The market for electric vehicles is growing rapidly. Among them, the demand for a dual motor type 4 WD (Four -Wheel Driving) system is increasing. In this paper, we present the Torque Matching Strategy (TMS) method to select the optimal torque distribution ratio for dual motors. The TMS controller operates to set the optimal efficiency point by linearizing the drive efficiency combination of the two motors. Driving simulation and testing were performed through five drive cycles in the driver model interworking environment implemented in MATLAB and Carsim. The optimal distribution ratio was derived according to the front and rear gear ratios under the load condition, and the driving was verified by comparing it with the TMS control method. The efficiency was numerically verified by comparing the power loss of the driving motor. It reduced up to 34% in Urban Dynamometer Driving Schedule and up to 56.3% in Highway fuel efficiency test. The effectiveness of the TMS control method was demonstrated through the distribution rate trend based on the operation cycle and power loss.

Magnetic flux leakage, a significant issue in motors that hinders performance and torque production, was investigated in this study. This study focused on analyzing the impact of varying the thickness of a flux barrier embedded within the rotor of interior permanent magnet axial flux motors. Magnetic properties, including torque, leakage flux ratio, and inductance, were evaluated through simulation and analysis to understand the influence of the flux barrier on the motor’s characteristics. The results demonstrated significant improvement: the motor using the flux barrier exhibited an 11.28% reduction in leakage flux and a torque increase of 6.85 Nm compared to the conventional motor. Additionally, the design achieved maximum torque with a 2 mm thick barrier and a 10° phase angle. This study highlights the effectiveness of the flux barrier structure in enhancing the performance of axial flux motors and suggests valuable insights for motor design.

Steer-by-wire (SBW) systems in wheel-steering vehicles enhance maneuverability by eliminating mechanical linkages. However, they are susceptible to delays between steering input and pinion response, which can compromise control precision and safety. To mitigate these delays, we developed a machine learning-based compensation method employing a hybrid architecture of convolutional neural networks (CNNs) and gated recurrent units (GRUs) to predict and adjust pinion behavior in real time. The model was trained using experimental data collected from a four-wheel steering test platform, including steering angle inputs, motor signals, and pinion position feedback. By learning the relationship between steering commands and rack force, the model enables dynamic delay correction under both nominal and fault conditions. The system is implemented on an NXP microcontroller and validated through experimental testing, and compared with other hybrid model configurations for performance evaluation. The results demonstrate that the CNN–GRU approach reduces the average steering delay to 3 ms, outperforming conventional PID tuning methods while maintaining high accuracy and system stability.

This paper presents an arc-shaped rotor notch topology for reducing cogging torque in Axial Flux Permanent Magnet (AFPM) motors. Unlike traditional techniques such as magnet skewing or segmentation, which often increase manufacturing complexity, the proposed notch enables localized flux attenuation without altering the stator or magnet volume. By aligning the notch with high-flux-density regions beneath the magnet edge, the design passively modulates magnetic energy and suppresses cogging torque. To evaluate the effectiveness of the proposed notch geometry, we propose a geometry-sensitive analytical indicator that combines magnetic field strength with flux attenuation efficiency. This indicator serves as a theoretical tool to guide notch design and is shown to correlate well with FEA and experimental results, including a 27.5% reduction in cogging torque and only a 5.4% decrease in average torque. This approach offers a simple, cost-effective, and scalable solution for cogging torque suppression without compromising performance or manufacturability. It is especially suitable for AFPM drive employing bobbin-inserted windings, where wider slot openings tend to amplify cogging torque. This insight offers a new strategy for geometry-driven rotor design optimization in precision electric mobility systems.