This paper presents a three-dimensional wireless power transfer (3D-WPT) system that uses three transmitting coils oriented in orthogonal directions and an adaptive model predictive control (MPC) strategy that incorporates maximum power point tracking (MPPT). The framework combines MPC's predictive optimization and MPPT's efficiency-seeking behavior, which simultaneously optimizes power tracking accuracy, system efficiency, and current distribution, while maintaining zero-voltage-switching (ZVS) operation. The dynamic weight-adjustment mechanism alters the weights of competing objectives automatically and based on the current operational conditions. The system model indicates that power distribution in 3D space fits a Lemniscate of Bernoulli surface revolved about its longitudinal axis, so the rotational Lemniscate of Bernoulli surface is important to the control design. The experimental results presented in this paper show up to 20% increased efficiency at 40 cm transmission distance, and a peak efficiency of 87% at 60W and at 40<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 40\times$</tex-math></inline-formula>40cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> charging volume. The framework provides stable power delivery while the receiver is in motion, making it appealing for applications that require spatial freedom, including mobile robots and bio-inspired aerial vehicles.

Research on constant current and constant voltage charging in frequency switching wireless power transfer systems

According to the duality of inductive wireless power transfer (IPT) and capacitive wireless power transfer (CPT) in coupler and tuning method, an integrated coil is designed in this paper to constructs a compact hybrid wireless power transfer (HPT). The magnetic field coupling and the electric field coupling constructed by coils and plates share one energy transmission channel, which enhances the transmission power and energy density. The inductance of coils and capacitances of plates compensate for each other. The resonant frequency of integrated coils is adjusted to kHz. Additional tuning inductances and capacitances are eliminated to build a lighter self-resonance coupler. The stacked structure design of coils and plates enables compact integration and miniaturization of integrated coils. A 300W experimental platform was built, and some comparative experiments were conducted. Compared to the coupler of IPT, the self-resonance coupler has improved performance in transmission power, anti-offset, and over-coupling suppression. HPT performs better than CPT in transmission distance, transmission power, and transmission efficiency. It has some advantages in the medium-power application scenarios such as inspection robots and drones, which expect couplers to have a smaller volume, lighter weight, higher power density and better anti-offset.

Misalignment tolerance represents a critical challenge in unmanned aerial vehicle wireless power transfer (UAV-WPT) systems. Conventional approaches enhance positional and angular misalignment tolerance by generating wide-range omnidirectional magnetic fields through transmitter design or rotating field techniques. However, these methods exhibit a low magnetic field utilization rate since UAVs typically require only specific field orientations post-landing rather than omnidirectional fields. This article proposes a UAV-WPT system employing two orthogonal bipolar transmitting coils with a targeting magnetic field to achieve omnidirectional powering and enhance the magnetic field utilization rate simultaneously. COMSOL simulations are used to analyze the magnetic field distribution of the transmitter, and the principles for generating a targeting magnetic field through excitation current control are explained. The amplitude and phase of the excitation currents are controlled by adjusting the duty cycle and phase difference of the two half-bridge inverters. The mathematical relationships between maximum output power, maximum input power, and maximum transmission efficiency with respect to duty cycle and phase difference are established. A targeting magnetic field control method combining phase difference and duty cycle scanning is proposed. Experimental results show that the system can establish a targeting magnetic field within 85 ms, with a maximum output power of 216.2 W and a peak DC-to-DC efficiency of 89.5%. The DC-to-DC efficiency exceeds 76.1% across <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>100 mm positional misalignment and arbitrary angular misalignment. The DC-to-DC efficiency of the targeting magnetic field mode is at least 5% higher than that of the rotating magnetic field mode.

Wireless power transfer (WPT) systems with battery loads exhibit low damping, which results in significant current overshoots and sustained oscillations when subjected to disturbances. Pulse skipping control is frequently employed in WPT systems and can be considered a significant disturbance. However, the accurate analysis of the resulting system dynamics and current envelope remains a challenge. To allow accurate evaluation of the performance of WPT systems with pulse skipping, this paper introduces a new reduced-order model for WPT systems through balanced truncation. The derived second-order model enables real-time computation of the current envelopes on both sides of the system, facilitating a comprehensive analysis of the overall performance. This paper presents two case studies that utilize this reduced-order model to optimize pulse skipping control in WPT systems. The first study proposes a pulse skipping soft-start strategy based on this model. The second study investigates the optimal pattern of pulse skipping modulations. Simulations and experimental results from a laboratory prototype validate the effectiveness and feasibility of the proposed reduced-order model and the design and refinement of the pulse skipping patterns.

PV-integrated wireless power transfer system for high-efficiency electric vehicle charging applications

Gastric cancer remains a global health challenge with high mortality rates, underscoring the urgent need for advanced diagnostic tools. While conventional gastroscopy encounters patient reluctance due to procedural discomfort, wireless capsule endoscopy (WCE) provides a non-invasive alternative but faces challenges, including intricate motion control, constrained power supply, and restricted detection capability. This study presents a bimodal imaging WCE system that integrates near-infrared fluorescence and white-light imaging, enhanced with linear magnetic navigation and motion-robust wireless power transfer. The innovative geometrically polarized permanent magnet configuration enables sensorless adaptive and precise linear navigation (position accuracy: 0.29 mm; orientation accuracy: 0.97°). The axially self-aligning coil configuration achieves motion-robust power transfer, with capacity further enhanced by a novel internal magnet layout. Experimental validation demonstrates stable high-power reception (2 W), reduced operator dependency through the linear navigation, and improved lesion detection capability via bimodal imaging. This breakthrough addresses the fundamental limitations of current WCE systems, showcasing a mechatronic approach to advance gastric cancer diagnostics.

AI-Optimized Inductive Coupling Coil Design for Safe Wireless Power Transfer in Implantable Medical Devices: An Interdisciplinary Engineering–Healthcare Approach

Wireless Power Transfer (WPT) systems have garnered increasing attention with the rapid advancement of Electric Vehicles (EVs). However, the design optimization of the WPT system remains a significant challenge due to the inherent multidisciplinary complexity. The core of the WPT system is the Magnetic Coupling Mechanism (MCM), which plays a critical role in energy transmission. To enhance the overall WPT system performance, this paper proposes an improved Marine Predators Algorithm (MPA), integrated with the Tent map and Quasi-oppositional learning strategy (TQMPA), to achieve the efficient optimization of the MCM. A comprehensive analytical model of the MCM using double-D coils is developed, and relevant performance evaluation metrics for the WPT system are systematically derived. Benchmark testing on 20 standard functions indicates that TQMPA converges faster and escapes local optima more effectively. These improvements are especially prominent in low-dimensional unimodal optimization problems. On this basis, a multidisciplinary design optimization framework is formulated based on the collaborative optimization strategy, incorporating key subsystems including transmission performance, electromagnetic safety, and structural compactness. Simulation results confirm the superior optimization performance of TQMPA over the original MPA. While maintaining electromagnetic safety and offset tolerance, the proposed method achieves a notable 53.27% improvement in the transmission efficiency and a significant 49.23% reduction in the ferrite volume, which can offer valuable insights for future intelligent optimization in complex WPT system design.

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.

New design of a high-efficiency rectenna for wireless power transfer in 5G applications.

Owing to the unique shape and spatial constraints of autonomous underwater vehicles (AUVs), designing magnetic couplers presents significant challenges. Current researches on AUV coupler design has produced limited findings regarding the magnetic integration of arc-shaped coils, and there is a notable absence of systematic methods for optimizing parameter design. In terms of the aforementioned challenges, this paper proposes arc-shaped magnetic integrated couplers designed for AUV wireless power transfer (WPT) systems, along with a multi- objective parameter optimization plan. The proposed magnetic integration scheme can achieve approximate decoupling among compensation inductors and between compensation inductors and the main coils. This design offers the benefits of reducing spatial leakage magnetic flux and significantly improving system power density. Furthermore, this scheme can be extended to other underwater high-order WPT systems. Subsequently, using output power and efficiency as optimization objectives and migration adaptability as one of the constraints, a multi-objective salp swarm algorithm (MSSA) is applied. Finally, the proposed quasi-decoupled arc-shaped magnetic integrated coupler is applied to an underwater hybrid WPT system with input series and output parallel (ISOP) configuration, and the feasibility of the proposed scheme is validated through experiments. The experimental results demonstrate that, under a rated power of 1.2 kW, the peak efficiency of the system is 92.4%, and the allowable offset ranges in the axial, radial and angular directions are [-20mm, 20mm], [0mm, 20mm] and [-20°, 20°], respectively.

Boosting energy harvesting and wireless power transfer via 2D MXene-CoFe2O4 filled piezoelectric polymer–inorganic nanofiber multiferroics

Low-altitude uncrewed aerial vehicles (UAVs) have become integral enablers for the Internet of Things (IoT) by offering enhanced coverage, improved connectivity and access to remote areas. A critical challenge limiting their operational capacity lies in the energy constraints of both aerial platforms and ground-based sensors. This paper explores WLPT as a transformative solution for sustainable energy provisioning in UAV-assisted IoT networks. We first systematically investigate the fundamental principles of WLPT and analysis the comparative advantages. Then, we introduce three operational paradigms for system integration, identify key challenges, and discuss corresponding potential solutions. In case study, we propose a multi-agent reinforcement learning framework to address the coordination and optimization challenges in WLPT-enabled UAV-assisted IoT data collection. Simulation results demonstrate that our framework achieves up to 15.1% reduction in peak AoI compared to conventional multi-agent deep reinforcement learning (MADRL) methods. Finally, we discuss some future directions.

This paper systematically investigates the effects of pressure on the performance of underwater wireless power transfer (WPT) systems by modeling the changes in coupler parameters and their impact on system power and efficiency. A comprehensive theoretical framework is developed to establish quantitative relationships between pressure variations and the coupler’s relative permeability, self-inductance, and mutual inductance, as well as system performance metrics. To ensure precision, four key shape factors are introduced, and an interior-point optimization-based fitting method is proposed for parameter determination. The results indicate that with every doubling of pressure, the mutual inductance and self-inductance of the coupler decrease by approximately 0.76% and 0.43%, respectively, while system power increases by 1.6% and efficiency exhibits a marginal decrease of 0.02%. The developed model demonstrates high reliability and precision, achieving coupler parameter errors below 1% and efficiency errors under 7%, thereby providing a solid basis for the evaluation and optimization of underwater WPT systems under varying pressure conditions.

Omnidirectional wireless power transfer via magnetoelectric composite transducer arrays for untethered bioelectronic systems

Wireless Power Transfer (WPT) technology has emerged as a promising solution for charging electric vehicles (EVs), offering enhanced convenience, safety, and reliability compared to conventional wired charging methods. This paper presents an overview of wireless power transfer systems for electric vehicle applications, focusing on inductive and resonant coupling techniques that enable efficient energy transmission across an air gap. Key components of WPT systems, including power electronics, coupling coils, compensation networks, and control strategies, are discussed in detail. The performance of wireless charging is evaluated in terms of power transfer efficiency, alignment tolerance, electromagnetic compatibility, and system scalability. Additionally, challenges such as coil misalignment, power losses, electromagnetic field exposure, and infrastructure cost are analyzed. Recent advancements in dynamic wireless charging, standardization efforts, and integration with smart grid technologies are also highlighted

ABSTRACT Undersea ocean currents tend to cause multidirectional offsets of the coupling mechanism in the wireless charging system of autonomous underwater vehicles (AUVs), leading to reductions in power supply efficiency and power, and even impairing the normal operation of the system; meanwhile, the internal space of AUVs is extremely limited. To address these issues, this paper proposes a dual‐output underwater wireless power transfer (UWPT) system based on a lightweight magnetic‐integrated coupler and its parameter optimization method, which is suitable for AUV applications. First, a novel lightweight magnetic‐integrated arc‐shaped coupler is designed. Then, a dual‐output WPT topology matched with this coupler is developed, and an adaptive modulation strategy based on the parameters of the magnetic coupling structure and compensation structure is established. The first type of modification factor leverages the relatively stable characteristic of mutual inductance offset variation to construct a mapping relationship between the mutual inductance offset ratio and compensation structure parameters, effectively suppressing the equivalent mutual inductance fluctuation caused by coil offset. The second type of modification factor can realize the control of output gain by regulating the equivalent mutual inductance parameters. Finally, an experimental prototype with a rated power of 1 kW is built for actual measurement and verification. The experimental results show that the maximum allowable axial offset range is [−50 mm, 50 mm]; the angular offset range is [−20°, 25°], and the maximum fluctuations of the system output current and output voltage are both maintained within 7%.

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.

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance (p&lt;0.001), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%.