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Low vibration and noise level in internal combustion engines has become an essential part of the design process. It is well known that the piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. The piston secondary motion and piston-bore contact pattern are critical in piston design because they affect the skirt-to-bore impact force and therefore, how the piston impact excitation energy is damped, transmitted and eventually radiated from the engine structure as noise. An analytical method is presented in this paper for simulating piston secondary dynamics and piston-bore contact for an asymmetric half piston model. The method includes several important physical attributes such as bore distortion effects due to mechanical and thermal deformation, inertia loading, piston barrelity and ovality, piston flexibility and skirt-to-bore clearance. The method accounts for piston kinematics, rigid-body dynamics and flexibility.

Flame speed data reported in most literature are acquired in conventional apparatus such as the spherical combustion bomb and counterflow burner, and are limited to atmospheric pressure and ambient or slightly elevated unburnt temperatures. As such, these data bear little relevance to internal combustion engines and gas turbines, which operate under typical pressures of 10-50 bar and unburnt temperature up to 900K or higher. These elevated temperatures and pressures not only modify dominant flame chemistry, but more importantly, they inevitably facilitate pre-ignition reactions and hence can change the upstream thermodynamic and chemical conditions of a regular hot flame leading to modified flame properties. This study focuses on how auto-ignition chemistry affects flame propagation, especially in the negative-temperature coefficient (NTC) regime, where dimethyl ether (DME), n-heptane and iso-octane are chosen for study as typical fuels exhibiting low temperature chemistry (LTC).

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

Real-time measurement or estimation of crank-angle-resolved engine cylinder pressure may become commonplace in the next generation of engine controllers to optimize spark, valve timing, or compression ratio. Toward the development of a real-time cylinder pressure estimator, this work presents a crank-angle-resolved engine cylinder pressure estimation model that could accept inputs such as speed, manifold pressure and throttle position, and deliver crank-angle resolved cylinder pressure in real-time, at engine speeds covering the useful operating range of most engines. The model was validated by comparing simulated cylinder pressure with thirteen sets of cylinder pressure data, from two different commercial engines from two different OEMs. Estimated pressures were compared against the actual measured pressure traces. The average relative error is about 3% while the maximum relative error is 5%. Both can be improved with further tuning.

This work analyzes a cantilevered piezoelectric beam device for harvesting energy from the simultaneous rotation and translational vibration of vehicle wheels. The device attaches to the wheel rim so that it displaces tangentially during operation. A lumped-parameter analytical model for the coupled electromechanical system is derived. The device has one natural frequency that is speed-dependent because of centripetal acceleration affecting the total stiffness of the device. Even though the device has one natural frequency, it experiences three resonances as the rotation speed varies. One resonance occurs when the rotation speed coincides with the speed-dependent natural frequency of the device. The other two resonances are associated with excitations from the vibration of the vehicle wheel. The device’s parameters are chosen so that these three resonances occur when the wheel travels near 30 mph, 55 mph, and 70 mph.

In Aluminum Alloy, AA, sheet metal forming, the through thickness cracking at the edge of cut out is one of the major fracture modes. In order to prevent the edge cracking in production forming process, practical edge stretch limit criteria are needed for virtual forming prediction and early stamping trial evaluations. This paper proposes new methods for determining the edge stretching limit of the sheet coupons, with and without pre-stretching, based on the Digital Image Correlation (DIC) technique. A numbers of sets of notch-shaped smaller coupons with three different pre-stretching conditions (near 5%, 10% and fractured) are cut from the prestretched large specimens. Then the notch-shaped smaller coupons are stretched by uniaxial tension up to through edge cracking observed. A dual-camera 3D-DIC system is utilized to measure both coupon face strain and thickness strain in the notch area at the same time.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

Vehicle weight reduction is a significant challenge for the modern automotive industry. In recent years, the amount of vehicular components constructed from aluminum alloy has increased due to its light weighting capabilities. Automotive manufacturing processes, predominantly those utilizing various stamping applications, require a thorough understanding of aluminum fracture predictions methods, in order to accurately simulate the process using Finite Element Method (FEM) software or use it in automotive engineering manufacture. This paper presents the strain distribution of A5182 aluminum samples after punch impact under various conditions by Digital Image Correlation (DIC) system, its software also measured the complete strain history, in addition to sample curvature after it was impacted; therefore obtaining the data required to determine the amount of side-wall-curl (Aluminum sheet springback) present after formation.

The heat generation rate of a lithium ion cell was estimated using a reversible heat generation rate equation. Because the equation is based on the energy conservation law, the influence of kinetically slow processes should be considered. In this analysis, the influence of kinetically slow processes is present but it is small within the domain of the test measurements. This approximation can be of significant usefulness for modeling the thermal response of single cells and multi-cell batteries.

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.

With increasing demand for cost-effective fuel cells, it is essential to investigate alternative materials for components of the fuel cells. The objective of this paper is to implement elastomeric materials (silicon rubber) for the use of bipolar plates in a polymer electrolyte membrane fuel cell. Two different types of conductive fillers, a graphite fiber and flake, were added at different concentrations to a two-component silicone rubber slurry. Electrical, thermal and mechanical properties of the composite material were investigated. Comparable electrical and thermal conductivities were achieved to that of commercially available plates. The silicone rubber based composite material maintained elastomeric properties for improved sealing of cell fluid reactants and products.

Self-piercing riveting (SPR) has been used in production to join sheet materials since the early 1990s. A large amount of experimental trial work was required in order to determine an appropriate combination of rivet and anvil design to fulfill the required joint parameters. The presented study is describing the methodology of SPR joint design based on numerical simulation and experimental methods of defining required simulation input parameters. The required inputs are the stress-strain curves of sheet materials and rivets for the range of strains taking place in the SPR joining process, parameters required for a fracture model for all involved materials, and friction parameters for all interfaces of SPR process. In the current study, the normalized Cockroft-Latham fracture criterion was used for predicting fracture. Custom hole and tube expansion tests were used for predicting fracture of the riveted materials and the rivet, respectively.

In recent years, implementation of dual phase (DP) Advanced High Strength Steels (AHSS) and Ultra High Strength Steels (UHSS) is increasing in automotive components due to their superior structural performance and vehicle weight reduction capabilities. However, these materials are often sensitive to trimmed edge cracking if stretching along sheared edge occurs in such processes as stretch flanging. Tool wear is another major issue in the trimming of UHSS because of higher contact pressures at the interface between cutting tools and sheet metal blank caused by UHSS’s higher flow stresses and the presence of a hard martensitic in the microstructure. The objective of the present paper is to discuss the methodology of analyzing die wear for trimming operations of UHSS components and illustrate it with some examples of tool wear analysis for trimming 1.5mm thick DP980 steel.

Presented are analytical and experimental results for both the conventional trimming process and a recently developed robust trimming process, which involves dulling the upper trimming tool and providing elastic offal support. The robust process, which has strong potential to lower the requirements for the accuracy of trim die alignment, is analyzed. Material flow of the trimming process is modeled numerically using the commercially available LS-Dyna finite element program and an in-house finite element program, called Solid 2D. An experimental technique, which provides plane strain material deformation data as a function of hydrostatic pressure has been developed. Experimental results from the plane strain FLD test and a single interrupted trimming test were obtained in order to find agreement between analytical and experimental results. Analysis of the mechanisms of blank separation in conventional trimming and trimming with an elastic scrap support is also provided.

As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB.

Two popular critical plane models developed by Fatemi-Socie and Smith-Watson-Topper were derived from the experimental observations of the nucleation and growth of cracks during loading. The Fatemi-Socie critical plane model is applicable for the life prediction of materials for which the dominant failure mechanism is shear crack nucleation and growth, while the Smith-Watson-Topper model, for materials that fail predominantly by crack growth on planes perpendicular to the planes of maximum tensile strain or stress. The two critical plane models have been validated primarily by in-phase and 90° out-of-phase loading, and few, on the complex, non-proportional loading paths. A successful critical plane model should be able to predict both the fatigue life and the dominant failure planes. However, some experimental studies indicate the 304 stainless steel has the two possible failure modes, shear and tensile failure dominant, depending on the loading mode and stress and strain states.

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.

This paper explores the trade-off between reliability-based design and robustness for an automotive under-hood thermal system using the iSIGHT-FD environment. The interaction between the engine cooling system and the heating, ventilating, and air-conditioning (HVAC) system is described. The engine cooling system performance is modeled using Flowmaster and a metamodel is developed in iSIGHT. The actual HVAC system performance is characterized using test bench data. A design of experiment procedure determines the dominant factors and the statistics of the HVAC performance is obtained using Monte Carlo simulation (MCS). The MCS results are used to build an overall system response metamodel in order to reduce the computational effort. A multi-objective optimization in iSIGHT maximizes the system mean performance and simultaneously minimizes its standard deviation subject to probabilistic constraints.

Bendability is a critical characteristic of sheet metal during the stamping process in automobile industry. Bending operation plays an important role in the panels forming of vehicles. In this study, the recently developed “Incremental Bending” method was utilized to evaluate the ambient bendability of 7xxx series avoiding bending crack. A 3D digital image correlation (DIC) measurement system is improved to capture the displacement and strain information on the stretched side of the sheet samples. The background, experimental method and data post-procedure are introduced in detail. After several sequential images acquisition and data processing, the major strain histories on the stretch zone of the samples are measured. With different bending process and parameters, the location of peak strain and the surface major strain distribution were evaluated as a function of R/T ratio (the inner radius over sheet thickness).

The Press-in-Place (PIP) gasket is a static face seal with self-retaining feature, which is used for the mating surfaces of engine components to maintain the reliability of the closed system under various operating conditions. Its design allows it to provide enough contact pressure to seal the internal fluid as well as prevent mechanical failures. Insufficient sealing pressure will lead to fluid leakage, consequently resulting in engine failures. A test fixture was designed to simulate the clamp load and internal pressure condition on a gasket bolted joint. A sensor pad in combination with TEKSCAN equipment was used to capture the overall and local pressure distribution of the PIP gasket under various engine loading conditions. Then, the test results were compared with simulated results from computer models. Through the comparisons, it was found that gasket sealing pressure of test data and CAE data shows good correlations in all internal pressure cases when the bolt load was 500 N.