An analytical approach is proposed for the cogging torque of a flux-switching permanent magnet machine based on the general air-gap field modulation theory. The modulation process is first investigated, and the cogging torque generation mechanism is analyzed based on the distribution of the air-gap magnetic flux density harmonics. Thus, the relationship between the air-gap field harmonics and the cogging torque is revealed, and the contribution of each harmonic to the cogging torque is calculated using the proposed method. Simultaneously, the characteristics of the cogging torque, including amplitude and frequency, are analyzed. Subsequently, the calculated cogging torque is compared with the simulated torque using finite element analysis. The two results exhibit considerable consistency, confirming the feasibility of the proposed method. Moreover, a prototype experiment is conducted, and the cogging torque is measured to verify the effectiveness of the proposed method.

The rapid growth of renewable energy sources, such as wind and solar power, together with global carbon neutrality targets, is driving the transformation of the energy system. To mitigate the intermittency inherent in renewable energy, the integration of energy storage systems has become imperative. In China, the expansion of electric vehicles (EVs) has positioned them as mobile energy storage units, with the stock of new energy vehicles (NEVs) reaching 31.4 million by 2024. While retired EV batteries retain 70% to 80% of their original capacity, they are suitable for second-life applications, such as grid peak shaving and distributed storage, offering both environmental and economic benefits. However, safety concerns persist, requiring accurate predictions of state of health (SOH) for safe operation and optimal utilization of these batteries. To address this challenge, this paper proposes an improved Transformer model, where discrete wavelet transform (DWT) is first employed to deal with the inherent noise during charge/discharge cycles. The serial Convolutional Neural Network (CNN) structure is utilized to mine local health factors and position information based on residual connections encoded into the Transformer network. The trend fusion module is added to improve the network integration capability. Evaluations using both public center for advanced life cycle engineering (CALCE) and experimental lifetime battery datasets B_X demonstrate the superiority and effectiveness of the DWT-CNN-Transformer model. It showcases faster convergence speed and higher optimization accuracy compared with other baseline approaches, significantly bolstering the precision and robustness of SOH predictions.

Recent technological advancements have propelled remarkable progress in servo systems, resulting in their extensive utilization across various high-end applications. A comprehensive review of high-quality servo system technologies, focusing specifically on electrical motor topologies and control strategies is presented. In terms of motor topology, this study outlines the mainstream servo motors used across different periods, as well as the latest theories and technologies surrounding contemporary servo motors. In terms of control strategies, two well-established approaches are presented: field-oriented control and direct torque control. Additionally, it discusses advanced control strategies employed in servo systems, such as model predictive control (MPC) and fault tolerance control, among others.