This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric vehicles, have their performance and safety significantly impacted by temperature. This paper begins by introducing the fundamental components and operating principles of lithium-ion batteries, followed by an analysis of how temperature affects battery performance and safety. Next, the methods for measuring and predicting battery temperature are categorized and discussed, including model-based methods, data-driven methods, and hybrid approaches that combine both. Finally, the paper summarizes the application of temperature prediction in a BMS and provides an outlook on future research directions.

Magnetic gears (MGs) have gained increasing attention due to their sound performance in high torque density and low friction loss. Aiming to maximize the torque density, topology design has been a popular issue in recent years. However, studies on the optimization comparisons of a general MG topology pattern are very limited. This paper proposes a Taguchi-method-based optimization method for a general MG topology pattern, which can cover most of the common types of radially magnetized concentric-surface-mounted MGs (RMCSM-MGs). The Taguchi method is introduced to evaluate the influence of each parameter in MGs. Moreover, the parameter value range is re-examined based on the sensitivity analysis results. The genetic algorithm (GA) method is adopted to optimize the topology pattern in the study.

This paper presents an auxiliary passive circuit and control design for wireless power transfer (WPT) systems in DC microgrids to achieve optimal power transfer efficiency while maintain accurate output voltage regulation. An auxiliary inductance is added at the transmitter resonator to form a current sink to ensure zero voltage switching (ZVS) of the primary-side full-bridge inverter with even extremely light load conditions. Moreover, an adaptive proportional–integral (PI) controller is adopted to track the output voltage references by regulating the phase shift angle of the phase shift control for the full-bridge inverter. The coefficients of the adaptive PI controller are determined by the inductance of the auxiliary inductance. Both simulation and experimental results validated the effectiveness of the proposed circuit and control design in achieving optimal efficiency and output voltage regulation for WPT systems in DC microgrids with source and load variations.

In order to meet the demands of desirable efficiency, transformerless DC/DC equipment with great voltage step-down are inevitable needed. This research offers a unique type of high-frequency, high-voltage-gain DC/DC converter, which comprises a switched capacitor (SC) converter and a buck converter. Thanks to the transformation of a two-stage converter to a single-stage converter, it has a considerable ratio of step-down voltage transformation and a reasonable duty cycle. In addition, it can permit low voltage stress on the switches. The simple control method and easy driving circuit implementation makes it scalable for high-power-level devices. Low cost can be realized as fewer components are needed. Under all operational circumstances, total soft-charging and low equipment voltage stresses are accomplished. Compared to those classic high-voltage-gain converters, the proposed converter exhibits merits of higher efficiency, higher flexibility, lower ripples, and lower costs. A comprehensive analysis is carried out for the converter’s steady-state operations. With a 1 MHz switching frequency, a 900 W prototype of a 20-time converter is constructed, with a peak efficiency of 92.5%. Simulations and experiments verify the effectiveness of the theoretical investigation of the converter’s operation.

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments.