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This paper proposed a novel fault-tolerant control method based on control allocation via dynamic constrained optimization for electric vehicles with XBW systems. The total vehicle control command is first derived based on interpretation on driver's intention as a set of desired vehicle body forces, which is further dynamically distributed to the control command of each actuator among vehicle four corners. A dynamic constrained optimization method is proposed with the cost function set to be a linear combination of multiple control objectives, such that the control allocation problem is transformed into a linear programming formulation. An analytical yet explicit solution is then derived, which not only provides a systematic approach in handling the actuation faults, but also is efficient and real-time feasible for in-vehicle implementation. The simulation results show that the proposed method is valid and effective in maintaining vehicle operation as expected even with faults.

Power management of a hybrid energy storage system (HESS) with battery and supercapacitor(SC) is of critical importance for electric vehicles to achieve good driving performance, long traveling range and high energy efficiency. Due to the great differences in dynamic characteristics between battery and supercapacitor, and the complexity of a HESS, proper power management strategy between battery and supercapacitor remains to be challenging. The proposed research in this paper is to develop a power-balance and wavelet-transform based strategy for power distribution in a way such that each device can be utilized optimally. The transient dynamics is first decoupled via wavelet-transform algorithm while the power-balance algorithm is employed to improve system robustness based on the desired velocity-SOC relationship and a fuzzy logical controller. Finally some simulations have been conducted with results shown that the proposed strategy is valid and effective.

Electric vehicle (EV) has been regarded as not only an effective solution for environmental issues but also a more controllable and responsible device to driving forces with electric motors and precise torque measurement. For electric vehicle equipped with four in-wheel motors, its tire longitudinal forces can be generated independently and individually with fully utilized tire adhesion at each corner. This type of the electric vehicles has a distributed drive system, and often regarded as an over-actuated system since the number of actuators in general exceeds the control variables. Control allocation (CA) is often considered as an effective means for the control of over-actuated systems. The in-vehicle network technology has been one of the major enablers for the distributed drive systems. The vehicle studied in this research has an electrohydraulic brake system (EHB) on front axle, while an electromechanical brake system (EMB) on rear axle.

As a novel assist actuator of brake system, the electromechanical brake (EMB) booster has played a significant role in the battery electric vehicles and automatic driving vehicles. It has advantages of independent to vacuum source, active braking, and tuning pedal feeling compared with conventional vacuum brake booster. In this article, a novel EMB booster system is proposed, which is consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction by gears and ball screw, a servo body, and a reaction disk. Together with the hydraulic control unit, it has two working modes: active braking for automatic drive and passive braking for driver intervention. The structure and work principle of the electric brake booster system is first introduced. The precise control from pedal force to hydraulic pressure is the key for such a power-assisted brake actuator. We translate the control problem of force feedback control to position tracking control.

The evaluation of electric vehicle electric/electronic-architectures (e/e-architectures) is the main topic of this paper. The electric vehicle is chosen as an example system, as it reflects the typical challenges of modern vehicle e/e-architecture development. The development of modern automotive technology also presents another important trend - vehicle electrification. New electric and electronic devices are developed and required in the automotive industry and control commands are exchanged by electric and electronic ones. The energy storage systems (ESS) properly reflect the above two aspects. The energy storage device also takes care of the peak loads, the high load dynamics, and it utilizes the braking energy in order to increase the efficiency. In this work a Li-ion battery and an ultracapacitor both are considered as energy storage devices.

This paper presents a unified novel function-based brake control architecture, which is designed based on a top-down approach with functional abstraction and modularity. The proposed control architecture includes a commands interpreter module, including a driver commands interpreter to interpret driver intention, and a command integration to integrate the driver intention with senor-guided active driving command, state observers for estimation of vehicle sideslip, vehicle speed, tire lateral and longitudinal slips, tire-road friction coefficient, etc., a commands integrated control allocation module which aims to generate braking force and yaw moment commands and provide optimal distribution among four wheels without body instability and wheel lock or slip, a low-level control module includes four wheel pressure control modules, each of which regulates wheel pressure by fast and accurate tracking commanded wheel pressure.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

The Electromechanical Brake Booster system (EMBB) integrates active braking and energy recovery and becomes a novel brake-by-wire solution that substitutes the vacuum booster. While the intelligent unmanned vehicle is in unstable state, the EMBB can improve the vehicle yaw stability more quickly and safely. In this paper, a new type of integrated EMBB has been designed, which mainly includes two parts: servo motor unit and hydraulic control unit. Aiming at the dynamic instability problem of intelligent unmanned vehicle, a three-layer vehicle yaw stability control structure including decision layer, distribution layer and execution layer is proposed based on integrated EMBB. Firstly, the decision layer calculates the ideal yaw rate and the side slip angle of the vehicle with the classic 2DOF vehicle dynamics model. The boundary of the stable region is determined by the phase plane method and the additional yaw moment is determined by the feedback PI control algorithm.

Since road electric vehicles typically require a significantly variable and random load power demand in response to traffic conditions, such as frequent sequences of acceleration and deceleration and uphill followed by downhill runs. In this context, the energy management system of electric vehicle must ensure an effective power distribution between battery and supercapacitor to satisfy load demand. In this paper, the power management control strategy of hybrid energy storage system is developed by introducing terrain information to optimize system efficiency and battery lifetime. In this presented research, we aim at developing a power management control strategy considering the influence of the terrain information on system efficiency and battery lifetime.

With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoon technology will be more prosperous in the future. In this article, the cooperative vehicle platoon method on the public road is represented. The method’s architecture is mainly composed of the following parts: decision-making, path planning and control command generation. The decision-making uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change requirements are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of control command generation module, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem.

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level.

Autonomous driving technology, as the product of the fifth stage of the information technology revolution, is of great significance for improving urban traffic and environmentally friendly sustainable development. Autonomous driving can be divided into three main modules. The input of the decision module is the perception information from the perception module and the output of the control strategy to the control module. The deep reinforcement learning method proposes an end-to-end decision-making system design scheme. This paper adopts the Deep Deterministic Policy Gradient Algorithm (DDPG) that incorporates the Priority Experience Playback (PER) method. The framework of the algorithm is based on the actor-critic network structure model. The model takes the continuously acquired perception information as input and the continuous control of the vehicle as output.

Subframe is an important part of automobile chassis, which is connected with body, suspension control arm, powertrain mount, etc. The dynamic stiffness value of the connection point is an important performance index of the subframe, which affects the vibration of the vehicle body. This paper introduces the basic concept and related theory of dynamic stiffness, derives the theoretical formula of dynamic stiffness, and analyzes the frequency response of the key points of the subframe. In view of the fact that the dynamic stiffness of the subframe of a certain vehicle model is not up to the standard at some connection points, the dynamic stiffness CAE simulation analysis is carried out to determine the frequency range of insufficient dynamic stiffness and the connection points that need to be optimized.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.

The limitation of drivers' attention and perception may bring collision dangers, Autonomous Emergency Braking (AEB) can help drivers to avoid the potential collisions through active braking. Since the positive effect of it, motor corporations have begun to equip their vehicles with the system, and regulatory agencies in various countries have introduced test standards. At this stage, the actuator of AEB usually adopts Electronic Stability Program (ESP), but it poor performance of continuous working period and active pressure built-up for all wheels limits its implements. Electromechanical brake booster can realize power assisted brake without relying on the vacuum source and a variety of specific power curves. Moreover it can achieve the active braking with a rapid response, which make it can fulfill requirements of automotive electric and intelligent development.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

Adaptive Cruise Control (ACC) includes three modes: cruise control, car following control, and autonomous emergency braking. Among them, the car following control mode is mainly used to manage the speed and vehicle spacing approach the preceding vehicle within the range of smooth acceleration changes. In addition, although the motion information signal of the preceding vehicle can be collected by auxiliary equipment, it is still a random variable and normally regarded as a disturbance to affect the performance of vehicle controller. Therefore, this paper proposed an ACC strategy considering the disturbance of the preceding vehicle and multi-objective optimization.

This paper first presents an algorithm to detect tire blowout based on wheel speed sensor signals, which either reduces the cost for a TPMS or provides a backup in case it fails, and a tire blowout model considering different tire pressure is also built based on the UniTire model. The vehicle dynamic model uses commercial software CarSim. After detecting tire blowout, the active braking control, based on a 2DOF reference model, determines an optimal correcting yaw moment and the braking forces that slow down and stop the vehicle, based on a linear quadratic regulator. Then the braking force commands are further translated into target pressure command for each wheel cylinder to ensure the target braking forces are generated. Some simulations are conducted to verify the active control strategy.

Brake-by-wire (BBW) system has drawn a great attention in recent years as driven by rapidly increasing demands on both active brake controls for intelligent vehicles and regenerative braking controls for electric vehicles. However, unlike conversional brake systems, the reliability of the brake-by-wire systems remains to be challenging due to its lack of physical connection in case of system failure. There are various causes for the failure of a BBW system, such as failure of brake controller, loss of sensor signals, failure of communication or even power supply, to name a few. This paper presents a fault-tolerant control under novel control architecture. The proposed control architecture includes a driver command interpreter module, a command integration module, a control allocation module, a fault diagnosis module and state observers. The fault-tolerant control is designed based on a quadratic optimal control method with consideration of actuator constraints.