With the development of vehicle-to-vehicle (V2V) communication, it is possible to share information among multiple vehicles. However, the existing research on automated lane changes concentrates only on the single-vehicle lane change with self-detective information. Cooperative lane changes are still a new area with more complicated scenarios and can improve safety and lane-change efficiency. Therefore, a multi-vehicle cooperative automated lane-change maneuver based on V2V communication for scenarios of eight vehicles on three lanes was proposed. In these scenarios, same-direction and intersectant-direction cooperative lane changes were defined. The vehicle that made the cooperative decision obtained the information of surrounding vehicles that were used to cooperatively plan the trajectories, which was called cooperative trajectory planning. The cooperative safety spacing model was proposed to guarantee and improve the safety of all vehicles, and it essentially developed constraints for the trajectory-planning task. Trajectory planning was treated as an optimization problem with the objective of maximizing safety, comfort, and lane-change efficiency under the constraints of vehicle dynamics and the aforementioned safety spacing model. Trajectory tracking based on a model predictive control method was designed to minimize tracking errors and control increments. Finally, to verify the validity of the proposed maneuver, an integrated simulation platform combining MATLAB/Simulink with CarSim was established. Moreover, a hardware-in-the-loop test bench was performed for further verification. The results indicated that the proposed multi-vehicle cooperative automated lane-change maneuver can achieve lane changes of multiple vehicles and increase lane-change efficiency while guaranteeing safety and comfort.

Battery is the key technology to the development of electric vehicles, and most battery models are based on the electric vehicle simulation. In order to accurately study the performance of LiFePO4 batteries, an improved equivalent circuit model was established by analyzing the dynamic characteristics and contrasting different-order models of the battery. Compared to the traditional model, the impact of hysteresis voltage was considered, and the third-order resistance–capacitance (RC) network was introduced to better simulate internal battery polarization. The electromotive force, resistance, capacitance and other parameters were calibrated through battery charge and discharge experiments. This model was built by using Modelica, a modeling language for object-oriented multi-domain physical systems. MWorks was used to implement the cycle conditions and vehicle simulation. The results show that the third-order RC battery model with hysteretic voltage well reflects the dynamics of a LiFePO4 battery. The difference between the simulated and measured voltages is small, with a maximum error of 1.78%, average error of 0.23%. The validity and feasibility of the model are verified. It can be used in unified modeling and simulation of subsequent multi-domain systems of electric vehicles.

The Vold–Kalman (VK) order tracking filter plays a vital role in the order analysis of noise in various fields. However, owing to the limited accuracy of double-precision floating-point data type, the order of the filter cannot be too high. This problem of accuracy makes it impossible for the filter to use a smaller bandwidth, meaning that the extracted order signal has greater noise. In this paper, the Python mpmath arbitrary-precision floating-point arithmetic library is used to implement a high-order VK filter. Based on this library, a filter with arbitrary bandwidth and arbitrary difference order can be implemented whenever necessary. Using the proposed algorithm, a narrower transition band and a flatter passband can be obtained, a good filtering effect can still be obtained when the sampling rate of the speed signal is far lower than that of the measured signal, and it is possible to extract narrowband signals from signals with large bandwidth. Test cases adopted in this paper show that the proposed algorithm has better filtering effect, better frequency selectivity, and stronger anti-interference ability compared with double-precision data type algorithm.

In the automotive and transport industry, braking noise and vibrations are persisting issues and difficult to control. Automotive engineers and researchers are putting considerable effort into overcoming these problems, and significant breakthroughs have been made in this area. In this study, M-shaped grooves were bionically designed and manufactured on the frictional surfaces of four automotive brake discs using a laser machine. Various tests were conducted to characterize the physical and mechanical performance of the modified discs along with their noise and vibration responses. The experimental results demonstrate that discs with laser-machined grooved surfaces have better surface hardness and residual stress reduction than discs with un-grooved surfaces. Significant improvement in the braking performance was observed in terms of disc thickness variation, friction and wear, noise, and vibration reduction. It is concluded that the reduction in braking noise and vibrations is mainly caused by the reduction in the coefficient of friction and wear, increase in damping ratio, and improvement of disc thickness variation of the brake disc by laser surface grooving.

A version of an electric vehicle was developed and designed for the US market on the basis of the required domestic body structure. When compared with the original car, the new car body design leads to two major technical difficulties. First, the installation of high-voltage components such as the battery pack and other new energy sources increases the vehicle weight and occupies a great deal of its structural space; this limits the impact paths and the use of traditional structural designs, which greatly increases the design difficulty. Second, the USA, as an advanced automobile-using country, has well-developed laws and regulations for collision standards, vehicle operating conditions and evaluation standards. Using a combination of butterfly diagram analysis, bending moment management, section forces and other computer-aided simulation and analysis techniques, this paper presents a body structure design that can achieve a “GOOD” evaluation under the US Insurance Institute for Highway Safety (IIHS) side impact body structure conditions by optimizing the force transfer path, the B-pillar deformation mode and the threshold support structure. The threshold support structure supports realization of the “GOOD” rating for IIHS side impact and helps the body to meet the crash requirements of the Federal Motor Vehicle Safety Standard FMVSS214 and the US New Car Assessment Program (NCAP) requirements for side impact at 32 km/h and 75° angular pole impact.

The performance of electric vehicles is affected by the shift quality of multi-gear transmission. The realization of dual-target tracking control requires the transmission control unit (TCU) to accurately measure and process the input signals of the gear-shifting control system and precisely control the drive motor torque and the position of shift motors. An electric-vehicle-dedicated TCU was designed to meet the above design requirements. Its function modules included a single-chip control circuit, shift position signal sampling circuit, signal conditioning circuit of the rotational speed and angle, controller area network communication circuit, and shift motor drive circuit. A hardware-in-the-loop simulation test system showed that the TCU design scheme met measurement accuracy requirements and coordinated the actions of the shift actuator and motor control unit to achieve fast and smooth shifting before the road test. The power interruption time of the shifting process was within 350 ms. The reliability of the TCU design was further verified in a 150,000-km vehicle road test.

Electric drive systems for new energy cars are complex systems that should have multivariate, strong coupling, and nonlinear characteristics and should also involve the multiphysics field. The singular simulation software used at present in the modeling of electric drive systems cannot simulate the influences of all the physics fields on the operating system. The co-simulation model used in this paper was based on a specific type of car. The motor control algorithm model was built in MATLAB/Simulink, the electromagnetic finite element model of the motor was built in ANSYS EM-Maxwell, and the motor controller hardware circuit was built in ANSYS EM-Simplorer. To make real-time connections among these software platforms, a multi-software co-simulation platform was built, and the co-simulation platform’s simulation results were input into STAR CCM+ software to enable finite element modeling of the motor and running of thermal analysis. When compared with the electric drive system model built using single Simulink software, the simulation results from this co-simulation platform were more realistic and were shown to be closer to reality when the dynamic characteristics of the electric drive system’s power semiconductor switching devices and the motor’s electromagnetic characteristics were considered. Finally, by benchmarking the multiphysics field co-simulation platform simulation results using dyno bench test results, the validity of the co-simulation platform was verified and the development of the multiphysics field co-simulation of the basic electric drive system was complete.