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An investigation into two new control strategies for the vehicle Anti-lock Braking System (ABS) are made for a possible replacement of current non-optimal slip control methods. This paper applies two techniques in order to maximize the braking force without any wheel locking. The first considers the power dissipated by the brake actuator. This power method does not use slip to construct its reference signal for control. A heuristic approach is taken with this algorithm where one searches for the maximum power dissipated. This can open up easier implementation of regenerative braking concurrently with ABS on an electro-hydraulic braking system. Parameter scheduling is explored in this algorithm. The second algorithm employs the use of perturbation based Extremum Seeking Control (ESC) to provide a reference slip and a Youla controller in a negative feedback loop.

Optimizing/maximizing regen braking in a hybrid electric vehicle (HEV) is one of the key features for increasing fuel economy. However, it is known [1] that maximizing regen braking by braking the rear axle on a low friction surface results in compromising vehicle stability even in a vehicle which is equipped with an ESP (Enhanced Stability Program). In this paper, we develop a strategy to maximize regen braking without compromising vehicle stability. A yaw rate stability control system is designed for a hybrid electric vehicle with electric rear axle drive (ERAD) and a “hang on” center coupling device which can couple the front and rear axles for AWD capabilities. Nonlinear models of the ERAD drivetrain and vehicle are presented using bond graphs while a high fidelity model of the center coupling device is used for simulation.

In practical applications, failures in the components of the control system can lead to improper, or even unstable, operation of the control loop. These failures can be associated with the process (abrupt change in the process dynamics), the measuring and manipulating devices (sensors, actuators) or the controller itself. It is therefore desired to design control system capable of handling such events in the sense that stability is guaranteed and performance degradation is minimized. The proposed formulation of the reliable performance problem involves the simultaneous minimization of the performance index for all considered failure scenarios. Employing the fractional representation theory, the reliable performance problem is formulated as a quadratically constrained control problem. The solution to this problem is discussed in this paper and an illustrative example is presented.

The Variable Dynamic Testbed Vehicle (VDTV) concept has been proposed as a tool to evaluate collision avoidance systems and to perform driving-related human factors research. The goal of this study is to analytically investigate to what extent a VDTV with adjustable front and rear anti-roll bar stiffnesses, programmable damping rates, and four-wheel-steering can emulate the lateral dynamics of a broad range of passenger vehicles. Using a selected compact-sized automobile as a baseline, our study indicated this baseline vehicle can be controlled to emulate the lateral response characteristics (including the vehicle's understeer coefficient and the 90% lateral acceleration rise time in a J-turn maneuver) of a fleet of production vehicles, from low to high lateral acceleration conditions.

AN ENGINEERING evaluation of six automobile head-on collision experiments is presented for impact speeds ranging from 21 to 52 mph. An analysis of the relative collision performances of unit-body and frame-type construction is made. Anthropometric dummy subjects facilitate determination of force systems for restrained and unrestrained motorists, their dynamic and kinetic responses to impact, and the causative factors associated with motorist injury production.* The systems of instrumentation which enabled a comprehensive analysis to be made from an event lasting only 0.25 sec are briefly presented.

The postcollision motion starts immediately upon completion of a collision impact where the vehicles obtain new sets of velocities through an exchange of momentum. Similitude with model study and fullscale automobile experiments indicate that the post-collision trajectory is essentially a plane motion, governed by inertia and tire friction. Trajectories depend on many parameters (such as tire friction coefficient, front wheel steering angle, vehicle geometrics, and whether wheels are locked or free to rotate) but not on the vehicle weight. Theoretical computation of trajectories are compared with experiments.

A torque control policy for four-wheel drive road-going vehicles is developed, based on the use of a compact variable ratio unit (VRU) located at each wheel. Since the appropriate hardware is not yet available, a computer model is developed to examine what gear ratio range and frequency response might be required of the hardware to allow for improved performance and stability over current four-wheel drive systems. A comparison is then made to a front-wheel drive (FWD), rear-wheel drive (RWD) and four-wheel drive (4WD) to determine the effectiveness of the derived control policy.

Vehicle stability augmentation has been refined over many years, and currently there are commercial systems that control right/left braking and throttle to create vehicles that remain controlled when road conditions are very poor. These systems typically use yaw rate and lateral acceleration in their control philosophy. The tire/road friction coefficient, μ, has a significant role in vehicle longitudinal and lateral control, and there has been associated efforts to measure or estimate the road surface condition to provide additional information for the stability augmentation system. In this paper, a differential braking control strategy using yaw rate feedback, coupled with μ feedforward is introduced for a vehicle cornering on different μ roads. A nonlinear 4-wheel car model is developed. A desired yaw rate is calculated from the reference model based on the driver steering input.

This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events perceived by map and navigation information, machine vision and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while in real time considering preceding vehicles. A practical force balance relationship based on an electrified powertrain is explicitly utilized for building a cost function of the associated optimal control problem. The optimal inputs are parameterized on each computation node from a set of available deceleration profiles resulting from a deceleration time model which are configured by real-world test drivings.

The question of whether or not direct-hydrogen fuel cell systems in automotive applications should be used in load following or load leveled (battery hybrid) configurations is addressed. Both qualitative and quantitative analyses are performed to determine the potential strengths and weaknesses of each option. It is determined that the amount of energy that can be recovered through regenerative braking has a strong impact on the relative fuel economy of load following versus load leveled operation. Further, it is demonstrated that driving cycles with lower power requirements will show an improvement in vehicle fuel economy from hybridization while those with higher power requirements will not. Finally it is acknowledged that the practical considerations of cost and volume must also weigh heavily into the decision between the two configurations.

Drivers continually require steering performance improvement, particularly in the area of feedback from the road. In this study, we develop a new electrically-assisted steering logic by 1) analyzing existing steering systems to determine key factors, 2) modeling an ideal steering system from which to obtain a desirable driver torque, 3) developing a rack force observer to faithfully represent road information and 4) building a feedback compensator to track the tuned torque. In general, the estimator uses the driver torque, assist torque and other steering system signals. However, the friction of the steering system is difficult to estimate accurately. At high speed, where steering feeling is very important, greater friction results in increased error. In order to solve this problem, we design two estimators generated from a vehicle model and a steering system model. The observer that uses two estimators can reflect various operating conditions by using the strengths of each method.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles.