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The 2013 SRT Viper Carbon Fiber X-Brace, styled by Chrysler's Product Design Office (PDO), is as much of a work of art as it is an engineered structural component. Presented in this paper is the design evolution, development and performance refinement of the composite X-Brace (shown in Figure 1). The single-piece, all Carbon Fiber Reinforced Plastic (CFRP) X-Brace, an important structural component of the body system, was developed from lightweight carbon fiber material to maximize weight reduction and meet performance targets. The development process was driven extensively by virtual engineering, which applied CAE analysis and results to drive the design and improve the design efficiency. Topology optimization and section optimization were used to generate the initial design's shape, form and profile, while respecting the package requirements of the engine compartment.

Environmental concerns and government regulations are factors that have led to an increased focus on fuel economy in the automotive industry. This paper identifies a method used to improve the efficiency of a front-wheel-drive (FWD) automatic transmission. In order to create improvements in large complex systems, it is key to have a large scope, to include as much of the system as possible. The approach taken in this work was to use Design for Six Sigma (DFSS) methodology. This was done to optimize as many of the front-wheel-drive transmission components as possible to increase robustness and efficiency. A focus of robustness, or consistency in torque transformation, is as important as the value of efficiency itself, because of the huge range of usage conditions. Therefore, it was necessary to find a solution of the best transmission component settings that would not depend on specific usage conditions such as temperatures, system pressures, or gear ratio.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

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.

In order to take into account the local material non-linear elastic-plastic effects generated by notches, Glinka proposed the equivalent strain energy density (ESED) Criterion which has been widely accepted and used in fatigue theory and calculation for the last few decades. In this paper, Glinka's criterion is applied to structural optimization design for elastic-plastic correction to consider material non-linear elastic-plastic effects. The equivalent (fictitious) stress was derived from Glinka's Criterion equation for the commonly used Ramberg-Osgood and bi-linear stress and strain relationships. This equivalent stress can be used as the stress boundary constraint threshold in structural optimization design to control the elastic-plastic stress or strain in nonlinear optimization.

Interference assessments of a stepped-radius power-train component moving within a deformed stepped bore often arise during engine and transmission development activities. For example, when loads are applied to an engine block, the block distorts. This distortion may cause a cam or crankshaft to bind or wear prematurely in its journals as the part rotates within them. Within an automatic transmission valve body, care must be taken to ensure valve body distortion under oil pressure, assembly, and thermal load does not cause spool valves to stick as they translate within the valve body. In both examples, the mechanical scenario to be assessed involves a uniform or stepped radius cylindrical part maintaining a designated clearance through a correspondingly shaped but distorted bore. These distortions can occur in cross-sections (“out-of-round”) or along the bore (in an “s” or “banana” shaped distortions).

There is a continual need to apply heat treatment processes in innovative ways to optimize material performance. One such application studied in this research is carburizing followed by austempering of low carbon alloy steels, AISI 8620, AISI 8822 and AISI 4320, to produce components with high strength and toughness. This heat treatment process was applied in two steps; first, carburization of the surface of the parts, second, the samples were quenched from austenitic temperature at a rate fast enough to avoid the formation of ferrite or pearlite and then held at a temperature just above the martensite starting temperature to partially or fully form bainite. Any austenite which was not transformed during austempering, upon further cooling formed martensite or was present as retained austenite.

The performance of door assembly is very significant for the vehicle design and door sag/set is one of the important attribute for design of door assembly. This paper provides an overview of conventional approach for door sag/set study based on door-hinge-BIW assembly (system level approach) and its limitation over new approach based on subassembly (subsystem level approach). The door sag/set simulation at system level is the most common approach adopted across auto industry. This approach evaluates only structural adequacy of door assembly system for sag load. To find key contributor for door sagging is always been time consuming task with conventional approach thus there is a delay in providing design enablers to meet the design target. New approach of door sag/set at “subsystem level” evaluates the structural stiffness contribution of individual subsystem. It support for setting up the target at subsystem level, which integrate and regulate the system level performance.

Carburized parts often see use in powertrain components for the automotive industry. These parts are commonly quenched and tempered after the carburizing process. The present study compared the austempering heat treatment to the traditional quench-and-temper process for carburized parts. Samples were produced from SAE 8620, 4320, and 8822 steels and heat treated across a range of conditions for austempering and for quench-and-tempering. Distortion was examined through the use of Navy C-Ring samples. Microstructure, hardness, and Charpy toughness were also examined. X-ray diffraction was used to compare the residual stress found in the case of the components after the quench-and-temper and the austempering heat treatments. Austempering samples showed less distortion and higher compressive residual stresses, while maintaining comparable hardness values in both case and core. Toughness measurements were also comparable between both processes.

Physical modeling has been used by the industry to improve development time and produce a quality product. In this paper, we will describe two methods used in system control to take advantage of the physical model. One method describes a complete transmission physical model with a full system control utilizing co-simulation techniques. Data will be presented, and comparison to vehicle data will be conducted and verified. The second method will illustrate how to utilize the physical model to improve system design and modification. In this method, vehicle data will be used as inputs to the model, the model output will be verified against vehicle output data. The two methods are excellent tools for the Design For Six Sigma process (DFSS design).

In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

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].

Generic spindle loads are used in the upfront analysis for vehicle durability development. They represent different load case into the vehicle suspension system, such as potholes, cornering, and braking. The advantage of using these generic load cases is that they can be used upfront in the durability development process before hardware is available. The generic spindle loads are cascaded through the suspension system to generate component loads which can then be used for stress analysis. The paper describes a study that was done to determine the validity of current generic spindle loads by analyzing spindle data from multiple vehicles in the same class. The paper will explain the initial data analysis that was done, which was normalizing the spindle loads by weight. In addition, the paper will then go into further detail on describing a relationship between spindle loads and tire sidewall height, which reduced the load scatter.

This paper presents the design of a hybrid powertrain damping control algorithm using the sliding mode control (SMC) scheme. Motor control-based active damping control strategy is used to ensure smooth drive line operation and provide the driver with seamless driving experience. In the case of active damping control, motor and engine speeds are measured to monitor the driveline state, and corrective motor torques are generated to dampen out drive line vibrations. Drive lines are prone to internal vibration (engine, clutches and motors) as well as external disturbances caused by road inputs. As such, fast-response actuator-based damping control systems are desirable in a hybrid powertrain application, where a torque converter is generally not used. The most significant aspect of an active damping control algorithm is the error calculation, based on proper states information, and torque determination based on the adaptive control gain applied to the nonlinear system.

Strain gages have been widely used for measuring strain or deformation. They are very reliable and accurate. However, for application on fabric material, strain gages have their limitations. In this paper, digital image correlation (DIC) is used to measure the deformation around the rear window on a convertible top. The test needed to be non destructive, the vehicle and convertible top could not be damaged. The deformation or strain measured on the fabric was used to estimate the force experienced at the interface between the glass and the fabric during an opening/closing application. A speckle pattern was created on the convertible fabric where deformation was to be measured with washable paint. The image of the measured area was first recorded. The convertible top was then latched down and the fabric was stretched. A second image was recorded again. Based on the two images, the deformation/strain between the two conditions was measured.

Regression models are used to understand the relative fatality risk for drivers in front-front and front-left crashes. The field accident data used for the regressions were extracted by NHTSA from the FARS database for model years 2000-2007 vehicles in calendar years 2002-2008. Multiple logistic regressions are structured and carried out to model a log-linear relationship between risk ratio and the independent vehicle and driver parameters. For front-front crashes, the regression identifies mass ratio, belt use, and driver age as statistically significant parameters (p-values less than 1%) associated with the risk ratio. The vehicle type and presence of the ESC are found to be related with less statistical significance (p-values between 1% and 5%). For front-left crashes the driver risk ratio is also found to have a log-log linear relationship with vehicle mass ratio.

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.

This paper introduces a methodology to calculate confidence bounds for a normal and Weibull distribution using Monte Carlo pivotal statistics. As an example, a ready-to-use lookup table to calculate one-sided lower confidence bounds is established and demonstrated for normal and Weibull distributions. The concept of one-sided lower tolerance limits for a normal distribution was first introduced by G. J. Lieberman in 1958 (later modified by Link in 1985 and Wei in 2012), and has been widely used in the automotive industry because of the easy-to-use lookup tables. Monte Carlo simulation methods presented here are more accurate as they eliminate assumptions and approximations inherent in existing approaches by using random experiments. This developed methodology can be used to generate confidence bounds for any parametric distribution. The ready-to-use table for the one-sided lower tolerance limits for a Weibull distribution is presented.

This paper will define the process for correlating fatigue based customer duty cycle with laboratory bench test data. The process includes the development of the Median and Design Load-Life curve equations. The Median Load-Life curve is a best fit linear regression; whereas, the Design Load-Life curve incorporates component specific reliability and confidence targets. To account for the statistical distribution of fatigue life, due to sample size, the one-side lower-bound tolerance limit method ( Lieberman, 1958 ) will be utilized. This paper will include a correlation between the predicted design fatigue life and the actual product life.

Advanced high strength steels (AHSS) offer a good balance of strength, durability, crash energy absorption and formability. Applications of AHSS for lightweight designs of automotive structures are accelerating in recent years to meet the tough new CAFE standard for vehicle fuel economy by 2025. At the same time, the new generation pickup cargo box is to be designed for a dramatic increase in payload. Upgrading the box material from conventional mild steels to AHSS is necessary to meet the conflicting requirements of vehicle light weighting and higher payload. In this paper, typical AHSS grades such as DP590 and DP780 were applied to selected components of the pickup cargo box for weight reduction while meeting the design targets for fatigue, strength and local stiffness.