The analysis and control of powertrain systems of electric vehicle, which is an important type of new energy vehicle, have been the focus of extensive research, but determining the motor modeling parameters remains a problem. A method of parameter determination for brushless DC motor modeling based on vehicle power performance was developed in this study. The power and torque of the driving motor of an electric vehicle were obtained by using the dynamic equation of the electric vehicle to satisfy the requirements of power performance. The ranges of the back electromotive force coefficient and the winding inductance were derived from the voltage and dynamic equations of brushless DC motor, which were deduced from the expected power and torque of the motor. The modeling parameters were then determined on the basis of the influence of power source voltage, back electromotive force coefficient, winding inductance, and winding resistance on vehicle power performance. A hardware-in-loop simulation of vehicle power performance was performed to verify the effectiveness of the proposed method. Results indicate that the maximum vehicle velocity is 172 km/h, and the acceleration time of 100 km/h is 13 s, which reveal that the motor modeling parameters obtained with the method satisfy relevant requirements.

A coordinated torque distribution method (CTDM) combined with balanced torque vectoring distribution method (BTVDM) and differential braking distribution method (DBDM) is proposed to enhance vehicle lateral stability and improve ride comfort. The phase plane of sideslip angle and its angular velocity with three regions are used to evaluate vehicle lateral stability. The models of region boundary parameters considering vehicle velocity, steering angle, and road adhesion coefficient are established on the basis of the nonlinear partial least squares regression model (NLPLSRM). Vehicle lateral stability is dynamically evaluated on the basis of the vehicle state position in the phase plane. The distribution ratio of direct yaw moment (DYM) realised by DBDM and BTVDM is determined in accordance with the instability risk of vehicle lateral stability. Compared with BTVDM and DBDM, the proposed CTDM can correctly realise the accuracy of DYM and reduce vehicle velocity, which is beneficial to improving vehicle lateral stability. Numerical simulation results show that the proposed CTDM substantially improves vehicle lateral stability and reduces control intervention for ride comfort.

Operational transfer path analysis is applied in this study to identify the vibration source and its critical transfer path. A simple analytical five-degrees-of-freedom mechanical isolation system is first taken as an example to illustrate the analysis flow and to validate the accuracy of operational transfer path analysis. The acceleration amplitude spectrum of the receiver is used to prove the accuracy, and the path contribution of each path is used to identify the critical path. Operational transfer path analysis is then applied to the cab mount system of a heavy commercial vehicle to identify the vibration source and its critical transfer path. The vibration energy propagation capabilities from the four cab mounts to the driver’s seat are analyzed by operational transfer path analysis with the path contribution analysis, and the maximum vibration source is identified by the path operation contribution analysis. The analysis and evaluation method of the operational transfer path analysis introduced in this study can provide a research foundation and reference for vibration or noise source identification in mechanical systems.