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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.

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.

A turbocharger is currently widely used to boost performance of an internal combustion engine. Generally, a turbocharger consists of a compressor which typically is driven by an exhaust turbine. The compressor will influence how the low frequency engine pulsation propagates in the intake system. The compressor will also produce broad-band flow induced sound due to the turbulence flow and high frequency narrowband tonal sound which is associated with rotating blade pressures. In this paper, a practical simulation procedure based on a computational fluid dynamics (CFD) approach is developed to predict the flow induced sound of a turbocharger compressor. In the CFD model of turbocharger compressor, the unsteady, moving wheel, detached eddy simulation (DES) approach are utilized. In this manner, both the broad-band and narrow-band flow induced sound are directly resolved in the CFD computation.

Vehicle multi-zone automatic Heating, Venting and Air Conditioning (HVAC) is the advanced form of the traditional air conditioning. The advantage of multi-zone automatic HVAC is that it allows the passengers of a vehicle to set a desired temperature for their own zone within the vehicle compartment. This desired temperature is then maintained by the HVAC system, which determines how best to control the available environment data to provide optimal comfort for the passengers. To achieve overall thermal comfort of the occupants in a vehicle, multi-zone HVAC takes things a step further by adding heated steering wheel and heated/vented seats to the overall HVAC control strategy. The heating and cooling of the occupants by this integrated system is performed by complex control algorithms in form of embedded software programs and Private LIN network. This paper describes the approach and tools used to develop, simulate and validate the multi-zone integrated climate control system.

Due to the increase in heat wave across the globe, maintaining the thermal comfort of passengers in a vehicle is becoming a challenge. Considering global warming, there is a need to shift towards greener refrigerants which in itself causes a marginal degradation in the Heating Ventilation and Air Conditioning (HVAC) system performance. Also the emission norms and regulations demanding for a smaller engine if not for a hybrid or electric vehicle, there is a need for optimally designing the HVAC system since it is directly related with the efficiency of the vehicle and also plays a vital role in the customer comfort. Hence maintaining the comfort level of the passengers needs further exploration and challenging rather than optimizing the HVAC system alone in the competitive market. Conventionally for given system where we need sufficient cooling, the capacity of the components can be increased in order to meet the customer comfort.

Idle shake is an important NVH attribute. Vehicles with good NVH characteristics are designed to perform excellent in IDLE and SHAKE conditions. Typically, tactile vibrations at idle are measured at the driver seat and steering wheel. Vibrations caused by engine excitation at idle are passed through several paths to the body structure. The dominant paths being the engine mounts and the half-shafts, either one of them or both can be a major factor influencing the perceived idle vibration in a vehicle. In the past, modeling the half-shafts accurately has been a challenge and often time has been ignored because of modeling complexity. This has led to idle CAE predictions not correlating with test data. The aim of this paper is to describe a finite element modeling method of half-shaft to predict idle vibrations levels.

It has been previously shown that a detailed representation of the half-shaft correlates with test data. Developed detailed half-shaft models have shown improvement in capturing the half-shaft path at vehicle idle condition. Since the detailed half-shaft model needs to capture many components and requires detailed solid geometry for each component represented, full CAD model from half-shaft supplier or part scanning is required. Furthermore, despite the availability of CAD geometry, the detailed half-shaft will require solid meshing of the CV joints, the shaft, linearized springs and manual creation of the complex coordinate systems for orientation of contact points. This paper proposes an automated method to reduce the half-shaft model to a semi-elastic rigid body elements model with linearized spring components. The simplified model reduces the modeling time by eliminating solid meshing of components and automating complex coordinate system development without losing accuracy.

There are many noise sources from the vehicle fuel system to generate noise inside a vehicle. Among them, the pressure pulsation due to the rapid opening and closing of gasoline engine injectors can cause undesirable fuel pulse noise. As the pressure pulsation propagates in the fuel supply line toward to rear end of the vehicle, the pressure energy is transferred from fuel lines to the vehicle underbody through clips and into the passenger compartment. It is crucial to attenuate the pressure pulsation inside the fuel line to reduce the fuel pulse noise. In this paper, a case study on developing an effective countermeasure to reduce the objectionable fuel pulse noise of a V8 gasoline injection system at engine idle condition is presented. First, the interior noise of a prototype vehicle was tested and the objectionable fuel pulse noise is exhibited. The problem frequency ranges of the pulse noise were identified.

One approach of reducing weight of vehicles is using composite materials, and short glass fiber reinforced polypropylene is one of most popular composite materials. To more accurately predict durability performance of structures made of this kind of composite material, static and fatigue performance of coupons and components made of a short glass fiber reinforced polypropylene has been physically studied. CAE simulations have been conducted accordingly. This paper described details of CAE model setup, procedures, analysis results and correlations to test results for static, fiber orientation flow and fatigue of coupons and a battery tray component. The material configurations include fiber orientations (0, 20 and 90 degrees), and mean stress effect (R = -1.0, -0.5, -0.2, 0.1 and 0.4). The battery tray component samples experience block cycle loading with loading ratio of R = -0.3 and 0.3. The CAE predictions have reasonable correlations to the test results.

In this paper we present an integrated approach which combines analysis of the effect of simultaneous variations in model input parameters on component or system temperatures. The sensitivity analysis can be conducted by varying model input parameters using specific values that may be of interest to the user. The alternative approach is to use a structured set of parameters generated in the form of a DFSS DOE matrix. The matrix represents a combination of simulation conditions which combine the control factors (CF) and noise factors. CF’s are the design parameters that the engineer can modify to achieve a robust design. Noise factors include parameters that are outside the control of the design engineer. In automotive thermal management, noise factors include changes in ambient temperature, exhaust gas temperatures or aging of exhaust system or heat shields for example.

Computational cost plays a major role in the performance of scientific and engineering simulation. This in turn makes the virtual validation process complex and time consuming. In the simulation process, achievement of appropriate level of accurate models as close as physical testing is the root for increase in the computational cost. During preliminary phase of product development, it is difficult to identify the appropriate size, shape and other parameters of the component and they will undergo several modifications in concept and other stages. An approximation model called metamodel or surrogate model has developed for reducing these effects and minimizing the computational cost. Metamodel can be used in the place of actual simulation models. Metamodel can be an algorithm or a mathematical relation representing the relations between input and output parameters.

Magnetic Resonance Velocimetry (MRV) measurements are performed in 1:1 scale models of a single-cylinder optical engine to investigate the differences in the inlet flow due to geometrical changes of the cylinder head. The models are steady flow water-analogue of the optical IC engine with a fixed valve lift of 9.21 mm to simulate the induction flow at 270° bTDC. The applicability of MRV to engine flows despite the differences in experimental operating parameters between the steady flow model and the optical IC engine are demonstrated and well addressed in this manuscript and in a previous work [1]. To provide trust into the MRV measurements, the data is validated with phase-averaged particle image velocimetry (PIV) measurements performed within the optical engine. The main geometrical changes between the cylinder heads include a variation of intake valve diameter and slight modifications to the exit of the intake port.

Determining coolant flow distribution in a topologically complex flow path for efficient heat rejection from the critical regions of the engine is a challenge. However, with the established computational methodology, thermal response of an engine (via conjugate heat transfer) can be accurately predicted [1, 2] and improved upon via Design of Experiment (DOE) study in a relatively short timeframe. This paper describes a method to effectively distribute the coolant flow in the engine coolant cavities and evenly remove the heat from various components using a novel technique of optimization based on an approximation model. The current methodology involves the usage of a sampling technique to screen the design space and generate the simulation matrix. Isight, a process automation and design exploration software, is used to set the framework of this study with the engine thermal simulation setup done in the CFD solver, STAR-CCM+.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.

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.

Two methods of assessing the similarity of a set of impact test signals have been proposed and used in the literature, which are cumulative variance-based and cross correlation-based. In this study, a normalized formulation unites these two approaches by establishing a relationship between the normalized cumulative variance metric (v), an overall similarity metric, and the normalized magnitude similarity metric (m) and shape similarity metric (s): v=1 − m · s. Each of these ranges between 0 and 1 (for the practical case of signals acquired with the same polarity), and they are independent of the physical unit of measurement. Under generally satisfied conditions, the magnitude similarity m is independent of the relative time shifts among the signals in the set; while the shape similarity s is a function of these.

One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.