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Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.

The permanent-magnet DC motor, which is directly connected to the hydraulic pump, is a significant component of hydraulic control unit (HCU) in an anti-lock braking system (ABS). It drives the pump to dump the brake fluid from the low-pressure accumulator back to master cylinder and makes sure the pressure decreases of wheel cylinder in ABS control. Obviously, the motor should run fast enough to provide sufficient power and prevent the low-pressure accumulator from fully charging. However, the pump don't need always run at full speed for the consideration of energy conservation and noise reduction. Therefore, it is necessary to accurately regulate the speed of the DC motor in order to improve quality of ABS control. In this paper, an accurate speed control algorithm was developed for the permanent-magnet DC motor of the ABS to implement the performance of the system, reduce the noise and save the energy in the meanwhile.

This paper proposes a novel allocation-based control method for four-wheel independently actuated electric vehicles. In the proposed method, both actuator dynamics and input/output constraints are fully taken into consideration in the control design. First, the actuators are modeled as first-order dynamic systems with delay. Then, the control allocation is formulated as an optimization problem, with the primary objective of minimizing errors between the actual and desired control outputs. Other objectives include minimizing the power consumption and the slew rate of the actuator outputs. As a result, this leads to frequency-dependent allocation that reflects the bandwidth of each actuator. To solve the optimization problem, an efficient numerical algorithm is employed. Finally the proposed control allocation method is implemented to control a four-wheel independently actuated electric vehicle.

To cope with the conflict requirements between the stability and handling performance, and the high-order and complex vehicle-trailer plant, a model tracking method is proposed. With this approach, a feedback control is designed to “decouple” the vehicle and the trailer plant, such that each tracks a well-defined second-order reference model independently yet coordinately. A feedforward control is designed to maintain its system steady-state performance. As a result, the proposed approach not only improves the system transient responses, but also its steady-state performance. This approach further yields a simple yet analytical control derivation that provides more insight to the system dynamics.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

In this article, the regenerative braking system is designed, which can realize the torque allocation between electric braking and hydraulic braking, where the cost function designed in this article considers factors of braking torque following effect, energy regenerative power, and hydraulic braking consumed power. In addition, a complete anti-lock braking system (ABS) is designed, which is based on regenerative braking. With the optimal slip ratio as control target, target wheel speed, control wheel speed, braking torque control strategy, and enable/disenable control logic of ABS are determined. By MATLAB/Simulink-DYNA4 co-simulation and real vehicle test, the feasibility and applicability of ABS based on regenerative braking are verified, under the condition of small severity of braking.

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.

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.

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.

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.

Driver individualities is crucial for the development of the Advanced Driver Assistant System (ADAS). Due to the mechanism that specific driving operation action of individual driver under typical conditions is convergent and differentiated, a novel driver individualities recognition method is constructed in this paper using random forest model. A driver behavior data acquisition system was built using dSPACE real-time simulation platform. Based on that, the driving data of the tested drivers were collected in real time. Then, we extracted main driving data by principal component analysis method. The fuzzy clustering analysis was carried out on the main driving data, and the fuzzy matrix was constructed according to the intrinsic attribute of the driving data. The drivers’ driving data were divided into multiple clusters.

This paper reports an effort to improve plan of vehicle trajectory using an approach with rapidly-exploring random trees (RRT), which has been widely adopted in the prior art for complex and dynamic traffic environment. Design and implement of an integrated threat assessment is presented that evaluates threats of the trajectory. A node and trajectory evaluation index was introduced into the proposed RRT algorithm to connect an appropriate node and select the best trajectory. The contribution of this paper is on the threat assessment that takes into account not only obstacle avoidance but also stability. The simulation is conducted and the results show that the proposed method works as expected and is valid and effective.

This paper proposes a novel modeling and simulation environment developed under Matlab/Simulink with friendly and intuitive graphic user interfaces, aimed to enable math-based virtual development and test of intelligent driving systems. Six typical driving maneuvers are first proposed, which are further abstracted into two atomic sub-maneuvers: lane following and lane change, as any maneuvers can be the combinations of these two. A generic trajectory planning and path tracking control algorithm are developed to deal with the generality and commonality of the lane change function with optimization among safety, comfort and efficiency in performing the lane change maneuver. Some typical simulations are conducted with results demonstrating the practical usefulness, efficiency and convenience in using this proposed tool.

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.

This paper presents a power assisted braking control based on a novel mechatronic booster system. A brake pedal feel control unit is first discussed which includes a pedal emulator with an angular sensor to detect driver’s pedal travel, a signal processing module with a Kalman filter for sensor signal conditioning, and a driver braking intention detection and behavior recognition module based on the displacement and velocity of the pedal travel. A power assisted braking control is then presented as the core of the system which consists of controls on basic power assist, velocity compensation and friction compensation. The friction is estimated based on a generic algorithm offline. A motor controller is designed to provide the desired torque for the power assist. Finally, a novel mechatronic booster system is designed and built with an experimental platform set up with a widely adopted rapid prototype system using dSPACE products, such as MicroAutoBox, RapidPro, etc.

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.

A vehicle dynamics stability control system based on integrated-electro-hydraulic brake (I-EHB) system with hierarchical control architecture and nonlinear control method is designed to improve the vehicle dynamics stability under extreme conditions in this paper. The I-EHB system is a novel brake-by-wire system, and is suitable to the development demands of intelligent vehicle technology and new energy vehicle technology. Four inlet valves and four outlet valves are added to the layout of a conventional four-channel hydraulic control unit. A permanent-magnet synchronous motor (PMSM) provides a stabilized high-pressure source in the master cylinder, and the four-channel hydraulic control unit ensures that the pressures in each wheel cylinder can be modulated separately at a high precision. Besides, the functions of Anti-lock Braking System, Traction Control System and Regenerative Braking System, Autonomous Emergency Braking can be integrated in this brake-by-wire system.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

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.