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Toyota Motor Corporation has developed the new compact-class hybrid vehicle (HV). This vehicle incorporates Toyota Hybrid System II (THS-II) to improve fuel efficiency. For this system we have developed a new power control unit (PCU) that features size reduction, light weight, and high efficiency. We have also improved the ability to mass produce these units with the expectation of rapid popularization of HV. The PCU, which plays an important role in THS-II, is our main focus in this paper. Its development is described.

In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks (i.e., gross vehicle weight greater than 4,536 kg). The Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed by the Federal Motor Carrier Safety Administration (FMCSA) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Visual warnings have been shown to be effective, assuming the following driver is looking directly at the warning display or has his/her eyes drawn to it. A visual warning can be placed where it is needed and it can be designed so that its meaning is nearly unambiguous. FMCSA contracted with the Virginia Tech Transportation Institute (VTTI) to investigate potential benefit of additional rear warning-light configurations as rear-end crash countermeasures for heavy trucks.

Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

Ride control of military vehicles is challenging due to varied terrain and mission requirements such as operating weight. Achieving top speeds on rough terrain is typically considered a key performance parameter, which is always constrained by ride discomfort. Many military vehicles using passive suspensions suffer with compromised performance due to single tuning solution. To further stretch the performance domain to achieving higher speeds on rough roads, semi-active suspensions may offer a wide range of damping possibilities under varying conditions. In this paper, various semi-active control strategies are examined, and improvements have been made, particularly, to the acceleration-driven damper (ADD) strategy to make the approach more robust for varying operating conditions. A seven degrees of freedom ride model and a quarter-car model were developed that were excited by a random road process input modeled using an auto-regressive time series model.

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.

Studying the kinetic and kinematics of the rim-tire combination is very important in full vehicle simulations, as well as for the tire design process. Tire maneuvers are either quasi-static, such as steady-state rolling, or dynamic, such as traction and braking. The rolling of the tire over obstacles and potholes and, more generally, over uneven roads are other examples of tire dynamic maneuvers. In the latter case, tire dynamic models are used for durability assessment of the vehicle chassis, and should be studied using high fidelity simulation models. In this study, a three-dimensional finite element model (FEM) has been developed using the commercial software package ABAQUS. The purpose of this study is to investigate the tire dynamic behavior in multiple case studies in which the transient characteristics are highly involved.

The road profile has been shown to have significant effects on various vehicle conditions including ride, handling, fatigue or even energy efficiency; as a result it has become a variable of interest in the design and control of numerous vehicle parts. In this study, an integrated state estimation algorithm is proposed that can provide continuous information on road elevation and profile variations, primarily to be used in active suspension controls. A novel tire instrumentation technology (smart tire) is adopted together with a sensor couple of wheel attached accelerometer and suspension deflection sensor as observer inputs. The algorithm utilizes an adaptive Kalman filter (AKF) structure that provides the sprung and unsprung mass displacements to a sliding-mode differentiator, which then yields to the estimation of road elevations and the corresponding road profile along with the quarter car states.

Vehicle analytical models are often favorable due to describing the physical phenomena associated with vehicle operation following from the principles of physics, with explainable mathematical trends and with extendable modeling to other types of vehicle. However, no experimentally validated analytical model has been developed as yet of diesel engine fuel consumption rate. The present paper demonstrates and validates for trucks and light commercial vehicles an analytical model of supercharged diesel engine fuel consumption rate. The study points out with 99.6% coefficient of determination that the average percentage of deviation of the steady speed-based simulated results from the corresponding field data is 3.7% for all Freeway cycles. The paper also shows with 98% coefficient of determination that the average percentage of deviation of the acceleration-based simulated results from the corresponding field data under negative acceleration is 0.12 %.

New engine controls have been developed for the turbocharged Lexus NX200t to improve driving power by reducing engine torque output lag. Drive power management functions have been centralized in an integrated drive power control system. The newly developed controls minimize the potential reduction in drivability associated with the adoption of a turbocharged engine while improving fuel efficiency. General driveability issues commonly associated with a turbocharged engine include sudden increases in drive power due to the response lag of the turbocharger, and higher shifting frequencies if this response lag triggers a disturbed accelerator operation pattern by the driver. The developed technologies detect and control sudden increases in drive power to create the optimum drive power map, and reduce unnecessary shifts even if the driver's accelerator operation is disturbed.

Modeling the tire forces and moments (F&M) generation, during combined slip maneuvers, which involves cornering and braking/driving at the same time, is essential for the predictive vehicle performance analysis. In this study, a new semi-empirical method is introduced to estimate the tire combined slip F&M characteristics based on flat belt testing machine measurement data. This model is intended to be used in the virtual tire design optimization process. Therefore, it should include high accuracy, ease of parameterization, and fast computational time. Regression is used to convert measured F&M into pure slip multi-dimensional interpolant functions modified by weighting functions. Accurate combined slip F&M predictions are created by modifying pure slip F&M with empirically determined shape functions. Transient effects are reproduced using standard relaxation length equations. The model calculates F&M at the center of the contact patch.

Intersection crashes are a frequent and dangerous crash mode in the U.S. Emerging Intersection Advanced Driver Assistance Systems (I-ADAS) aim to assist the driver to mitigate the consequences of vehicle-to-vehicle crashes at intersections. In support of the design and evaluation of such intersection assistance systems, characterization of the road, environment, and drivers associated with intersection crashes is necessary. The objective of this study was to characterize intersection crashes using nationally representative crash databases that contained all severity, serious injury, and fatal crashes. This study utilized four national crash databases: the National Automotive Sampling System, General Estimates System (NASS/GES); the NASS Crashworthiness Data System (CDS); and the Fatality Analysis Reporting System (EARS) and the National Motor Vehicle Crash Causation Survey (NMVCCS).

This paper presents a custom integrated circuit (IC) on which circuit functions necessary for “Active Hydraulic Brake (AHB) system” are integrated, and its key component, “Current-to-Digital Converter” for solenoid current measurement. The AHB system, which realizes a seamless brake feeling for Antilock Brake System (ABS) and Regenerative Brake Cooperative Control of Hybrid Vehicle, and the custom IC are installed in the 4th-generation Prius released in 2015. In the AHB system, as linear solenoid valves are used for hydraulic brake pressure control, high-resolution and high-speed sensing of solenoid current with ripple components due to pulse width modulation (PWM) is one of the key technologies. The proposed current-to-digital converter directly samples the drain-source voltage of the sensing DMOS (double-diffused MOSFET) with an analog-to-digital (A/D) converter (ADC) on the IC, and digitizes it.

In this paper, we consider smart charging and vehicle-to-home (V2H) technologies for plugin electric vehicles coordinated with home energy management systems (HEMS) for automated demand response. In this system, plugin electric vehicles automatically react to demand response events with or without HEMS’s coordination, while vehicles are charged and discharged (i.e., V2H) in appropriate time slots by taking into account demand response events, time-ofuse rate information, and users’ vehicle usage plan. We introduce three approaches on home energy management: centralized energy control, distributed energy control, and coordinated energy control. We implemented smart charging and V2H systems by employing two sets of standardized communication protocols: one using OpenADR 2.0b, SEP 2.0, and SAE standards and the other using OpenADR 2.0b, ECHONET Lite, and ISO/IEC 15118.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.

Plugin hybrid electric vehicles (PHEVs) have several attractive features in terms of reduction of greenhouse gas (GHG) emissions. Compared to conventional vehicles (CVs) that only have an internal combustion engine (ICE), PHEVs have better energy efficiency like regular hybrids (HEVs), allow for electrifying an appreciable portion of traveled miles, and have no range anxiety issues like battery-only electric vehicles (BEVs). However, in terms of criteria emissions (e.g., NOx, NMOG, HC), it is unclear if PHEVs are any better than HEVs or CVs. Unlike GHG emissions, criteria emissions are not continuously emitted in proportional quantities to fossil fuel consumption. Rather, the amount and type of criteria emissions is a rather complex function of many factors, including type of fuel, ICE temperature, speed and torque, catalyst temperature, as well as the ICE controls (e.g., fuel-to-air ratio, valve and ignition timing).

This paper presents typical flow structures around a 60%-scale wind-tunnel model of a Formula One (F1) car, using planar particle image velocimetry (PIV). The customized PIV system is permanently installed in a wind tunnel to help aerodynamicists in the development loop. The PIV results enhance the understanding of the mean velocity field in the two-dimensional plane in some important areas of the car, such as the front-wheel wake and the underfloor flow. These real phenomena obtained in the wind tunnel also help maintain the accuracy of simulations using computational fluid dynamics (CFD) by allowing regular checking of the correlation with the real-world counterpart. This paper first surveys recent literature on unique flow structures around the rotating exposed wheel, mostly that on the isolated wheel, and then gives the background to F1 aerodynamics in the late 2000s.

The use of hybrid, fuel cell electric, and pure electric vehicles is on the increase as part of measures to help reduce exhaust gas emissions and to help resolve energy issues. These vehicles use regenerative-friction brake coordination technology, which requires a braking system that can accurately control the hydraulic brakes in response to small changes in regenerative braking. At the same time, the spread of collision avoidance support technology is progressing at a rapid pace along with a growing awareness of vehicle safety. This technology requires braking systems that can apply a large braking force in a short time. Although brake systems that have both accurate hydraulic control and large braking force have been developed in the past, simplification is required to promote further adoption.

Conventional CFD-based shape optimization technology that uses parametric shape modification and optimal solutions searching algorithms has the two problems: (1) outcome of optimized shapes depend on the selection of design parameters made by the designer, and (2) high computational costs. To resolve those problems, two innovative inverse analysis technologies based on the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape for maximizing the cost function in the constrained design space. However, these technologies are only applicable to steady flows. Since most flows in a vehicle (such as engine in-cylinder flow) are transient, a practical technology for surface geometry sensitivity analysis has been developed based on the Transient Adjoint Method.