Ze Zhao

In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can further compromise vertical and longitudinal vehicle dynamics. This paper presents a comprehensive study to reveal the coupled vertical–longitudinal effect on suspension-in-wheel-motor systems (SIWMS) along with a viable optimization procedure to improve ride comfort and handling performance. First, a UEMF model is established to analyze the mechanical–electrical–magnetic coupling relationship inside an IWM. Then a road–tire–ring force (RTR) model that can capture the transient tire–road contact patch and tire belt deformation is established to accurately describe the road–tire and tire–rotor forces. The UEMF and the RTRF model are incorporated into the quarter-SIWMS model to investigate the coupled vertical–longitudinal vehicle dynamics. Through simulation studies, a comprehensive evaluation system is put forward to quantitatively assess the effects during braking maneuvers under various road conditions. The key parameters of the SIWMS are optimized via a multi-optimization method to reduce the adverse impact of UEMF. Finally, the multi-optimization method is validated in a virtual prototype which contains a high-fidelity multi-body model. The results show that the longitudinal acceleration fluctuation rate and the slip ratio signal-to-noise ratio are reduced by 5.07% and 6.13%, respectively, while the UEMF in the vertical and longitudinal directions varies from 22.2% to 34.7%, respectively, and is reduced after optimization. Thus, the negative coupling effects of UEMF are minimized while improving the ride comfort and handling performance.

This paper presents a comprehensive study revealing the influences of tire pressure variations and their distribution among four tires on vehicle dynamics performance and driver's steering workload for a four wheel independent drive electric vehicle (FWID EV). Then a direct yaw-moment control (DYC) strategy employing an unbiased adaptive model predictive control (MPC) algorithm is proposed to compensate for the influences. An unbiased estimation strategy is developed to update the time-variant prediction model for the adaptive MPC system. An extended magic formula (MF) tire model and a modified Unitire model involving tire inflation pressure are employed to describe the tire longitudinal and lateral forces, respectively. Various tire pressure variations are simulated in CarSim to exhibit the influences of tire inflation pressure, including all four tires at same and different pressures. The unbiased estimation results and compensation effects of the DYC strategy are also verified through simulations. A vehicle dynamics and lateral motion stability index, and driver's steering workload are proposed to quantify the influence of tire pressure variations and distributions. Analyses on the simulation results indicate that: pressure reduction on a front tire and rear tire induces a large steering angle and a large vehicle sideslip angle, respectively; all-tire inflation pressure decrease will increase driver's steering workload. The proposed DYC strategy can achieve satisfactory compensation effects through significantly decreasing the vehicle sideslip angle, steering wheel angle and reducing driver's steering workload by up to 90%.