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Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories.

With rapid development of the automobile industry and the growing maturity of the automotive electronic technologies, the distributed-traction electric vehicle with four-wheel-distributed steering/braking/traction systems is regarded as an important development direction. With its unique chassis structure, it is the ideal benchmark platform used to evaluate active safety systems. The distributed-traction electric vehicle with four-wheel-distributed steering system is essentially full drive-by-wire vehicle. With its flexible chassis layout and high control degrees-of-freedom, the full drive-by-wire electric vehicle acted as a kind of redundant system is an ideal platform for the research of integrated control. In this treatise, the longitudinal dynamics of the electric vehicle as well as its lateral and yaw motions are controlled simultaneously.

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.

This paper presents an adaptive observer-based approach for the combined state estimation and active fault detection and isolation (FDI) of the individual-wheel-drive (IWD) vehicles. A 3-DOF vehicle model coupled with the Highway Safety Research Institute (HSRI) tire model is established and used as the observation model. Based on this model, the dual unscented Kalman filter (DUKF) technique is employed for the observer design to give fusion results of the interdependent state and parameter variables, which undergo nonlinear transformations, with the minimum square errors. Effectiveness of the proposed algorithm is examined and validated through co-simulation between MATLAB/Simulink and CarSim. The results demonstrate that the DUKF-based observer effectively filters the sensor signals, accurately obtains the longitudinal and lateral velocities, explicitly isolates the faulty wheel(s) and accurately estimates the actual torque(s) even with the presence of noise.

The passive fault-tolerant approach for four-wheel independently driven and steered (4WID/4WIS) electric vehicles has been investigated in this study. An adaptive control based passive fault-tolerant controller is designed to improve vehicle safety, performance and maneuverability when an actuator fault happens. The proposed fault tolerant control method consists of the following three parts: 1) a fault detection and diagnosis (FDD) module that monitors vehicle driving condition, detects and diagnoses actuator failures with the inequality constraints; 2) a motion controller that computes the generalized forces/moments to track the desired vehicle motion using Model Predictive Control (MPC); 3) a reconfigurable control allocator that redistributes the generalized forces/moments to four wheels with equality constrained optimization.

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.

The hybrid electric city bus, which consists of the electric motor and battery, is obviously different from the traditional buses. This paper focuses on optimizing the characteristics of the automatic mechanical transmission in hybrid electric city bus and does the following studies: firstly, in order to reduce the fuel consumption, the transmission ratio and some structural parameters are optimized with CRUISE software; secondly, the volume and weight of the transmission structure is reduced and optimized by numerical optimization approach, with the limitation of the structural reliability.

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.

The passive fault-tolerant performance of the integrated vehicle controller (IVC) applied on 4WID/4WIS Electric Vehicles has been investigated in this study. The 4WID/4WIS EV is driven independently by four in-wheel motors and steered independently by four steering motors. Thanks to increased control flexibility of the over-actuated architecture, Control Allocation (CA) can be applied to control the 4WID/4WIS EVs so as to improve the handling and stability. Another benefit of the over-actuated architecture is that the 4WID/4WIS Electric Vehicle has sufficient redundant actuators to fight against the safety critical situation when one or more actuators fail.

A fault tolerant control (FTC) approach based on reconfigurable control allocation for four-wheel independently driven and steered (4WID/4WIS) electric vehicles against driving motor failures is proposed in order to improve vehicle safety, performance and maneuverability after the driving motor failures. The proposed fault tolerant control method consists of the following three parts: 1) a fault detection and diagnosis (FDD) module that monitors vehicle driving condition, detects and diagnoses actuator failures; 2) a motion controller that computes the generalized forces/moments to track the desired vehicle motion using model predictive control method; 3) a reconfigurable control allocator that optimally distributes the generalized forces/moments to four wheels aiming at minimizing the total tire usage. The FTC approach is based on the reconfigurable control allocation which reallocates the generalized forces/moments among healthy actuators once the actuator failures is detected.

The paper describes an algorithm, which estimates the mass of large buses and axle load distribution using pedal position, wheel speed and the wheel cylinder pressure sensors. This algorithm is allowed to achieve the purpose without additional sensors by using the rotational speed sensors from ABS system and air pressure sensors in brake cylinders form ESP system. The axle load distribution algorithm mainly consists of three steps. Firstly, deceleration of the bus is estimated and then the mass of the bus is estimated. After that, the position of the mass centre is estimated. Taking account of the tire nonlinear characteristics under longitudinal forces and vertical forces, mass estimation, deceleration and the position of the mass centre of buses is corrected by the coefficient, which is determined by the wheel cylinder pressure, the wheel speed and mass estimation.