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This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

The driving range of the electric vehicle (EV) greatly restricts the development of EVs. The vehicles waste plenty of energy on account of automobiles frequently braking under the city cycle. The regenerative braking system can convert the braking kinetic energy into the electrical energy and then returns to the battery, so the energy regeneration could prolong theregenerative braking system. According to the characteristics of robustness in regenerative braking, both regenerative braking and friction braking based on fuzzy logic are assigned after the front-rear axle’s braking force is distributed to meet the requirement of braking security and high-efficient braking energy regeneration. Among the model, the vehicle model and the mechanical braking system is built by the CRUISE software. The paper applies the MATLAB/SIMULINK to establish a regenerative braking model, and then selects the UEDC city cycle for model co-simulation analysis.

This paper presents a fault-tolerant control (FTC) algorithm for four-wheel independently driven and steered (4WID/4WIS) electric vehicle. The Extended Kalman Filter (EKF) algorithm is utilized in the fault detection (FD) module so as to estimate the in-wheel motor parameters, which could detect parameter variations caused by in-wheel motor fault. A motion controller based on sliding mode control (SMC) is able to compute the generalized forces/moments to follow the desired vehicle motion. By considering the tire adhesive limits, a reconfigurable control allocator optimally distributes the generalized forces/moments among healthy actuators so as to minimize the tire workloads once the actuator fault is detected. An actuator controller calculates the driving torques of the in-wheel motors and steering angles of the wheels in order to finally achieve the distributed tire forces. If one or more in-wheel motors lose efficacy, the FD module diagnoses the actuator failures first.

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.

A full drive-by-wire electric vehicle, named Urban Future Electric Vehicle (UFEV) is developed, where the four wheels' traction and braking torques, four wheels' steering angles, and four active suspensions (in the future) are controlled independently. It is an ideal platform to realize the optimal vehicle dynamics, the marginal-stability and the energy-efficient control, it is also a platform for studying the advanced chassis control methods and their applications. A centralized control system of hierarchical structure for UFEV is proposed, which consist of Sensor Layer, Identification and Estimation Layer, Objective Control Layer, Forces and Motion Distribution Layer, Executive Layer. In the Identification and Estimation Layer, identification model is established by utilizing neural network algorithms to identify the driver characteristics. Vehicle state estimation and road identification of UFEV based on EKF and Fuzzy Logic Control methods is also conducted in this layer.

Omni-directional electric vehicle built by our research group is an advanced electric vehicle whose four wheels can drive, steer and brake independently. The vehicle chassis system is composed of four in-wheel motors, four independent steer motors and electromagnetic brake system, and its control system is divided into logical control layer and underlying execution layer. The information exchange between these two layers is implemented by CAN bus. In this paper, the traction control logic for Omni-directional electric vehicle is developed. The study mainly involves two aspects: the vehicle states estimation and the traction control logic design. The vehicle states, including vehicle longitudinal velocity, lateral speed, side slip angle and yaw rate, etc, are estimated based on Extended Kalman Estimation and multiple degrees of freedom vehicle model.