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The initial presence and dynamic formation of internal voids in human body models have been subjects of discussion within the human body modeling community. The relevant physics of the human body are described and the importance of capturing this physics for modeling of internal organ interactions is demonstrated. Basic modeling concepts are discussed along with a proposal of simulation setups designed to verify model behavior in terms of volume and pressure between internal organs.

Trivial Principal Component method (TPC) was developed recently to model a system based on measured data. It is a statistical method that utilizes Eigen-pairs of covariance matrix obtained from the measured data. It determines linear coefficients of a model by using the trivial eigenvector corresponding to the least eigenvalue. In general, linear modeling accuracy depends on the strength of nonlinearity and interaction terms as well as measurement error. In this paper, the TPC method is extended to analyze residual (error) vector to identify significant higher order and interaction terms that contribute to the modeling error. Subsequently, these additional terms are included for constructing a robust system model. Also, an iterative TPC analysis is proposed for the first time to correct the model gradually till the least eigenvalue becomes minimum.

General Motors has developed an all-new Ecotec 1.5 L range extender engine for use in the 2016 next generation Voltec propulsion system. This engine is part of a new Ecotec family of small displacement gasoline engines introduced in the 2015 model year. Major enhancements over the range extender engine in the current generation Voltec propulsion system include the adoption of direct injection (DI), cooled external exhaust gas recirculation (EGR), and a high 12.5:1 geometric compression ratio (CR). Additional enhancements include the adoption of high-authority phasers on both the intake and exhaust camshafts, and an integrated exhaust manifold (IEM). The combination of DI with cooled EGR has enabled significant thermal efficiency gains over the 1.4 L range extender engine in the current generation Voltec propulsion system at high engine loads.

This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

Model-Based Development processes in the automotive industry typically use high-level modeling languages to build the reference models of embedded controllers. One can use formal verification tools to exhaustively verify these design models against their requirements, ensuring high quality models and a reduction in the cost and effort of functional testing. However, there is a gap, in terms of processes and tools, between the informal requirements and the formal specifications required by the verification tools. In this paper, we propose an approach that tries to bridge this gap by (i) identifying the verifiable requirements through a categorization process, (ii) providing a set of templates to easily express the verifiable requirements, and (iii) generating monitors that can be used as specifications in design verification tools. We demonstrate our approach using the Simulink Design Verifier tool for design verification of Simulink/Stateflow models.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

One of the major problems that the automotive industry faces is reducing friction to increase efficiency. Researchers have shown that 30% of the fuel energy was consumed to overcome the friction forces between the moving parts of any automobile, Holmberg et al. [1]. The interface of the piston pin and pin bore is one of the areas that generate high friction under severe working conditions of high temperature and lack of lubrication. In this research, experimental investigation and theoretical simulation have been carried out to analyze the motion of the floating pin against pin bore. In the experimental study, the focus was on analyzing the floating pin motion by using a bench test rig to simulate the floating pin motion in an internal combustion engine. A motion data acquisition system was developed to capture and record the pin motion. Thousands of images were recorded and later analyzed by a code written by MATLAB.

A numerical and corresponding experimental study was undertaken to identify the ability to accurately predict combustion performance using our 3-D numerical tools for a direct-injection homogeneous-charge engine. To achieve a significant range of combustion rates, the evaluation was conducted for the engine operating with and without enhanced charge motion. Five charge motion configurations were examined, each having different levels of swirl and tumble flow leading to different turbulence generation and decay characteristics. A detailed CFD analysis provides insight into the in-cylinder flow requirements as well as the accuracy of the submodels. The in-cylinder air-fuel distribution, the mass-averaged swirl and tumble levels along with mean flow and turbulent kinetic energies are calculated throughout the induction and compression processes.

Determining Li-ion battery life through life modeling is an excellent tool in determining and estimating end-of-life performance. Achieving End-of-Life (EOL) can be challenging since it is difficult to achieve both cycle and calendar life during the same test without years of testing. The plan to correlate testing with the model included three (3) distinct temperature ranges, beginning with the four-Season temperature profile, an aggressive profile with temperatures in the 50 to 55°C range, and using a mid-temperature range (40-45°C) as a final comparison test. A high duty-cycle drive profile was used to cycle all of the batteries as quickly as possible to reach the one potential definition of EOL; significant increases in resistance or capacity fade.

Developing a robust model that can simulate all real world conditions a vehicle can experience can be extremely difficult to predict. When working through the engineering process, Computer Aided Engineers (CAE) traditionally set modeling parameters and conditions to a nominal setting. This is done to simplify the models so that it avoided inputting too much tedious details into the system and wasting so much engineering time preparing the work. It was soon realized that this strategy did not capture all the possible conditions a hood on a vehicle could experience. There was a need to develop a formal approach and method to correlate an analysis model to real world conditions. The Design for Six Sigma (DFSS) process was utilized to develop robustness in the techniques used to accurately understand the vehicle environment. The DFSS process is normally used to design and develop robustness into physical parts.

Simscape is a physical modeling language developed by Mathworks Inc. The language uses equation statements instead of assignment statements to describe physical systems. The paper focuses on the Simscape language itself instead of using components in the Simscape libraries. The language will be introduced from a perspective different from the Mathworks' Physical Network point of view. Our perspective focuses on two types of variables at the connectors. In additional, internal variables are not separated into through and across variables. The alternative perspective is more general and easier to understand. The paper also illustrates how to develop components in a powertrain library following the proposed new perspective.

A Design for Six Sigma (DFSS) statistical approach is presented in this report to correlate a CFD cabin model with test results. The target is the volume-averaged hot-soak terminal temperature. The objective is to develop an effective correlation process for a simplified CFD cabin model so it can be used in practical design process. It is, however, not the objective in this report to develop the most accurate CFD cabin model that would be too expensive computationally at present to be used in routine design analysis. A 3-D CFD model of a vehicle cabin is the central part of the computer modeling in the development of automotive HVAC systems. Hot-soak terminal temperature is a thermal phenomenon in the cabin of a parked vehicle under the Sun when the overall heat transfer reaches equilibrium. It is often part of the simulation of HVAC system operation.

The projected frontal area of a vehicle has a significant impact on aerodynamic drag, and thus is an important parameter, for vehicle development, benchmarking, and modeling. However, determining vehicle frontal area can be tedious, time consuming, expensive, or inaccurate. Existing methods include analysis of engineering drawings, vehicle projections, 3D scanners, planimeter measurements from photographs, and estimations using vehicle dimensions. Currently accepted approximation methods can be somewhat unreliable. This study focuses on introducing a method to find vehicle frontal area using digital images and subtraction functions via MATLABs' Image Processing Toolbox. In addition to an overview of the method, this paper describes several variables that were examined to optimize and improve the process such as camera position, surface glare, and vehicle shadow effects.

This paper reports a study undertaken by the Crash Safety Working Group (CSWG) of the United States Council for Automotive Research (USCAR) to determine generic acceleration pulses for testing and evaluating advanced batteries subjected to inertial loading for application in electric passenger vehicles. These pulses were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used in this study. Crash test data, in terms of acceleration time histories, were collected from various crash modes conducted by the National Highway Traffic Safety Administration (NHTSA) during their New Car Assessment Program (NCAP) and Federal Motor Vehicle Safety Standards (FMVSS) evaluations, and the Insurance Institute for Highway Safety (IIHS).

The fifth generation of General Motor's “Small Block” 90-degree V engine family has been developed with a totally new combustion system. This system employs direct fuel injection (DI) and carefully architected in-cylinder flow field development in order to significantly improve all aspects of combustion system performance. Efficiency improvements stem from increased compression ratio, greatly improved dilution tolerance, and excellent knock resistance. The asymmetric, 2-valve (2V) layout of the “Small Block” engine presented unique challenges in developing the combustion system, but also offered unusual opportunities for an elegant solution while retaining the traditional “Small Block” attributes of packaging efficiency and power density.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline injected using a triple-pulse strategy in the low temperature combustion (LTC) regime is presented. This work aims to extend the operation ranges for a light-duty diesel engine, operating on gasoline, that have been identified in previous work via extended controllability of the injection process. The single-cylinder engine (SCE) was operated at full load (16 bar IMEP, 2500 rev/min) and computational simulations of the in-cylinder processes were performed using a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion chosen to match ignition characteristics of the gasoline fuel used for the SCE experiments.

Recently, high-speed optical imaging data for a single operating point of a spray-guided gasoline engine has, along with the flamelet model and the G-equation theory, enabled the development of the new spark-ignition model SparkCIMM. Within its framework, detailed chemistry flamelet models capture the experimental feature of multiple localized ignition events along the excessively stretched and restriking spark channel, as well as the observations of non-spherical highly corrugated early turbulent flame fronts. The developed flamelet models account for the substantial turbulent fluctuations in equivalence ratio and enthalpy present under spray-guided conditions. A non-unity Lewis number formulation captures the deficient species diffusion into the highly curved flame reaction zone.

An iterative learning control (ILC) algorithm has been developed for a test cell electro-hydraulic, fully flexible valve actuation system to track valve lift profile under steady-state and transient operation. A dynamic model of the plant was obtained from experimental data to design and verify the ILC algorithm. The ILC is implemented in a prototype controller. The learned control input for two different lift profiles can be used for engine transient tests. Simulation and bench test are conducted to verify the effectiveness and robustness of this approach. The simple structure of the ILC in implementation and low cost in computation are other crucial factors to recommend the ILC. It does not totally depend on the system model during the design procedure. Therefore, it has relatively higher robustness to perturbation and modeling errors than other control methods for repetitive tasks.