Loss characteristics and loss separation methods of ultrahigh-speed permanent magnet motors (UHSPMSM) are comprehensively studied in this article. In view of the influence of stator structure, material, winding type, control method, and fluid dynamics, the ac copper loss equivalent calculation model, fluid dynamic model, and finite element simulation model are established, respectively, to calculate and analyze the loss of each part of UHSPMSM. Moreover, a set of loss separation methods in dynamic is designed, which can separate stator core loss, dc copper loss, eddy copper loss, rotor eddy current loss, and mechanical friction loss of UHSPMSM one by one under the condition of considering the high-frequency electromagnetic field and fluid dynamic. A 550 000 r/min prototype and back-to-back loss separation setup is built to complete the experiment. Finally, loss distribution of the motor is obtained, which provides a certain reference for the loss research of an ultrahigh speed motor.

Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the battery’s voltage, current, temperature, and other data during the charging stage were measured and recorded through experiments. Incremental Energy Analysis (IEA) was conducted on the charging data to extract various incremental energy characteristics. The Pearson correlation method was used to verify the strong correlation between the proposed characteristics and the battery SOH. This paper includes experimental verification of the method for both battery cells and battery pack. For the battery cell, a complete multi-feature sequence was formed based on the incremental energy curve characteristics combined with temperature characteristics. For the battery pack, the characteristics of the incremental energy curve were supplemented with Variance of Voltage Means (VVM) as an inconsistent feature, combined with Standard Deviation of Temperature Means (SDTM), to create a complete multi-feature sequence. The features were then input into the CNN-KAN-BiLSTM deep learning model developed in this study for training, successfully estimating the SOH of lithium batteries. The results demonstrate that the proposed method can accurately estimate the SOH of lithium batteries, even though the SOH degradation of lithium batteries has significant nonlinear characteristics. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the lithium battery pack were 0.3910 and 0.4797, respectively, with an average coefficient of determination (R²) exceeding 99%. The final SOH estimation MAE values for battery cells at different charging rates of 0.1 C (250 mA), 0.2 C (500 mA), and 0.5 C (1250 mA) were 0.2728, 0.3301, and 0.2094. The RMSE were 0.3792, 0.4494, and 0.2699, respectively. The corresponding R² values were 98.76%, 97.07%, and 99.37%, respectively. Finally, the effectiveness and universality of the method proposed in this paper were verified using the NASA battery dataset and the CALCE battery dataset.

The error of the radial displacement sensor causes a deviation between the rotor levitation position of the bearingless motor (BM) and the geometric center of the stator. As a theoretical basis, force analysis of the rotor is performed first to analyze the effects caused by the aforementioned deviation. A radial levitation force modeling scheme for BM is proposed in this article, whose derivation is directly on the rotor coordinate system. The proposed modeling scheme has a simpler form of expression and a more straightforward physical concept than the traditional scheme. To completely compensate for the aforementioned deviation, a levitation position eccentricity compensation scheme for BM based on the tracking of the minimum antieccentric levitation current is proposed in this article. The proposed scheme takes the amplitude of the antieccentric levitation current vector as the index, which characterizes the degree of eccentricity of the rotor and dynamically adjusts the rotor levitation position in two dimensions and, finally, makes the rotor stably levitated at the optimal point, where the amplitude of the antieccentric levitation current vector is minimum. The absence of parameter dependence, ease of implementation, and generality makes the proposed scheme attractive. Meanwhile, the design of the displacement regulator can also be simplified. Sufficient simulation and experiments verify the effectiveness of the proposed scheme.

Lithium-ion batteries are the primary power source for new energy vehicles, making accurate estimation of their state of health (SOH) essential for ensuring the safe operation of battery systems. This paper proposes a Capacity–Power Analysis (CPA) method that incorporates temperature features to enhance feature extraction across a broader range. Additionally, we introduce an SOH estimation method for lithium batteries based on a Convolutional Neural Network (CNN) and a Kolmogorov–Arnold Network (KAN). By extracting the capacity–power curve and average temperature features during constant-current and constant-voltage charging, the CNN-KAN model establishes a nonlinear mapping relationship between the extracted features and SOH, enabling high-precision SOH estimation for lithium-ion batteries. Four 18650 batteries were tested under various charging and discharging conditions in a laboratory setting. The coefficient of determination (R²) exceeded 96.4%, the root mean square error (RMSE) was below 0.86%, and the mean absolute error (MAE) was under 0.7%, confirming that the proposed method demonstrates excellent estimation performance.

In this paper, the maximum power point tracking problem of variable-speed wind turbine systems is studied. Both mechanical part and electromagnetic part of the wind energy conversion system are taken into consideration. In view of the different time scales between the mechanical part and electromagnetic part, singular perturbation theory is applied to model the system in order to cope with the stiffness caused by the two-time-scale characteristic. Then, linear parameter varying (LPV) model is developed to approximate the nonlinear singularly perturbed model. In consideration of data detection time of the state variables, in practice, the control inputs are dependent on the states with a small time delay. Therefore, a novel delay dependent $H_{\infty }$ controller is designed to make the rotor speed track the reference rotor speed. Furthermore, it is proved that the closed-loop system under control is asymptotically robust stable using Lyapunov theory. In the end, an example simulation verifies the effectiveness and advantages of the developed method by means of comparison with optimal torque control.