It is crucial for inductive power transfer (IPT) systems to achieve load-independent outputs. However, the complex charging environment brings a series of challenges to the parameter design of the IPT system. For example, in the case of wireless charging of electric vehicles, there are situations such as large load variation range and inaccurate coupling coil alignment, which requires the IPT system to maintain constant current (CC) and constant voltage (CV) outputs under variable coupling conditions. Therefore, this paper proposes a parameter design method for the double-sided LCC compensation network, which can decouple the CC and CV outputs from the mutual inductance parameter, and by changing the operating frequency, the CC output and CV output of the IPT system can be mutually switching, and with zero phase angle (ZPA) input at the same time. In this paper, the CC and CV output modes of the double-sided LCC compensation network is designed based on the two-port network, and then the ZPA input condition is deduced through the mutual inductance model. To maximize the system efficiency, an improved genetic algorithm is proposed to solve the system CC and CV output operating frequencies. Finally, a 3.3kW prototype and simulation model are established to verify the correctness of the theoretical analysis.

Inductive power transfer (IPT) technology is widely used because of its high convenience especially for charging of electric vehicles. It has remained a high failure rate in recent years due to the large number of semiconductor components they contain. In this article, to address the problem that rectifier with constant-current output characteristics in the IPT cannot occur to open-circuit fault, a rectifier protected method based on SS type IPT system is designed. First, the hazards and impacts on the system after a diode fault are analyzed. Then, a method for parameter design of protective devices have developed that does not affect the compensation parameter design of the original system, which maintain high efficiency operation after open-circuit fault, and a fault detection technology based on dc bias of the fault eigenvalue is proposed. This article solves the problem that the rectifier cannot be open-circuit faulted of the traditional IPT constant-current topology, which improves the reliability and safety of the system. Finally, experiments under different loads verify that the system rectifier can maintain an operating state with an efficiency of 91.78% after a diode open-circuit fault occurs, as well as the accuracy of the fault detection method.

In the traditional random switching frequency pulsewidth modulation (RSFPWM), the frequency range should be set large enough to make the harmonics evenly distributed without spikes, which may also disperse the harmonics to the lower frequency band that is hard to be filtered. To avoid the low-frequency harmonics induced by widening the frequency range, a double frequency-band random pulsewidth modulation (DFB-RPWM) strategy is proposed in this article, to form a more uniform harmonic distribution in a narrower frequency range. In this strategy, each harmonic cluster is divided into two harmonic peaks, and the relative position of the two harmonic peaks influences the harmonic dispersion effect. By adopting the harmonic dispersion theory of the frequency spectrum, the condition that makes lower harmonic amplitudes and more uniform harmonic distribution can be determined. The simulation and experiment are conducted to verify the effectiveness of the DFB-RPWM, showing a better performance than the RSFPWM.

Parameter identification-based control strategies are considered the preferred solution for achieving constant current (CC) and constant voltage (CV) charging control in communication-free inductive power transfer systems. However, identification errors in mutual inductance, load resistance, and other parameters can affect control accuracy. To improve control accuracy and response speed, a communication-free control strategy based on neural networks and deep transfer learning is proposed. This strategy eliminates the need to identify parameters like mutual inductance and load resistance. Only a few measured datasets are required to train the network model offline, and a trained model can online estimate output voltage/current. By combining with a controller, CV/CC charging control can be achieved under conditions of real-time variations in mutual inductance and load resistance. The experimental results show that the proposed control strategy achieves a static error of only 1.5% and a response time of no more than 24 ms. Compared to the parameter identification-based control strategy, the proposed strategy demonstrates lower static error, shorter response time, and a wider dynamic range.

To achieve stable output and enhance the robustness of input-series–output-series multichannel inductive power transfer system under parameter inconsistency and load perturbation, an improved H _∞ robust control is proposed in this article. First, the control effect of multichannel system is analyzed, which transfers the controller design into single system with uncertainty. Then, a linear model is established based on generalized state space averaged method and the influence of parameter inconsistency and load perturbation is analyzed. After that, the uncertain model is obtained via frequency-domain analysis, generating a numerical solution of weighting function to describe the uncertainty of parameter inconsistency and load perturbation. Based on the proposed weighting function, the improved H _∞ robust controller is designed and the dynamic performance and robustness of the close-loop system is analyzed. Finally, a down-scale prototype with three channels is built to verify the effectiveness of the proposed method. The experimental results show that the system obtains good dynamic performance and strong robustness against parameter inconsistency and load perturbation. When load changes between 60 and 80 Ω, the settling time is less than 4.7 ms with the overshoot being less than 7.3%. Even against large load perturbations, the settling time is still less than 8 ms.

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies.