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A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

The reduction of automotive emissions is instrumental in the fight against air pollution and its impact on global warming. This realization has empowered governments around the world to mandate lower levels of vehicle emissions requiring the Original Equipment Manufacturers (OEMs) to implement advanced aftertreatment technologies in their applications. Achieving emission levels as low as SULEV30 or SULEV20 would have been impossible only a couple of decades ago, however, these lower levels of emissions are now a possibility through advanced control strategies and aftertreatment systems. As a part of this mandate to lower emissions, OEMs are also continuously monitoring the health and performance of their aftertreatment and control components. The implementation of On Board Diagnostics (OBD) ensures that control systems are functioning robustly and the emission levels are achieved and maintained to high mileages for the life of the vehicle.

Engine air induction shell noise is a structure borne noise that radiates from the surface of the air induction system. The noise is driven by pulsating engine induction air and is perceived as annoying by vehicle passengers. The problem is aggravated by the vehicle design demands for low weight components packaged in an increasingly tight under hood environment. Shell noise problems are often not discovered until production intent parts are available and tested on the vehicle. Part changes are often necessary which threatens program timing. Shell noise should be analyzed in the air induction system design phase and a good shell noise analytical process and targets must be defined. Several air induction clean side ducts are selected for this study. The ducts shell noise is assessed in terms of material strength and structural stiffness. A measurement process is developed to evaluate shell noise of the air induction components. Noise levels are measured inside of the clean side ducts.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

Lithium-ion (Li-ion) batteries and issues related to their thermal management and safety have been attracting extensive research interests. In this work, based on a recent thermal chemistry model, the phenomena of thermal runaway induced by a transient internal heat source are computationally investigated using a three-dimensional (3D) model built in COMSOL Multiphysics 5.3. Incorporating the anisotropic heat conductivity and typical thermal chemical parameters available from literature, temperature evolution subject to both heat transfer from an internal source and the activated internal chemical reactions is simulated in detail. This paper focuses on the critical runaway behavior with a delay time around 10s. Parametric studies are conducted to identify the effects of the heat source intensity, duration, geometry, as well as their critical values required to trigger thermal runaway.

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.

This paper proposes a domain-centralized powertrain E/E (electrical and/or electronic) architecture for all-electric vehicles that features: a powerful master controller (domain controller) that implements most of the functionality of the domain; a set of smart actuators for electric motor(s), HV (High Voltage) battery pack, and thermal management; and a gateway that routes all hardware signals, including digital and analog I/O, and field bus signals between the domain controller and the rest of the vehicle that is outside of the domain. Major functional safety aspects of the architecture are presented and a safety architecture is proposed. The work represents an early E/E architecture proposal. In particular, detailed partitioning of software components over the domain’s Electronic Control Units (ECUs) has not been determined yet; instead, potential partitioning schemes are discussed.

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.

Due to magnesium alloy's poor weldability, other joining techniques such as laser assisted self-piercing rivet (LSPR) are used for joining magnesium alloys. This research investigates the fatigue performance of LSPR for magnesium alloys including AZ31 and AM60. Tensile-shear and coach peel specimens for AZ31 and AM60 were fabricated and tested for understanding joint fatigue performance. A structural stress - life (S-N) method was used to develop the fatigue parameters from load-life test results. In order to validate this approach, test results from multijoint specimens were compared with the predicted fatigue results of these specimens using the structural stress method. The fatigue results predicted using the structural stress method correlate well with the test results.

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

Recently the use of digital 3D profilometry in the automotive industries has become increasingly popular. The effective techniques for 3D shape measurement, especially for the measurement of complicated structures, have become more and more significant. Different optical inspective methods, such as 3D profilometry, laser scanning and Coordinate-Measuring Machine (CMM), have been applied for 3D shape measurement. Among these methods, 3D profilometry seems to be the fastest and inexpensive method with considerably accurate result, and it has simple setup and full field measuring ability compared with other techniques. In this paper, a novel calibration method for 3D-profilometry will be introduced. In this method, a multiple-step calibration procedure is utilized and best-fit calibration curves are obtained to improve measurement accuracy. A recursive algorithm is used for data evaluation, along with calibration data.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

This paper presents a novel Kalman filter based road grade estimation method using measurements from an accelerometer, a gyroscope and a velocity sensor. The accelerometer measures the longitudinal proper acceleration of the vehicle, and the accelerometer measurement is almost drift free but it is heavily corrupted by the accelerometer noise. The gyroscope measures the pitch rate of the vehicle, and the gyroscope measurement is quite clean but it is substantially disturbed by the gyroscope bias. The velocity sensor measures the longitudinal velocity of the vehicle, and the velocity sensor measurement is also considerably corrupted by the measurement noise. The developed Kalman filter based estimation method uses the models of the sensors and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The simulation results show that the developed method is very effective in producing an accurate road grade estimate.

The demand for vehicles with electrified powertrain systems is increasing due to government regulations on fuel economy. The battery systems in a PHEV (Plug-in Hybrid-electric Vehicle) have achieved tremendous efficiency over past few years. The system has become more delicate and complex in architecture which requires sophisticated thermal management. Primary reason behind this is to ensure effective cooling of the cells. Hence the current work has emphasized on developing a “Physics based” thermal management modeling framework for a typical battery system. In this work the thermal energy conservation has been analyzed thoroughly in order to develop necessary governing equations for the system. Since cooling is merely a complex process in HEV battery systems, the underlying mechanics has been investigated using the current model. The framework was kept generic so that it can be applied with various architectures. In this paper the process has been standardized in this context.

Swirling flows are very dominant in applied technical problems, especially in IC engines, and their prediction requires rather sophisticated modeling. An adaptive low-pass filtering procedure for the modeled turbulent length and time scales is derived and applied to Menter' original k - ω SST turbulence model. The modeled length and time scales are compared to what can potentially be resolved by the computational grid and time step. If the modeled scales are larger than the resolvable scales, the resolvable scales will replace the modeled scales in the formulation of the eddy viscosity; therefore, the filtering technique helps the turbulence model to adapt in accordance with the mesh resolution and the scales to capture.

In recent years, there is increasing demand for every CAE engineer on their confidence level of the virtual simulation results due to the upfront robust design requirement during early stage of an automotive product development. Apart from vehicle feel factor NVH characteristics, there are certain vibration target requirements at system or component level which need to be addressed during design stage itself in order to achieve the desired functioning during vehicle operating conditions. Vehicle passive safety system is one which primarily consists of acceleration sensors, control module and air-bag deployment system. Control module’s decision is based on accelerometer sensor signals so that its mounting locations should meet the sufficient inertance or dynamic stiffness performance in order to avoid distortion in signals due to its structural resonances.

The development of complex engineering systems often encounters various challenges in terms of meeting New Product Development (NPD) assigned budget, launch time, and system performance goals. Most of the NPD processes have been experiencing challenges to meet these goals within an increasingly competitive global market environment. These challenges become more complicated to manage when the development process is long with different sources of uncertainty. Despite decades of industrial experience and academic research efforts in managing NPD processes, it is observed that designing and developing increasingly complex systems, e.g., automotive, is still subjected to significant cost overrun, schedule delays, and functional issues during early design stages. To provide a Reliability Growth Planning (RGP) model, several inputs are required, e.g., the initial reliability estimation, the reliability goal, test recourses, and the duration of the design or test period.

The characterization of a turbulent diesel spray combustion process has been carried out in a divided chamber diesel system with optical accesses. Laser Doppler Anemometry, spectral extinction and flame intensity measurements have been performed from U.V., to visible from the start of injection to the end of combustion, at fixed air/fuel ratio and different engine speeds. Spatial distribution of fuel and vapor as well as the ignition location and soot distribution have been derived in order to study the mechanism of the air-fuel interaction and the combustion process. The analysis of results has shown that the high swirling motion transports the fuel towards the left part of the chamber and breaks up the jet into small droplets of different sizes and accelerates the fuel vaporization. Then, chemical and physical overlapped phases were observed during the ignition delay, contributing both to autoignition.