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Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Acoustical materials are widely used in automotive vehicles and other industrial applications. Two important parameters namely Sound Transmission Loss (STL) and absorption coefficient are commonly used to evaluate the acoustical performance of these materials. Other parameters, such as insertion loss, noise reduction, and loss factors are also used to judge their performance depending on the application of these materials. A systematic comparative study of STL and absorption coefficient was conducted on various porous acoustical materials. Several dozen materials including needled cotton fiber (shoddy) and foam materials with or without barrier/scrim were investigated. The results of STL and absorption coefficient are presented and compared. As expected, it was found that most of materials are either good in STL or good in absorption. However, some combinations can achieve a balance of performance in both categories.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Generally linear finite element analysis (FEA) is used to predict fatigue life of spot welded joints in a vehicle body structure. Therefore, the effect of plastic deformation at the vicinity of the spot welded joints is not included on fatigue prediction. This study introduces a simple technique to include the plastic deformation effect without performing elastic-plastic finite element analysis. The S-N curve obtained from fatigue test results is modified to consider this effect. Tensile strength test results of spot welded joint specimens were utilized to find the load range for FEA equivalent to the applied load range for fatigue tests. To demonstrate the proposed approach, fatigue test results of advanced high strength steels (AHSS) for lap-shear and coach peel specimens were used. Both the specimen types were tested at various constant amplitudes with the load ratios of R=0.1 and 0.3.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

The largest contribution to engine rubbing friction is made by the piston and piston rings running in the cylinder liner. The magnitude and characteristics of the friction behaviour and the influence on these of factors such as surface roughness, piston design and lubricant properties are of keen interest. Investigating presents experimental challenges, including potential problems of uncontrolled build-to-build variability when component changes are made. These are addressed in the design of a new motored piston and floating liner rig. The design constrains transverse movement of a single liner using cantilevered mounts at the top and bottom. The mounts and two high stiffness strain gauged load cells constrain vertical movement. The outputs of the load cells are processed to extract the force contribution associated with friction. The liner, piston and crankshaft parts were taken from a EuroV-compliant, HPCR diesel engine with a swept capacity of 550cc per cylinder.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

Among the various high strength steels available today, boron steels are finding increasing applications in bumper beams and other crash resistant structures, primarily for their high strength. However, to overcome the forming difficulty at room temperature and to achieve the microstructural changes needed for high strength, manufacturing of boron steel parts is done under hot forming conditions. In this study, the effect of three principal bumper design parameters, namely depth, thickness and corner radius on the formability of a hat section bumper beam was considered. Using a forming simulation program, 27 different combinations of these three design parameters were examined for forming limits, failure types and failure locations. The bumper beams were also examined for energy absorption in pendulum impact tests. Recommendations are made for the design of boron steel bumper beams based on both impact energy absorption and formability.

We present research in progress to develop and implement a transportable instrumentation package (TIP) to collect driver data in a vehicle. The overall objective of the project is to investigate the symbiotic relationship between humans and their vehicles. We first describe the state-of-art technologies to build the components of TIP that meet the criteria of ease of installation, minimal interference with driving, and sufficient signals to monitor driver state and condition. This method is a viable alternative to current practice which is to first develop a fully instrumented test vehicle, often at great expense, and use it to collect data from each participant as he/she drives a prescribed route. Another practice, as for example currently being used in the SHRP-2 naturalistic driving study, is to install the appropriate instrumentation for data collection in each individual's vehicle, often requiring several hours.

Exhaust gas recirculation (EGR) coolers are commonly used in diesel engines to reduce the temperature of recirculated exhaust gases in order to reduce NOx emissions. The presence of a cool surface in the hot exhaust causes particulate soot deposition as well as hydrocarbon and water condensation. Fouling experienced through deposition of particulate matter and hydrocarbons results in degraded cooler effectiveness and increased pressure drop. In this study, a visualization test setup is designed and constructed so that the effect of water condensation on the deposit formation and growth at various coolant temperatures can be studied. A water-cooled surrogate rectangular channel is employed to represent the EGR cooler. One side of the channel is made of glass for visualization purposes. A medium duty diesel engine is used to generate the exhaust stream.

ACOUSTOMIZE™ is a new method of acoustic evaluation used for the purpose of understanding and optimizing NVH performance of vehicles. The following paper documents a case study of the ACOUSTOMIZE™ test methodology on a passenger car BIW. This study includes an analysis of noise flow through BIW locations, a comparison of noise sound levels through BIW cavities with and without a sound treatment package and a comparison of the original cavity sealing design package consisting of baffles, tapes and baggies to low density polyurethane NVH Foam. The results of the study show detection of complex BIW pass throughs that the body leakage test (BLT) was not able to find. In addition, the data shows improved noise reduction with the low density polyurethane foam versus the original cavity sealing design package.

Accessory belt “chirp” noise is a major quality issue in the automotive and truck industry. Chirp noise control is often achieved by very tight pulley alignment, a guideline being .33 degree maximum belt entry angle into each grooved pulley. Occasionally belts will chirp at pulleys where the system alignment is this good or better. This study offers an explanation for such occurrences. This is a study to see if fundament groove side sticking theory correlates with the belt entry angle, and how the coefficient of friction relates to this entry angle. The study combines theory with lab data. In summary, the study fundamentally links the coefficient of friction of the belt to the belt chirp noise phenomenon, and allows the projection of a belt's general tendency to chirp to be predicted by the measurement of belt coefficient of friction on a test stand.

U.S. federal vehicle emission standards effective in 2007 require tight control of NOx and hydrocarbon emissions. For light-duty vehicles, the current standard of Tier 2 Bin 5 is about 0.07 g/mi NOx and 0.09 g/mi NMOG (non-methane organic gases) at 120,000 mi. However, the proposed future standard is 0.03 g/mi for NMOG + NOx (~SULEV30) at 150,000 mi. There is a significant improvement needed in catalyst system efficiencies for diesel vehicles to achieve the future standard, mainly during cold start. In this study, a less than 6000 lbs diesel truck equipped with an advanced urea Selective Catalytic Reduction (SCR) system was used to pursue lower tailpipe emissions with an emphasis on vehicle calibration and catalyst package. The calibration was tuned by optimizing exhaust gas recirculation (EGR) fuel injection and cold start strategy to generate desirable engine-out emissions balanced with reasonable temperatures.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

The helical spring is one of fundamental mechanical elements used in various industrial applications such as valves, suspension mechanisms, shock and vibration absorbers, hand levers, etc. In high speed applications, for instance in the internal combustion engine or in reciprocating compressor valves, helical springs are subjected to dynamic and impact loading, which can result in a phenomenon called “surge”. Hence, proper design and selection of helical springs should consider modeling the dynamic and impact response. In order to correctly characterize the physics of a helical spring and its response to dynamic excitations, a comprehensive model of spring elasticity for various spring coil and wire geometries, spring inertial effects as well as contacts between the windings leading to a non-linear spring force behavior is required. In practical applications, such models are utilized in parametric design and optimization studies.

Design and development of an electrohydraulically actuated gas sampling valve is presented for use in auto engine combustion studies. The valve was developed with particular emphasis on sampling within the vicinity of the wall quench layer, requiring minimum leakage rates to avoid sample contamination and flush seating of the valve-stem to valve-seat to avoid perturbations of the wall layer. Response in the range of 0.4 to 1.0 milliseconds is attainable for variable valve lifts measured between 0.01 to 0.30 mm while using a net sealing force of approximately 750N. Gas leakage rates ranged from 0.05% to 1% of the sample mass flow rate when sampling from estimated distances from the wall of 0.3 mm to 0.03 mm, respectively, at a cylinder pressure of 10 bar. The gas sampling valve is presently coupled to a gas chromatograph to measure concentrations of major species components.