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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.

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.

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.

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.

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.

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.

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.