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A fault detection and diagnosis (FDD) algorithm of 4WID/4WIS Electric Vehicles has been proposed in this study aiming to find the actuator faults. The 4WID/4WIS EV is one of the promising architectures for electric vehicle designs which is driven independently by four in-wheel motors and steered independently by four steering motors. The 4WID/4WIS EVs have many potential abilities in advanced vehicle control technologies, but diagnosis and accommodation of the actuator faults becomes a significant issue. The proposed FDD approach is an important part of the active fault tolerant control (AFTC) algorithm. The main objective of the FDD approach is to monitor vehicle states, find the faulty driving motor and then feedback fault information to the controller which would adopt appropriate control laws to accommodate the post-fault vehicle control system.

A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle.

An integrated control strategy for vehicle active suspension system which combines linear quadratic optimum control law with fuzzy control algorithm is designed to improve both ride and handling. The performance of this control strategy is then examined and assessed in an open-loop J-turn driving scenario on a random-rough road by means of computer simulation. Comparisons to a passive suspension system in terms of vehicle sprung mass vertical acceleration, body roll angle and yaw rate is conducted. Simulation results indicate that the integrated control strategy proposed in this paper could effectively enhance vehicle ride comfort meanwhile benefit handling quality and driving safety.

Impact torque will be generated on the steering wheel when one tire suddenly blows out on high way, which may cause driver's psychological stress and result in driver's certain misoperations on the car. In this paper, the model of tire blowout vehicle was established; the tire blowout was detected based on the change rate of tire pressure, meanwhile, the rack force caused by tire blowout was estimated through a reduce observer; finally the compensation current was figured out to reduce the impact torque on the steering wheel. Results of simulation tests showed that the control strategy proposed in this paper can effectively reduce the impact torque on the steering wheel and reduce the driver's discomfort caused by tire blowout.

The EPS motor will be over-heated if large current lasts for a long time, which will decline the performance of EPS motor and even lead to irreparable damage. So the over-temperature protection control should be conducted in order to protect the components of EPS system, especially the durability of EPS motor. In this paper, the motor temperature was estimated according to the environmental temperature and the current of motor armature, and then the EPS assist current was limited based on the estimated temperature of motor to ensure that the EPS motor had a good working condition. So the over-temperature protection control for motor can be realized without increasing the EPS system components. Finally the control strategy for over-temperature protection was conducted in a vehicle with EPS system and its performance was verified.

For the vehicles equipped with Electric Power Steering (EPS) system, the friction and damping effect brought by assisted motor and worm gear mechanism influence the return ability and handing stability. In order to eliminate the impacts, it is necessary to add return-to-center control in EPS control strategy. This paper proposes a practical active return-to-center control strategy with steering wheel angle signals based on return state identification. In the strategy, the return state of the steering system is identified quickly according to the two signals steering wheel angle velocity and steering wheel torque. Only under return state, a double closed-loop PID control strategy is carried out to calculate a compensation current to improve the return ability. For validating the proposed strategy, a fine EPS model including BLDC assisted motor is built based on carsim and simulink co-simulation platform.

Four-wheel independent drive EV is driven by four brushless DC motors which are embedded in the wheel hubs. It enables each wheel's driving torque to be controlled independently. Due to the motors' torque and rotational speed easily measured, as well as the features of fast response and precise control, the EV enjoys obvious advantages over traditional vehicles in acceleration slip regulation. In this paper a novel acceleration slip regulation strategy for four-wheel independent drive EV is studied. The strategy includes a road identification module for the peak value of road adhesion coefficient and a slip regulation logic based on PID algorithm. Through comparing the current wheel slip ratio and the utilized adhesion coefficient with the typical roads' value, the identification module adopts the fuzzy control algorithm to recognize the similarity between the current road and the typical roads. Utilizing the similarity we can calculate the optimal slip ratio of the current road.

Nowadays active collision avoidance has become a major focus of research, and a variety of detection and tracking methods of obstacles in front of host vehicle have been applied to it. In this paper, laser radars are chosen as sensors to obtain relevant information, after which an algorithm used to detect and track vehicles in front is provided. The algorithm determines radar's ROI (Region of Interest), then uses a laser radar to scan the 2D space so as to obtain the information of the position and the distance of the targets which could be determined as obstacles. The information obtained will be filtered and then be transformed into cartesian coordinates, after that the coordinate point will be clustered so that the profile of the targets can be determined. A threshold will be set to judge whether the targets are obstacles or not. Last Kalman filter will be used for target tracking. To verify the presented algorithm, related experiments have been designed and carried out.