The structure and magnetic circuits of axial-radial combined permanent magnet eddy current couplers are more complicated than those of axial or radial permanent magnet eddy current couplers, the degree of coupling is deeper, which will affect the device performance. In this paper, a novel analytical model for an axial-radial combined permanent magnet eddy current coupler is proposed based on the equivalent magnetic circuit method. The distribution of the magnetic field, the relationship between the slip and torque, the influence of the structural parameters on the transmission performance, the 3-D correction and thermal compensation are studied. Combined with the concept of the micro-element method, the air gap magnetic density, eddy current density and torque are accurately calculated, which solves the problem that the existing model cannot analyse the axial-radial combined structure. To show the effectiveness of the proposed analytical model, the transmission performance of an axial-radial combined permanent magnet eddy current coupler prototype is investigated, and the analytical calculation results are compared with the results of the finite element method simulations and experimental tests.

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 10⁵ kW²) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

The escalating global energy crisis has intensified the demand for sustainable hydrogen production through electrochemical water splitting. Herein, we report a novel oxygen-vacancy-rich bifunctional electrocatalyst, Pt-NiSe@NiFe-LDH-Ov, synthesized via a facile electrodeposition and reduction method. It demonstrates exceptional performance, requiring low overpotentials of 280 mV for the HER and 344 mV for the OER to achieve current densities of 50 and 100 mA cm⁻², respectively, in 1.0 M KOH. When employed for overall water splitting, the system requires a cell voltage of only 1.878 V to reach 50 mA cm⁻². Notably, in an anion exchange membrane water electrolyzer (AEMWE), the performance shows significant enhancement with increasing operating temperature (20 to 60 °C), particularly at high current densities (>200 mA cm⁻²), highlighting its excellent thermal adaptability. The superior activity is attributed to the synergistic effect between the Pt-NiSe and NiFe-LDH interfaces and the abundant oxygen vacancies, which collectively enhance charge transfer and optimize the adsorption of reaction intermediates.

Tungsten-based electrocatalysts are considered promising alternatives to noble metal catalysts for electrochemical water splitting; however, their practical application is severely limited by inherently sluggish reaction kinetics and low density of active sites. In this study, a rationally designed Co-doped WO₃@Mn₂WO₄ core–shell nanostructure with oxygen vacancy modulation (Co-WO₃@Mn₂WO₄-0.6-O_v) was fabricated via a facile hydrothermal synthesis combined with post-treatment using borohydride reduction. By precisely tuning the Co doping concentration (0, 0.3, 0.6, and 0.9 mM), the optimal catalytic performance was identified. Structural characterization confirmed the successful construction of the core–shell heterostructure and the effective modulation of the electronic structure of the W/Mn active sites through Co doping and oxygen vacancy engineering. Electrochemical measurements demonstrated that the optimized Co-WO₃@Mn₂WO₄-0.6-O_v catalyst exhibits a Tafel slope as low as 53.8 mV dec-1 for the hydrogen evolution reaction (HER) under acidic condition of 0.5 M H₂SO₄, achieving the best performance among all prepared samples and reflecting significantly accelerated electrocatalytic kinetics. This synergistic strategy combining heteroatom doping and oxygen vacancy engineering is also applicable to overall water splitting, enabling a membrane electrode assembly to achieve excellent long-term stability for over 40 h in both acidic and alkaline media. The enhanced catalytic performance is attributed to the synergistic effects of Co doping, oxygen vacancies, and the core–shell structure, which collectively promote interfacial charge transfer and increase the exposure of active sites.