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Safety and minimal transit time are vital during transportation of essential commodities and passengers, especially in winter conditions. Icy roads are the worst driving conditions with the least available friction, leaving valuable cargo and precious human lives at stake. The study investigates the available friction at the tire-ice interface due to changes in key operational parameters. Experimental analysis of tractive performance of tires on ice was carried out indoor, using the terramechanics rig located at the Advanced Vehicle Dynamics Laboratory (AVDL) at Virginia Tech. The friction-slip ratio curves obtained from indoor testing were inputted into TruckSIM, defining tire behavior for various ice scenarios and then simulating performance of trucks on ice. The shortcomings of simulations in considering the effects of all the operational parameters result in differences between findings of indoor testing and truck performance simulations.

The brake architecture of hybrid and full electric vehicle includes the distinctive function of brake blending. Known approaches draw upon the maximum energy recuperation strategy and neglect the operation mode of friction brakes. Within this framework, an efficient control of the blending functions is demanded to compensate external disturbances induced by unpredictable variations of the pad disc friction coefficient. In addition, the control demand distribution between the conventional frictional brake system and the electric motors can incur failures that compromise the frictional braking performance and safety. However, deviation of friction coefficient value given in controller from actual one can induce undesirable deterioration of brake control functions.

Main defect of traditional structure of the brake boosters is the necessity of an external energy source. The analysis of redistribution of power streams occurring during at braking of the automobile shows that it is possible to use the force component of the driving automobile kinetic energy for the drive of a booster (so-called kinetic booster). The power consumed by a booster is taken from power developed during brake action thereby it promotes vehicle slowing down. In the paper for the booster work the schemas of power take-off from an engine, onboard electric system, transmission, single wheel are considered. Especially for brake-by-wire systems the project of pilot management is probed. It allows applying the serial x-by-wire components both on small automobiles andon vehicle with the great load-carrying capacity and trailers.

Research and development tools for investigations of various facets of braking processes cover three major groups of devices: Dynamometer test rigs: assessment of performance, durability, life cycle and others; Tribometer test rigs: definition of parameters of friction and wear; Hardware-in-the-loop: estimation of functional properties of controlled braking. A combination of the listed devices allows to research complex phenomena related to braking systems. The presented work discusses a novel approach of test rig fusion, namely the combination of a brake dynamometer and hardware in the loop test rig. First investigations have been done during the operation of the anti-lock braking system (ABS) system to demonstrate the functionality of the approach.

This paper is part of the European OWHEEL project. It proposes a method to improve the comfort of a vehicle by adaptively controlling the Camber and Toe angles of a rear suspension. The purpose is achieved through two actuators for each wheel, one that allows to change the Camber angle and the other the Toe angle. The control action is dynamically determined based on the error between the reference angle and the actual angles. The reference angles are not fixed over time but dynamically vary during the maneuver. The references vary with the aim of maintaining a Camber angle close to zero and a Toe angle that follows the trajectory of the vehicle during the curve. This improves the contact of the tire with the road. This solution allows the control system to be used flexibly for the different types of maneuvers that the vehicle could perform. An experimentally validated sports vehicle has been used to carry out the simulations. The original rear suspension is a Trailing-arm suspension.

An important problem of recent active safety applications is data acquisition for parameters of tire-road interaction, especially coefficient of friction or specific forces in contact patch. Analysis of present solutions in this field allows setting off the virtual and hardware-based determination of tire grip properties. The virtual procedures can be subclassified into Dynamics simulation method Statistical method Fuzzy logic method. The hardware-based procedures are connected with On-board sensors of direct tire grip measurement On-board sensors of indirect tire grip measurement Off-board (on-road) sensors. For above mentioned variants the appropriate engineering solutions are considered in the paper. The long-term approach in road identification centers on active safety applications with on-road sensors, which are integrated in intelligent transportation systems (ITS). The paper proposes conceptual structure for this system.

Battery electric vehicles (BEV) share the ability of regenerative braking since they are equipped with two independent types of deceleration devices, namely the electric motor working as a generator and the friction brakes. Correct interaction of these systems in terms of driving safety and energy efficiency is a function of the Brake Blending Control. Individual electric motors for each wheel and a decoupled brake system provides the Brake Blending with a high design flexibility that allows significant advantages regarding energy consumption, brake performance, and driving comfort. This paper is focusing on the fail behaviour and analyses the robustness and redundancy abilities of such systems against various error scenarios. For this purposes, a distributed x-in-the-loop environment, consisting of dedicated simulation and hardware testing components, is introduced.

The presented study is dedicated to the technology supporting vehicle state estimation and motion control with a concept drone, which helps the vehicle in sensing the surroundings and driving conditions. This concept allows also extending the functionality of the sensors mounted on the vehicle by replacing or including additional parameter observation channels. The paper discusses the feasibility of such a drone-vehicle interaction as well as demonstrates several design configurations. In this regard, the paper presents a general description of the proposed drone system that assists the vehicle and describes an experiment in measuring the profile of the road with a range sensor. The results obtained in the experiment are described in terms of the accuracy to be achieved using the drone and are compared with other studies, which use the methods of estimation from the sensors mounted on the vehicle.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

E-mobility is a game changer for the automotive domain. It promises significant reduction in terms of complexity and in terms of local emissions. With falling prices and recent technological advances, the second generation of electric vehicles (EVs) that is now in production makes electromobility an affordable and viable option for more and more transport mission (people, freight). Still, major challenges for large scale deployment remain. They include higher maturity with respect to performance (e.g., range, interaction with the grid), development efficiency (e.g., time-to-market), or production costs. Additionally, an important market transformation currently occurs with the co-development of automated driving functions, connectivity, mobility-as-a-service. New opportunities arise to customize road transportation systems toward application-driven, user-centric smart mobility solutions.

The presented paper is dedicated to the driving comfort evaluation in the case of the electric vehicle architecture with four independent wheel corners equipped with in-wheel motors (IWMs). The analysis of recent design trends for electrified road vehicles indicates that a higher degree of integration between powertrain and chassis and the shift towards a corner-based architecture promises improved energy efficiency and safety performances. However, an in-wheel-mounted electric motor noticeable increases unsprung vehicle mass, leading to some undesirable impact on chassis loads and driving comfort. As a countermeasure, a possible solution lies in integrated active corner systems, which are not limited by traditional active suspension, steer-by-wire and brake-by-wire actuators. However, it can also include actuators influencing the wheel positioning through the active camber and toe angle control.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.

Battery-electric vehicles (BEVs) require new chassis components, which are realized as mechatronic systems mainly and support more and more by-wire functionality. Besides better controllability, it eases the implementation of integrated control strategies to combine different domains of vehicle dynamics. Especially powertrain layouts based on electric in-wheel machines (IWMs) require such an integrated approach to unfold their full potential. The present study describes an integrated, longitudinal vehicle dynamics control strategy for a battery electric sport utility vehicle (SUV) with an electric rear axle based on in-wheel propulsion. Especially the influence of electronic brake force distribution (EBD) and torque blending control on the overall performance are discussed and demonstrated through experiments and driving cycles on public road and benchmarked to results of previous studies derived from [1].

Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.