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.

For current power supply systems in internal combustion engine (ICE) vehicles, there exists a lack of comprehensive monitoring and sufficient management. There is no effective fault diagnosis and fault-tolerant protection either. With increasing number of devices applied in ICE vehicles, power supply system of vehicle is in desperate need of improvement. In this study, a novel in-vehicle intelligent electric power supply network with comprehensive on-board fault diagnosis and real-time fault-tolerant protection is proposed. Firstly, the architecture of in-vehicle intelligent electric power supply network is established, making the power supply process of all devices smarter. Then, a hybrid-signal-based method is presented for on-board detection of multifarious faults, and over-temperature failure of wiring harness is detected based on the proposed real-time temperature evaluation model of wiring harness. Furthermore, a multi-level fault-tolerant protection method is proposed based on redundant design, to achieve power supply security and avoid false protection. Finally, tests are performed on the vehicle as well as test bench to verify the feasibility and reliability of the proposed power supply network. The results reveal that each device's power supply process is appropriately monitored, online detection of faults is successfully achieved, and the fault-tolerant method is able to realize remediation and protection in real time.

Vehicle active safety systems can benefit significantly from a priori knowledge of the road conditions. This paper proposes a real-time tyre–road friction estimation algorithm based on an unscented Kalman filter and mean-square-error-weighted fusion, using measurements related to the electric vehicle and in-wheel motors. Because the modules are mutually independent, the dimensionality of the matrix used in the filtering process can be reduced to shorten the computational period. The approach can also work more effectively in various conditions than those techniques which focus on a specific direction of the vehicle dynamics. First, a modified Dugoff et al. tyre model is considered to express the non-linear characteristics of the tyres. Then, to observe the longitudinal tyre forces and the lateral tyre forces, a parameter identification technique that utilizes the information from an inertial sensor and a gyroscope is introduced. An unscented Kalman filter rather than the traditional linear filter is adopted to obtain higher accuracy in the saturation region. After the relationship between the mean square error and the side-slip angle or the slip ratio is taken into account, the final identified value is set by combining the results from the longitudinal unscented Kalman filter and the lateral unscented Kalman filter in order to improve the accuracy and the robustness further. Experiments conducted on both a dry road and a slippery road verify that the method based on an unscented Kalman filter can identify different tyre–road friction levels.

In this paper, a resonance frequency-based tire–road friction coefficient (TRFC) estimation method is proposed by considering the dynamics performance of the in-wheel motor drive system under small slip ratio conditions. A frequency response function (FRF) is deduced for the drive system that is composed of a dynamic tire model and a simplified motor model. A linear relationship between the squared system resonance frequency and the TFRC is described with the FRF. Furthermore, the resonance frequency is identified by the Auto-Regressive eXogenous model using the information of the motor torque and the wheel speed, and the TRFC is estimated thereafter by a recursive least squares filter with the identified resonance frequency. Finally, the effectiveness of the proposed approach is demonstrated through simulations and experimental tests on different road surfaces.

To solve the problems with the existing active fault-tolerant control system, which does not consider the cooperative control of the drive system and steering system or accurately relies on the vehicle model when one or more motors fail, a multi-input and multi-output model-free adaptive active fault-tolerant control method for four-wheel independently driven electric vehicles is proposed. The method, which only uses the input/output data of the vehicle in the control system design, is based on a new dynamic linearization technique with a pseudo-partial derivative, aimed at solving the complex and nonlinear issues of the vehicle model. The desired control objectives can be achieved by the coordinated adaptive fault-tolerant control of the drive and steering systems under different failure conditions of the drive system. The error convergence and input-output boundedness of the control system are proven by means of stability analysis. Finally, simulations and further experiments are carried out to validate the effectiveness and real-time response of the fault-tolerant system in different driving scenarios. The results demonstrate that our proposed approach can maintain the longitudinal speed error (within 3%) and lateral stability, thereby improving the safety of the vehicles.

Intelligent connected vehicles (ICVs) technologies will bring significant changes to future transportation, and urban intersections will be an important scenario for the application of ICVs. There exists one significant challenge to address for the control of ICVs in unsignalized, multi-intersection road networks, that is, how to realize the comprehensive optimization of traffic efficiency and energy saving. To solve this problem, the distributed and hierarchical optimal control architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the distributed model predictive control (DMPC) method is utilized for distributed optimization control of multi-intersection road network systems, to achieve optimization in terms of traffic efficiency. For the vehicle control layer, based on the reference speed optimized from the cloud decision layer, the DMPC method is further utilized for distributed optimal control of each vehicle platoon, to achieve optimization in terms of energy saving. Finally, the comparative simulation tests are carried out based on MATLAB and SUMO. The feasibility and effectiveness of the proposed method were verified, and the improvement of traffic efficiency and energy saving was achieved.