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Superior NVH performance is a key focus in the development of new powertrains. In recent years, computer simulations have gained an increasing role in the design, development, and optimization of powertrain NVH at component and system levels. This paper presents the results of a study carried out on a 4-cylinder in-line spark-ignition engine with focus on growl noise. Growl is a low frequency noise (300-700 Hz) which is primarily perceived at moderate engine speeds (2000-3000 rpm) and light to moderate throttle tip-ins. For this purpose, a coupled and fully flexible multi-body dynamics model of the powertrain was developed. Structural components were reduced using component mode synthesis and used to determine dynamics loads at various engine speeds and loading conditions. A comparative NVH assessment of various crankshaft designs, engine configurations, and in- cylinder gas pressures was carried out.

The fuel air ratio imbalance between individual cylinders can result in poor fuel economy and severe exhaust emissions. Individual cylinder fuel air ratio control is one of the important techniques used to improve fuel economy and reduce exhaust emission. California Air Resources Board (CARB) also has required automotive manufacturers to equip with on-board diagnosis system for cylinder fuel air ratio imbalance detection starting in 2011. However, one of the most challenging tasks for the individual cylinder fuel air ratio control and cylinder imbalance diagnosis is how to retrieve the cylinder fuel air ratio information effectively at low cost. This paper presents a novel and practical signal processing based fuel air ratio estimation method for individual cylinder fuel air ratio balance control and on-board fuel air ratio imbalance diagnosis.

Two on-line algorithms have been developed to acquire driveline component loads in terms of revolutions at torque and rainflow cycle counting matrix. These algorithms have been implemented in real-time on a standard engine controller unit and have been optimized for fast run-time and low memory requirements. The revolutions at torque algorithm is intended to count the number of driveshaft revolutions in each torque level for each gear and store the number of counts in the engine controller memory. The rainflow cycle counting algorithm is intended to count driveshaft torque cycles and to store the number of counts in a two dimensional “from-to” matrix format in the engine controller memory. The revolutions at torque histogram data and the rainflow cycle counting matrix are then downloaded from the vehicle using the data collection device. Download occurs when the vehicle is serviced at a dealership.

This paper is the second in the series of documents designed to record the progress of a series of SAE documents - SAE J2836™, J2847, J2931, & J2953 - within the Plug-In Electric Vehicle (PEV) Communication Task Force. This follows the initial paper number 2010-01-0837, and continues with the test and modeling of the various PLC types for utility programs described in J2836/1™ & J2847/1. This also extends the communication to an off-board charger, described in J2836/2™ & J2847/2 and includes reverse energy flow described in J2836/3™ and J2847/3. The initial versions of J2836/1™ and J2847/1 were published early 2010. J2847/1 has now been re-opened to include updates from comments from the National Institute of Standards Technology (NIST) Smart Grid Interoperability Panel (SGIP), Smart Grid Architectural Committee (SGAC) and Cyber Security Working Group committee (SCWG).

Improvements to 1D engine modeling accuracy and computational speed have led to greater reliance on this simulation technology during the engine development process. The benefits of modeling show up in many ways: increased simulation iterations for better optimization, reduction in prototype hardware iterations, reduction in program timing and overall cost. In this study a 1D GT-Power model of a turbocharged engine system was used to assist in the initial design phase and throughout the program. The model was developed using Chrysler Group LLC proprietary modeling features for predictive combustion and knock event prediction. In all stages of this project the model's accuracy was improved through regular correlation with dynamometer data. This paper mainly focuses on engine compression ratio selection, turbocharger selection, and cycle-to-cycle variation/cylinder-to-cylinder variation reduction through the combination of 1D GT-Power model optimization and dynamometer tests.

The common practice in finite element based fatigue calculation with multiple channels of road load is to perform a set of unit load static stress analysis and conduct stress time history construction later during fatigue calculation. The main advantage of this so-called quasi-static finite element based fatigue calculation is to avoid time-consuming dynamic stress analysis and also reduce static stress analysis from millions of real load cases to a few dozens unit-load cases. The main disadvantage of this quasi-static finite element based fatigue calculation is the absence of vibration-induced stresses in stress time history construction and fatigue analysis. A decade ago, a modal transient finite element based fatigue calculation was proposed to introduce vibration-induced stresses into finite element based fatigue calculation. The idea is to add vibration-induced modal stresses to load-induced instant stresses in stress time history construction and fatigue calculation.

The concept of full vehicle simulation has been embraced by the automobile industry as it is an indispensable tool for analyzing vehicles. Vehicle loads traditionally obtained by road load data acquisition such as wheel forces are typically not invariant as they depend on the vehicle that was used for the measurement. Alternatively, virtual road load data acquisition approach has been adopted in industry to derive invariant loads. Analytical loads prior to building hardware prototypes can shorten development cycles and save costs associated with data acquisition. The approach described herein estimate realistic component load histories with sufficient accuracy and reasonable effort using full vehicle simulations. In this study, a multi-body dynamic model of the vehicle was built and simulated over digitized road using ADAMS software, and output responses were correlated to a physical vehicle that was driven on the same road.

The Cardan joint of a steerable beam front axle is a complicated mechanical component. It is subjected to drive torque, speed fluctuations, and joint articulation due to powertrain inputs, steering, and suspension kinematics. This combination of high torque and speed fluctuations of the Cardan joint, due to high input drive torque and/or high steer angle maneuvers, can result in premature joint wear. Initially, some observations of premature wear were not well understood based on the existing laboratory and road test data. The present work summarizes a coordinated program of computer modeling, vehicle Rough Road data acquisition, and physical testing used to predict the joint dynamics and to develop advanced testing procedures. Results indicate analytical modeling can predict forces resulting from Cardan joint dynamics for high torque/high turn angle maneuvers, as represented by time history traces recorded in rough road data acquisition.

In this paper, the problem of stability control of an electric vehicle is addressed. To this aim, it is required that the vehicle follows a desired yaw rate at all driving/road conditions. The desired yaw rate is calculated based on steering angle, vehicle speed, vehicle geometric properties as well as road conditions. The vehicle response is modified by torque vectoring on front and/or rear axles. This control problem is subject to several constraints. The electric motors can only deliver a certain amount of torque at a given rotational speed. In addition, the tire capacity also plays an important role. It limits the amount of torque they can transfer without causing wheel to slip excessively. These constraints make the Model Predictive Control (MPC) approach a suitable choice, because it can explicitly consider the constraints of the control problem, in particular the tire capacity constraint, and help prevent tire saturation, which is often the cause of vehicle instability.

This paper describes a comprehensive methodology for the simulation of vehicle body panel buckling in an electrophoretic coat (electro-coat or e-coat) and/or paint oven environment. The simulation couples computational heat transfer analysis and structural analysis. Heat transfer analysis is used to predict temperature distribution throughout a vehicle body in curing ovens. The vehicle body temperature profile from the heat transfer analysis is applied as an input for a structural analysis to predict buckling. This study is focused on the radiant section of the curing ovens. The radiant section of the oven has the largest temperature gradients within the body structure. This methodology couples a fully transient thermal analysis to simulate the structure through the electro-coat and paint curing environments with a structural, buckling analysis.

Computational tools have been extensively applied to predict component temperatures before an actual vehicle is built for testing [1, 2, 3, 4, and 5]. This approach provides an estimate of component temperatures during a specific driving condition. The predicted component temperature is compared against acceptable temperature limits. If violations of the temperature limits are predicted, corrective actions will be applied. These corrective actions may include adding heat shields to the heat source or to the receiving components. Therefore, design changes are implemented based on the simulation results. Sensitivity analysis is the formal technique of determining most influential parameters in a system that affects its performance. Uncertainty analysis is the process of evaluating the deviation of the design from its intended design target.

Today's vehicles contain a number of safety-critical systems designed to help improve overall vehicle safety. Such systems may control vital vehicle functions such as steering, braking and/or propulsion independently of the driver. In today's vehicles, much emphasis has been placed on helping ensure that these safety-critical vehicle systems operate as intended. Applying rigorous system safety engineering principles in developing these safety-critical automotive systems helps ensure that they operate as desired and expected. Less emphasis has been placed to-date on helping ensure cybersecurity of cyber-physical automotive systems. However, this is changing as both the world and the automotive industry become more aware of the potential ramifications of cyber-attacks on vehicles.

Vehicle front end air flow management affects many aspects of vehicle aero/thermal performances. The HVAC system capacity is greatly driven by the airflow and the air temperature received at the condenser. In this paper, front end design practices are investigated using computer simulation and full vehicle test to evaluate their effects on AC system performance. A full vehicle 3D CFD model is developed and used to predict the airflow and temperature in underhood and around the vehicle body, and specifically the conditions entering the condenser. The condenser inlet airflow and temperature profiles from 3D CFD model are then used as inputs for the 1D AC system model. The 1D AC system model, which includes condenser, compressor, evaporator and TXV (Thermal eXpansion Valve), is developed to observe the critical AC performance indicators such as panel out air temperature and compressor head pressure.