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Active safety systems have become an essential part of today's vehicles including SUVs and LTVs. Although they have advanced in many aspects, there are still many areas that they can be improved. Especially being able to obtain information about tire-vehicle states (e.g. tire slip-ratio, tire slip-angle, tire forces, tire-road friction coefficient), would be significant due to the key role tires play in providing directional stability and control. This paper first presents the implementation strategy for a dynamic tire slip-angle estimation methodology using a combination of a tire based sensor and an observer system. The observer utilizes two schemes, first of which employs a Sliding Mode Observer to obtain lateral and longitudinal tire forces. The second step then utilizes the force information and outputs the tire slip-angle using a Luenberger observer and linearized tire model equations.

Improving fuel economy and overall vehicle emissions are very important in today's society with strict new regulations throughout the world. To help in the education process for the next generation of design engineers, this paper seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. The 1 Hz model described within this work aims to allow energy to be simply and understandably traced through a hybrid powertrain. Through the use of a “backwards” energy tracking method, demand for a drive cycle is found, and, after tracing the energy demand through each powertrain component, the resulting fuel to meet vehicle demand and associated powertrain losses is found.

The recovery of braking energy through regenerative braking is a key enabler for the improved efficiency of Hybrid Electric Vehicles, Plug-in Hybrid Electric, and Battery Electric Vehicles (HEV, PHEV, BEV). However, this energy is often treated in a simplified fashion, frequently using an overall regeneration efficiency term, ξrg [1], which is then applied to the total available braking energy of a given drive-cycle. In addition to the ability to recapture braking energy typically lost during vehicle deceleration, hybrid and plug-in hybrid vehicles also allow for reduced or zero engine fueling during vehicle decelerations. While regenerative braking is often discussed as an enabler for improved fuel economy, reduced fueling is also an important component of a hybrid vehicle's ability to improve overall fuel economy.

Human expert drivers have the unique ability to combine correlated sensory inputs with repetitive learning to build complex perceptive models of the vehicle dynamics as well as certain key aspects of the tire-ground interface. This ability offers significant advantages for navigating a vehicle through the spatial and temporal uncertainties in a given environment. Conventional traction control algorithms utilize measurements of wheel slip to help insure that the wheels do not enter into an excessive slip condition such as burnout. This approach sacrifices peak performance to ensure that the slip limits are generic enough suck that burnout is avoided on a variety of surfaces: dry pavement, wet pavement, snow, gravel, etc. In this paper, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy.

Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

In the case of modern day vehicle control systems employing a feedback control structure, a real-time estimate of the tire-road contact parameters is invaluable for enhancing the performance of the chassis control systems such as anti-lock braking systems (ABS) and electronic stability control (ESC) systems. However, at present, the commercially available tire monitoring systems are not equipped to sense and transmit high speed dynamic variables used for real-time active safety control systems. Consequently, under the circumstances of sudden changes to the road conditions, the driver's ability to maintain control of the vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available.

The influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

Vehicle rollovers are one of the more severe crash modes in the US - accounting for 32% of all passenger vehicle occupant fatalities annually. One design enhancement to help prevent rollovers is Electronic Stability Control (ESC) which can reduce loss of control and thus has great promise to enhance vehicle safety. The objectives of this research were (1) to estimate the effectiveness of ESC in reducing the number of rollover crashes and (2) to identify cases in which ESC did not prevent the rollover to potentially advance additional ESC development. All passenger vehicles and light trucks and vans that experienced a rollover from 2006 to 2015 in the National Automotive Sampling System Crashworthiness Database System (NASS/CDS) were analyzed. Each rollover was assigned a crash scenario based on the crash type, pre-crash maneuver, and pre-crash events.

Hybrid Electric Vehicle (HEV) owners have reported significantly lower fuel economy than the published estimates. Under on-road driving conditions, vehicle acceleration, speed, and stop time differ from those on the normalized test procedures. To explain the sensitivity, several vehicles, both conventional and hybrid electric, were tested at Argonne National Laboratory. The tests demonstrated that the fuel economy of Prius MY04 was more sensitive to drive-cycle variations. However, because of the difficulty in instrumenting every component, an in-depth analysis and quantification of the reasons behind the higher sensitivity was not possible. In this paper, we will use validated models of the tested vehicles and reproduce the trends observed during testing. Using PSAT, the FreedomCAR vehicle simulation tool, we will quantify the impact of the main component parameters, including component efficiency and regenerative braking.

This paper provides a simulation analysis of a novel interconnected roll stability control (RSC) system for improving the roll stability of semitrucks with double trailers. Different from conventional RSC systems where each trailer’s RSC module operates independently, the studied interconnected RSC system allows the two trailers’ RSC systems to communicate with each other. As such, if one trailer’s RSC activates, the other one is also activated to assist in further scrubbing speed or intervening sooner. Simulations are performed using a multi-body vehicle dynamics model that is developed in TruckSim® and coupled with the RSC model established in Simulink®. The dynamic model is validated using track test data. The simulation results for a ramp steer maneuver (RSM) and sine-with-dwell (SWD) maneuver indicate that the proposed RSC system reduces lateral acceleration and rollover index for both trailers, decreasing the likelihood of wheel tip-up and vehicle rollover.

Procedures are in place for testing emissions and fuel economy for virtually every type of light-duty vehicle with a single-axle chassis dynamometer, which is why nearly all emissions test facilities use single-axle dynamometers. However, hybrid electric vehicles (HEVs) employ regenerative braking. Thus, the braking split between the driven and non-driven axles may interact with the calculation of overall efficiency of the vehicle. This paper investigates the regenerative braking systems of a few production HEVs and provides an analysis of their differences in single-axle (2WD) and double-axle (4WD) dynamometer drive modes. The fuel economy results from 2WD and 4WD operation are shown for varied cycles for the 2000 Honda Insight, 2001 Toyota Prius, and the 2004 Toyota Prius. The paper shows that there is no evidence that a bias in testing an HEV exists because of the difference in operating the same hybrid vehicle in the 2WD and 4WD modes.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

The strong correlation between vehicle weight and fuel economy for conventional vehicles (CVs) is considered common knowledge, and the relationship of mass reduction to fuel consumption reduction for conventional vehicles (CVs) is often cited without separating effects of powertrain vs. vehicle body (glider), nor on the ground of equivalent vehicle performance level. This paper challenges the assumption that this relationship is easily summarized. Further, for hybrid electric vehicles (HEVs) the relationship between mass, performance and fuel consumption is not the same as for CVs, and vary with hybrid types. For fully functioning (all wheel regeneration) hybrid vehicles, where battery pack and motor(s) have enough power and energy storage, a very large fraction of kinetic energy is recovered and engine idling is effectively eliminated.

Hybrid electric vehicles have demonstrated their ability to significantly reduce fuel consumption for several medium- and heavy-duty applications. In this paper we analyze the impact on fuel economy of the hybridization of a tractor-trailer. The study is done in PSAT (Powertrain System Analysis Toolkit), which is a modeling and simulation toolkit for light- and heavy-duty vehicles developed by Argonne National Laboratory. Two hybrid configurations are taken into account, each one of them associated with a level of hybridization. The mild-hybrid truck is based on a parallel configuration with the electric machine in a starter-alternator position; this allows start/stop engine operations, a mild level of torque assist, and a limited amount of regenerative braking. The full-hybrid truck is based on a series-parallel configuration with two electric machines: one in a starter-alternator position and another one between the clutch and the gearbox.

This study involves the battery requirements for a fuel cell-powered hybrid electric vehicle. The performances of the vehicle [a 3200-lb (1455-kg) sedan], the fuel cell, and the battery were evaluated in a vehicle simulation. Most of the attention was given to the design and performance of the battery, a lithium-ion, manganese spinel-graphite system of 75-kW power to be used with a 50-kW fuel cell. The total power performance of the system was excellent at the full operating temperatures of the fuel cell and battery. The battery cycling duty is very moderate, as regenerative braking for the Federal Urban Driving Schedule and the Highway Fuel Economy Test cycles can do all charging of the battery. Cold start-up at 20°C is straightforward, with full power available immediately.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

Outdoor objective evaluations form an important part of both tire and vehicle design process since they validate the design parameters through actual tests and can provide insight into the functional performances associated with the vehicle. Even with the industry focused towards developing simulation models, their need cannot be completely eliminated as they form the basis for approving the performance predictions of any newly developed model. An objective test was conducted to measure the ABS performance as part of validation of a tire simulation design tool. A sample vehicle and a set of tires were used to perform the tests- on a road with known profile. These specific vehicle and tire sets were selected due to the availability of the vehicle parameters, tire parameters and the ABS control logic. A test matrix was generated based on the validation requirements.

Dynamic “Game Theory” brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In previous studies, it was shown that vehicle stability can be represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the vehicle stability controller (VSC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the VSC command is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion. This study focus on the uncertainty in the inputs, and more specifically, the driver's steering input.