In order to identify the road friction coefficient of the left and right sides of the vehicle more accurately, this paper presents an estimation framework based on a novel tyre model and modified square-root cubature Kalman filter (SCKF). To begin with, a novel tyre model is proposed by adaptively calculating the longitudinal and lateral stiffness and the effective friction coefficient. The tyre forces calculated by this model are more accurate than those calculated by the Brush model. Then, to avoid the influence of abnormal measurement noise on the estimation effect, we develop an improved SCKF (ISCKF) algorithm based on the maximum correntropy criterion. The algorithm can update the measurement noise covariance adaptively. Furthermore, a real-time estimation scheme of road friction coefficient is designed by combining the vehicle dynamics model with the proposed novel tyre model and ISCKF algorithm. Finally, the performance of the presented method is verified by the co-simulation of CarSim and MATLAB/Simulink. The results show that the designed estimation scheme not only has excellent robustness in the case of abnormal measurement noise interference but also has good adaptability to the uncertainty of road friction coefficients distribution.

In this paper, after investigating the coupling effect in a permanent magnet synchronous in-wheel motor, a robust control method for active suspension of hub-driven electric vehicles (EVs) to enhance the performance of the in-wheel motor and the vehicle is proposed. Based on the electric vehicle model addressing the coupling effect between the electromagnetic excitation of the permanent magnet synchronous motor (PMSM) and the transient dynamics in EVs, the influence of the coupling effect on the motor and the vehicle performance is analyzed. The results reflect that the coupling effect in in-wheel motors intensifies the magnetic force oscillation, aggravates the eccentricity of the rotor, deteriorates the motor operation performance, and worsens the ride comfort. To suppress the magnetic force oscillation in motor and enhance the vehicle comfort, the active suspension system considering five aspects of suspension performance is introduced. Simultaneously, on the basis of Lyapunov stability theory, a reliable robust H_ꝏ controller considering model uncertainties, actuator failure and electromagnetic force interference is designed. The simulation results reflect that the robust H_ꝏ feedback controller can not only achieve better ride comfort, but also restrain the coupling effect in the motor. Meanwhile the other requirements such as the road holding capability, the actuator limitation, and the suspension deflection are also maintained. The proposed robust control method demonstrates a potential application in the practice of EV control.

For in-wheel driving vehicle electric vehicles (EVs), mechanical electromagnetic coupling effect caused by the air gap deformation in permanent magnet synchronous hub motor and intensified by the road excitation deteriorates the EVs performance. In this paper, after studying the numerical method for multi-field coupling problems of hub-driving vehicle under the coupled action of electromagnetic field and mechanical field. The experimental validation is investigated. The results indicate that the multi-field coupling effect in hub-driving motor worsens the dynamics performance of the vehicle. To enhance the vehicle performance, suppress mechanical electromagnetic coupling effect and, at same time, reduce the influence of controllable suspension time delay, a delay-dependent H∞ controller is designed based on Lyapunov theory. By applying the particle swarm optimization (PSO) algorithm and the linear matrix inequality theory, the desired output controller gain is derived. Numerical simulations reflect that the active suspension controller considering control time delay not only achieves the favorable riding comfort performance and restrains the coupling effect in hub driving motor but also ensures the suspension deflection and the safety performance requirement. Moreover, it maintains the closed-loop asymptotically stability regardless of t the variation on the sprung mass and control time delay.

Momentum wheel is the main execution unit for the attitude control of three-axis stabilized satellite. On the long-life satellites, momentum wheels are often required working stably and reliably for several years or even ten years. It is necessary to evaluate the remaining life of on-orbit satellite momentum wheel. The failure mechanism of momentum wheel is very complex and it is difficult to predict its long-range performance with trend extrapolation method. Data-driven prognostic is combined with similitude method to predict the remaining life of momentum wheel. The causes of the momentum wheel failure are analyzed to select key parameters to indicate life characteristics of momentum wheel. The lifetime performance data of some other momentum wheels failed are used to train neural network models to describe the degradation process of similar momentum wheels, which is used for predicting momentum wheel on-orbit. In the case study, this method showed a good performance. It is more applicable than trend extrapolation and has much better performance.

The mechanical‐electrical‐magnetic coupling effects caused by the air gap deformation in in‐wheel motors exacerbate motor magnetic force oscillation, deteriorating the in‐wheel motor operation and ride comfort. In order to enhance vehicle performances, it is imperative to mitigate these coupling effects during the design of hub‐driven electric vehicle suspensions. In this paper, the analytical investigation is conducted on the coupling effects between electromagnetic excitation and transient dynamics in EVs based on the half‐vehicle model. The dynamic responses of hub‐driven EVs considering coupling effects are explored under both open‐loop and closed‐loop systems at various speeds and road conditions. Numerical simulations demonstrate that the closed‐loop system with wheelbase preview robust control exhibits superior comprehensive performance in suppressing coupling effects of the rear wheel hub motor and enhancing ride comfort compared to the system without preview control. Furthermore, as vehicle speed increases, robust preview control effectively reduces fluctuations in pitch angle acceleration, pitch angle, and rear wheel rotor eccentricity. In addition, robust preview control yields better performance in reducing vehicle body centroid acceleration and rear wheel rotor eccentricity and enhancing motor and vehicle performance with increasing road roughness. This work provides a practical vibration model and control reference for studying the dynamic characteristics of hub‐driven EVs.