adopting a dual-side communication control strategy is susceptible to interference from the system’s power circuitry. To address these issues, this paper proposes a novel state variable modeling method and designs a state observer based on the extended Kalman filter (EKF) algorithm to estimate the secondary-side parameters, thereby enabling observation of the voltage across the load at the receiver side. The state observer is configured with two operating modes to monitor variations in mutual inductance and load resistance. The observer outputs are compared with the actual load-side voltage, and the effectiveness of the proposed state observer is verified. Full article

We describe AI agents as stochastic dynamical systems and frame the problem of learning to reason as in transductive inference: Rather than approximating the distribution of past data as in classical induction, the objective is to capture its algorithmic structure so as to reduce the time needed to solve new tasks. In this view, information from past experience serves not only to reduce a model’s uncertainty, as in Shannon’s classical theory, but to reduce the computational effort required to find solutions to unforeseen tasks. Working in the verifiable setting, where a checker or reward function is available, we establish three main results. First, we show that the optimal speed-up for a new task is tightly related to the algorithmic information it shares with the training data, yielding a theoretical justification for the power-law scaling empirically observed in reasoning models. Second, while the compression view of learning, rooted in Occam’s Razor, favors simplicity, we show that transductive inference yields its greatest benefits precisely when the data-generating mechanism is most complex. Third, we identify a possible failure mode of naïve scaling: in the limit of unbounded model size and computing, models with access to a reward signal can behave as savants, brute-forcing solutions without acquiring transferable reasoning strategies. Accordingly, we argue that a critical quantity to optimize when scaling reasoning models is time, the role of which in learning has remained largely unexplored. Full article

This work investigates the geometric design and optimisation of a dynamic inductive power transfer coupler for two-wheeled electric vehicles under misalignment and magnetic-field exposure constraints. A computational three-dimensional finite-element model of a shielded rectangular coupler is developed to characterise coupling coefficients and magnetic flux density levels on control planes along the longitudinal travel range and under lateral and angular misalignments. Two simulation datasets are generated: one varying only geometric parameters at a nominal position for surrogate construction and global sensitivity analysis, and a second jointly sampling geometry, the travel range and misalignments for optimisation. Sparse Polynomial Chaos Expansions and Canonical Low-Rank Approximation surrogates are built to quantify Sobol’ indices, revealing that a small subset of primary-side geometric variables dominates both coupling efficiency and magnetic field levels. Random forest regressors are then trained on the extended dataset and embedded in the Non-dominated Sorting Genetic Algorithm II to solve a multi-objective optimisation problem that maximises worst-case coupling, improves robustness to misalignment, and enforces magnetic-field leakage limits. Optimal designs were obtained, and a subset was selected for re-evaluation using the finite-element method. The results confirm that the proposed surrogate-assisted framework yields coupler geometries with enhanced coupling and reduced magnetic field leakage while respecting the mechanical constraints for the electric motorcycle system. Full article

Underwater wireless power transfer (UWPT) operates under special conditions, where the conductivity of seawater introduces eddy current losses, thereby reducing system efficiency. Meanwhile, the design parameters of magnetic couplers significantly influence their transmission characteristics. This paper proposes a fast and accurate neural network prediction model for mutual inductance and losses of magnetic couplers based on mirror-method prior knowledge within a prior knowledge input (PKI) framework. The proposed model integrates a low-fidelity analytical model with data-driven learning to achieve high prediction accuracy while maintaining computational efficiency. Based on the developed model, the transmission characteristics of unipolar rectangular and bipolar DD magnetic couplers are systematically investigated. The results indicate that the rectangular couplers exhibit higher overall efficiency than the DD couplers, with a more monotonic variation in efficiency under design constraints. Owing to its structural characteristics, the DD couplers present an optimal current-carrying area ratio, which is approximately 0.85 within the parameter range. Experimental validation is conducted at a 1 kW power with outer dimensions of 200 mm × 250 mm. The optimal transfer efficiencies of the rectangular and DD couplers reach 97.33% and 96.19%, respectively. The experimental results show good agreement with both simulations and model predictions, demonstrating the reliability of the proposed method for UWPT magnetic coupler analysis. Full article

The scale mismatch between wind turbine hub heights and conventional meteorological masts introduces uncertainties in wind resource assessment. Vertical wind speed extrapolation serves as a critical technique to bridge this spatial gap. Current extrapolation paradigms struggle with two fundamental limitations. Physical models fail to capture non-stationary atmospheric stability, whereas purely data-driven methods depend heavily on unavailable hub-height ground truth. To bridge this gap, this paper proposes a Physically Guided Neural Network framework. By integrating physical boundary-layer principles with an adaptive residual correction mechanism, the model introduces an inductive bias that maps near-surface observations to dynamic wind shear evolutions. The network employs a “Near-Surface Learning and Hub-Height” Transfer strategy. This approach optimizes the model exclusively on multi-level observations from 10 to 70 m to eliminate the dependency on high-altitude target labels. Validation on a 100 MW wind farm dataset, utilizing a 70 m proxy variable evaluation, demonstrates that this framework reduces the wind speed extrapolation root mean square error by 56.48% compared to traditional power law models. Furthermore, downstream theoretical power estimation errors are reduced by 10.72%, effectively mitigating power curve lag phenomena. This hybrid approach establishes a robust and low-cost paradigm for refined wind energy assessment in engineering scenarios lacking tall meteorological monitoring. Full article

Dynamic wireless power transfer (DWPT) has attracted significant interest due to its ability to transfer power to moving electric vehicles. Most existing DWPT research focuses on vehicles traveling along fixed paths. However, modern warehouses increasingly employ indoor electric vehicles (IEVs), such as autonomous mobile robots, that move along non-fixed paths. Although several solutions have been proposed for large-area DWPT systems applicable to IEVs with non-fixed trajectories, these approaches are predominantly based on inductive DWPT. Such systems require a large number of densely arranged transmitting coils and expensive ferrite pads, resulting in high system cost. To the authors’ best knowledge, no published work has addressed large-area capacitive DWPT systems for IEVs moving along non-fixed paths. This paper aims to fill this research gap. The main novelty of this work is the first proposal of a capacitive DWPT system for lightweight IEVs operating along non-fixed paths. The feasibility of the proposed solution is validated through simulation studies conducted in PSIM. The simulation results demonstrate that the proposed DWPT system, employing an advanced transmitting-metal-plate activation strategy, can maintain an almost constant mutual capacitance, thereby ensuring a smooth output voltage at the receiving side for a moving IEV. Full article

This paper proposes a novel magnetic coupler, a segmented bipolar pad (SBP) that outperforms the conventional bipolar pad (BP) with symmetrical geometrical dimensions. The performance parameters: mutual inductance (M_TR), coupling coefficient (k), output power (P_O), and DC-DC efficiency (η). The performance evaluation of the proposed pad is compared with the conventional pad under cases: (1) lateral misalignment (ΔY), and (2) interoperability with non-polarized pad (NPP) and polarized pad (PP). A 4.7 kW inductive power transfer (IPT) system is designed with an inductor–capacitor–capacitor-series (LCC-S) compensation network. For case 1, the M_TR of the SBP at ΔY = ±90 mm is the same as the M_TR of BP at ΔY = 0 mm, ensuring better misalignment tolerance capability of SBP. The maximum η of SBP is 93.64%, which is 4.96% greater than the highest η of BP. For case 2, the M_TR of the SBP with NPP is 22–24% and with PP is 20–25% higher than the BP interoperable performance. The obtained η shows maximum improvement of 2.46% for SBP with NPP, and 3.7% for SBP with PP when compared to the interoperable results of BP. SBP gives enhanced performance for both cases compared to the conventional pad at no additional pad design cost. The proposed work is validated through an experimental setup. Full article

To meet the power supply requirements of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this study proposes a wireless power transfer (WPT) system for AUVs, incorporating novel nanocrystalline flake ribbon cores. The proposed system utilizes the excellent magnetic field concentrating and shielding characteristics of nanocrystalline materials. The flake ribbons are fabricated by compressing dielectric materials mixed with nanocrystalline ribbons, which effectively reduces eddy-current loss. The layout arrangement of nanocrystalline materials is investigated, and a magnetic coupler employing ribbon-type nanocrystalline materials is adopted based on simulation analysis and comparison. The influence of nanocrystalline materials on the mutual inductance distribution between the transmitting and receiving coils is explored. Considering the potential positional misalignments between the transmitting and receiving coils in practical marine environments, the misalignment tolerance of the system is comprehensively analyzed and experimentally verified. An experimental prototype is established, and the results demonstrate that the proposed magnetic coupler design significantly improves the performance of the WPT system. Compared with the conventional WPT system without nanocrystalline flake ribbon cores, the proposed design effectively increases the power transfer efficiency by 2.99% and greatly stabilizes the output power by 36%. This study validates the effectiveness and practicability of using nanocrystalline flake ribbon cores in WPT systems for AUV applications. Full article

Wireless power transfer (WPT) systems are generally sensitive to variations in separation distance and coil alignment, which result in reduced power transfer efficiency and delivered power. Various approaches based on control system and active matching circuits have resulted in more complex implementations. This work, by contrast, presents a full DC–DC inductively coupled WPT system employing coupled nonlinear resonators to automatically adapt the system for variations in transfer coil separation and orientation, maintaining high transfer efficiency at a constant output power level. With entirely passive circuit components, the nonlinear resonators suppress the frequency-splitting phenomenon typical of WPT systems that leads to efficiency degradation. A class-EF power amplifier used in the transmitter experiences an approximately constant impedance, providing a constant output power while maintaining high efficiency. On the receive side, a class-E rectifier operates at a constant input power, achieving high overall efficiency without active control. An experimental demonstration delivers 5 W with a 6.12% power variation over a 1 to 9 cm distance variation and achieves a peak DC–DC efficiency of 71.6%. The response of the system to changes in coil separation is compared with a conventional linear WPT circuit, showing a constant-power and high-efficiency operation. Full article

Power seats in vehicles require multiple cables, which can lead to potential short- or open-circuit issues. To address this limitation, this paper proposes a rail-embedded wireless power transfer coil. By embedding the coil within the rail structure, leakage magnetic fields are reduced by up to 90%, which helps mitigate electromagnetic interference. Additionally, various coil structures are compared and analyzed to enhance power transfer efficiency. Moreover, considering practical operating conditions where the power seat position varies, the compensation capacitance is determined based on the minimum Tx coil inductance to ensure zero-voltage-switching conditions. The theoretical analysis of power transfer efficiency is validated through simulation and experimental results. The results demonstrate that the proposed approach is well suited for power seat applications, offering a compact structure while maintaining high power transfer efficiency. In this research, a power of 70 W is successfully transferred, achieving a maximum coil-to-coil power transfer efficiency of 92% and an overall system efficiency of 80%. Full article

Dynamic inductive power transfer (DIPT) can enable dynamic wireless charging for urban micromobility, but deployment is constrained by electromagnetic field (EMF) exposure compliance and by lateral and angular misalignment typical of two-wheeled vehicles. This review consolidates the state of the art and links these constraints to smart grid control and charging optimisation. It frames dynamic charging lanes as corridor infrastructure that behaves as a distributed electrical load whose demand depends on traffic and availability, with segmentation control as a key lever for controllability. It then synthesises practical system architectures that combine power electronics, segmented transmitters, sensing, communication, and supervisory control, because these interfaces determine which degrees of freedom are available to shape demand in space and time. The review also summarises coupler, shielding, and compensation choices that jointly determine efficiency, misalignment robustness, and EMF leakage. Finally, it surveys scheduling methods that incorporate network limits, output from distributed energy resources, and uncertainty through rolling horizon, robust, and risk-constrained formulations. The synthesis supports deployment aligned with renewable integration and sustainable urban mobility, and it highlights open needs in forecasting robustness, scalable optimisation, and secure interoperability. Full article

Dynamic wireless power transfer (DWPT) systems for in-motion electric vehicle (EV) charging often suffer from unstable power delivery due to spatial variations in magnetic coupling caused by vehicle misalignment. This study presents a stabilization-oriented DWPT design methodology that prioritizes minimizing spatial variations of mutual inductance rather than maximizing peak coupling under perfect alignment. A ferrite-backed double-D coil configuration is analyzed and refined using three-dimensional finite-element electromagnetic modeling integrated with circuit-level co-simulation to evaluate coupling behavior, magnetic field homogeneity, and power transfer efficiency under realistic dynamic misalignment conditions. The proposed design achieves a coupling coefficient of 0.50–0.55 under aligned conditions and exhibits smooth, predictable degradation for lateral offsets up to 40–50 mm. Quantitative analysis demonstrates a low spatial coupling gradient of approximately 0.001 mm⁻¹, indicating that abrupt coupling transitions are effectively suppressed during vehicle motion. The system attains a maximum power transfer efficiency of 84.37% at an 80 mm air gap, while maintaining stable performance under both lateral and vertical displacement. Comparative evaluation shows improved misalignment tolerance and coupling stability relative to conventional double-D configurations. The results demonstrate that electromagnetic field shaping focused on coupling smoothness is an effective and practical strategy for reliable dynamic wireless charging of electric vehicles. Full article