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A cargo box is a key structure in a pickup truck which is used to hold various items. Therefore, a cargo box must be durable and robust under different ballast conditions when subjected to road load inputs. This paper discusses a Design for Six Sigma (DFSS) approach to improve the durability of cargo box panel in its early development phase. Traditional methods and best practices resulted in multiple iterations without an obvious solution. Hence, DFSS tools were proposed to find a robust and optimum solution. Key control factors/design parameters were identified, and L18 Orthogonal Array was chosen to optimize design using CAE tools. The optimum design selected was the one with the minimum stress level and the least stress variation. This design was confirmed to have significant improvement and robustness compared to the initial design. DFSS identified load paths which helped teams finally come up with integrated shear plate to resolve the durability concern.

The test cycle average drag coefficient is examined for the variation of allowable EPA coastdown ambient conditions. Coastdown tests are ideally performed with zero wind and at SAE standard conditions. However, often there is some variability in actual ambient weather conditions during testing, and the range of acceptable conditions is further examined in detail as it pertains to the effect on aerodynamic drag derived from the coastdown data. In order to “box” the conditions acceptable during a coastdown test, a sensitivity analysis was performed for wind averaged drag (CD¯) as well as test cycle averaged drag coefficients (CDWC) for the fuel economy test cycles. Test cycle average drag for average wind speeds up to 16 km/h and temperatures ranging from 5C to 35C, along with variation of barometric pressure and relative humidity are calculated. The significant effect of ambient cross winds on coastdown determined drag coefficient is demonstrated.

This paper will explore the effect that non-constant function CD (as observed during wind tunnel testing) would have on the coastdown derived drag coefficient and other regulatory drive cycles. It is common in wind tunnel testing to observe road vehicle drag coefficients that vary with speed. These varying CD values as a function of velocity will be expressed as CD(V) in this paper. Wind tunnel testing for product development is generally conducted at 110 km/h (68.3 mph) which are similar speeds and typical of the United States (US), European, and Asian highway speeds. Reported values of CD are generally gathered at these speeds. However, coastdown testing by definition takes place over a large range of speeds mostly lower than the wind tunnel test speeds. This paper will explore the effect that six typical functions of CD(V) have on the coastdown derived CD. One of the six functions is a constant, to represent a wind tunnel reported CD.

Developing lightweight, stiff and crash-resistant vehicle body structures requires a balance between part geometry and material properties. High strength materials suitable for crash resistance impose geometry limitations on depth of draw, radii and wall angles that reduce geometric efficiency. The introduction of 3rd generation Advanced High Strength Steels (AHSS) can potentially change the relationship between strength and geometry and enable simultaneous improvements in both. This paper will demonstrate applicability of 3rd generation AHSS with higher strength and ductility to replace the 780 MPa Dual Phase steel in a sill reinforcement on the current Jeep Cherokee. The focus will be on formability, beginning with virtual simulation and continuing through a demonstration run on the current production stamping tools and press.

Every passing year automotive engineers are challenged to attain higher fuel economy and improved emission targets. One widely used approach is to use Cooled Exhaust Gas Recirculation (CEGR) to meet these objectives. Apart from reducing emissions and improving fuel economy, CEGR also plays a significant role in knock mitigation in spark ignited gasoline engines. Generally, CEGR is introduced into the intake manifold in SI gasoline engine. Even though the benefits of using CEGR are significant, they can be easily negated by the uneven CEGR flow distribution between the cylinders, which can result in combustion instability. This paper describes the application of co-simulation between one and three dimensional tools to accurately predict the distribution of CEGR to the cylinders and the effect of its distribution on engine performance.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

There are several commercial off-the-shelf software available to study transmission and driveline dynamics. Many of these software require a faithful representation of the transmission topology in order to carry out the analyses. These modeling techniques utilize several redundant degrees of freedom which may not be necessary for studying low frequency (< ~30 Hz) dynamics and may be computationally inefficient. The dual-clutch model has been proposed as a generic 2-DOF model that overcomes some of these drawbacks. In this paper, the dual-clutch model is initially derived from first principles, starting with the equations of motion for a planetary automatic transmission. The model coefficients - the inertia matrix and the matrix of clutch coefficients - are then derived using a more intuitive approach based on energy considerations.

The trend to simultaneously improve fuel economy and engine performance has led to industry growth of turbocharged engines and as a result, the need to address their undesirable airborne noise attributes. This presents some unique engineering challenges as higher customer expectations for Noise Vibration Harshness (NVH), and other vehicle-level attributes increase over time. Turbocharged engines possess higher frequency noise content compared to naturally aspirated engines. Therefore, as an outcome, whoosh noise in the Air Induction System (AIS) during tip in conditions is an undesirable attribute that requires high frequency attenuation enablers. The traditional method for attenuation of this type of noise has been to use resonators which adds cost, weight and requires packaging space that is often at a premium in the under-hood environment.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

Dimensional Engineering concentrates effort in the early design phases to meet the dimensional build objectives in automotive production. Design optimization tools include tolerance stack up, datum optimization, datum coordination, dimensional control plans, and measurement plans. These tools are typically based on the assumption that parts are rigid and tooling dimensions are perfect. These assumptions are not necessarily true in automotive assemblies of compliant sheet metal parts on high volume assembly lines. To address this issue, Finite Element Analysis (FEA) has been increasingly used to predict the behavior of imperfect and deformable parts in non-nominal tooling. This paper demonstrates an application of this approach. The complete analysis is divided into three phases. The first phase is a nominal design gravity analysis to validate the nominal design and tooling.

One of the key inputs 1-D transient simulation takes is a detailed front end cooling flow map. These maps that are generated using a full vehicle Three-dimensional Computational Fluid Dynamics (3D CFD) model require expensive computational resources and time. This paper describes how an adaptive sampling of the design space allowed the reduction of computational efforts while keeping desired accuracy of the analysis. The idea of the method was to find a pattern of Design of Experiments (DOE) sampling points for 3D CFD simulations that would allow a creation of an approximation model accurate enough to predict output parameter values in the entire design space of interest. Three procedures were implemented to get the optimal sampling pattern.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

Threaded fasteners or bolted joints are used extensively in automotive assemblies. There are standard procedures to evaluate joint performance under block cycles or road loads. The deciding load case for such joint design is slippage analysis of the joint. There are studies done to evaluate the theoretical and experimental behavior of these joints. There are different ways of understanding the interaction between the bolt and the nut under different loading scenarios. However, none have provided a satisfactory method of quantifying bolt loosening or loss of clamp load under cyclic loading, where no slippage is observed. Under varying loads, initial relaxation of the joint is followed by a loss of clamping load. Below a critical value, complete loss of clamping load progresses very rapidly and this results in a loose joint.

This paper describes an experimental study on the performance of self-piercing riveted (SPR) joints and adhesively bonded SPR joints connecting steel and aluminum components under both quasi-static and cyclic loading. The joint configurations cover a wide range of material gauges, types and grades. Two and three thickness joints, with and without adhesive are also part of this study. Load versus deflection behavior, load carrying capacity, fatigue life and the failure modes for each type of joint are discussed. This study focuses on the influence of dissimilar material and adhesives to the joint performance.

The purpose of this paper is to provide a comparison of multiaxial fatigue limit models and their correlation to experimental data. This paper investigates equivalent stress, critical plane and invariant-based multiaxial fatigue models. Several methods are investigated and compared based on ability to predict multiaxial fatigue limits from data published in literature. The equivalent stress based model developed by Lee, Tjhung and Jordan (LTJ), provides very accurate predictions of the fatigue limit under multiaxial loading due to its ability to account for non-proportional loading. This accuracy comes from the model constant which is calculated based on multiaxial fatigue data. This is the only model investigated that requires multiaxial fatigue testing to generate the model parameters. All other models rely on uniaxial test results.

It is a challenging task to find an optimal design concept for a truck frame structure given the complexity of loading conditions, vehicle configurations, packaging and other requirements. In addition, there is a great emphasis on light weight frame design to meet stringent emission standards. This paper provides a framework for fast and efficient development of a frame structure through various design phases, keeping durability in perspective while utilizing various weight reduction techniques. In this approach frame weight and stiffness are optimized to meet strength and durability performance requirements. Fast evaluation of different frame configurations during the concept phase (I) was made possible by using DFSS (Design for Six Sigma) based system synthesis techniques. This resulted in a very efficient frame ladder concept selection process.

Investigation into fuel system warranty has led to the need to develop cost effective, robust materials that are resistant to both fuel and aggressive cleaners. Acetal, chemically known as polyoxymethylene (POM), is the current material that is used universally by OEM’s throughout the fuel system for its excellent performance in fuel and relatively low cost, but lacks resistance to strong acidic solutions. Acid containing wheel cleaning solutions are increasingly being used by customers to clean their aluminum and magnesium wheels. Due to the proximity of the fuel modules to the wheel openings, acidic wheel cleaners chemically attack the POM resulting in cracks. The team worked closely with suppliers in recent years to develop cost effective, acid resistant POM materials that can withstand the stress-cracking at severe acid concentrations and meet the functional requirements.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

As automotive emissions become more stringent, accurate control of three-way catalyst temperature is increasingly important for maintaining high levels of conversion efficiency as well as preventing damage to the catalyst. A real-time catalyst temperature model provides critical information to the engine control system. In order to improve emissions and ensure regulatory compliance over a wide range of speed-load conditions, it is desirable to use modelled catalyst temperature as the primary input to catalyst efficiency control strategies. This requirement creates a challenge for traditional empirical models designed for component protection at high speed-load conditions. Simulation results show that a physics aligned model can estimate temperature in all operating conditions, including: cold-start, extended idle, engine shutdown, stop-start events, deceleration fuel shut-off, as well as traditional high load and part load points.