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This research assesses the integration of different levels of electric vehicles (EVs) in the distribution system and observes its impacts on environmental emissions and power system operational costs. EVs can contribute to reducing the environmental emission from two different aspects. First, by replacing the traditional combustion engine cars with EVs for providing clean and environment friendly transportation and second, by integrating EVs in the distribution system through the V2G program, by providing power to the utility during peak hours and reducing the emission created by hydrocarbon dependent generators. The PG&E 69-bus distribution system (DS) is used to simulate the integration of EVs and to perform energy management to assess the operational costs and emissions. The uncertainty of driving patterns of EVs are considered in this research to get more accurate results.

High-fidelity finite element (FE) tire-snow interaction models have the advantage of better understanding the physics of the tire-snow system. They can be used to develop semi-analytical models for vehicle design as well as to design and interpret field test results. For off-terrain conditions, there is a high level of uncertainties inherent in the system. The FE models are computationally intensive even when uncertainties of the system are not taken into account. On the other hand, field tests of tire-snow interaction are very costly. In this paper, dynamic metamodels are established to interpret interaction forces from FE simulation and to predict those forces by using part of the FE data as training data and part as validation data. Two metamodels are built based upon the Krieging principle: one has principal component analysis (PCA) taken into account and the other does not.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

Due to their high energy density and low self-discharge rates, lithium-ion batteries are becoming the favored solution for portable electronic devices and electric vehicles. Lithium-Ion batteries require special charging methods that must conform to the battery cells' power limits. Many different charging methods are currently used, some of these methods yield shorter charging times while others yield more charge capacity. This paper compares the constant-current constant-voltage charging method against the time pulsed charging method. Charge capacity, charge time, and cell temperature variations are contrasted. The results allow designers to choose between these two methods and select their parameters to meet the charging needs of various applications.

This work illustrates the potential of Electric Vehicles (EVs) as a grid support tool that will lower carbon emissions from both the energy production sector and the transportation sector. EVs can provide peak shaving power to the grid while discharging and valley filling power while charging to flatten the total load curve of a distribution system. The idea is called Vehicle to Grid (V2G). Flattening the load curve will allow utility providers to delay upgrading, or the purchase of new power generation stations, as well as best utilize renewable energy resources that may be uncontrollable in nature. Electrical energy production and transportation combined accounted for 2,534 million metric tons of carbon dioxide emissions in the US in 2019. Utilizing EVs for transportation as well as grid support will decrease this figure in each sector. This technology may pave the way to cleaner, more reliable, cost effective energy systems.

Durability of automotive structures is a primary engineering consideration that is evaluated during a vehicle's design and development. In addition, it is a basic expectation of consumers, who demand ever-increasing levels of quality and dependability. Automakers have developed corporate requirements for vehicle system durability which must be met before a products is delivered to the customer. To provide early predictions of vehicle durability, prior to the construction and testing of prototypes, it is necessary to predict the forces generated in the vehicle structure due to road inputs. This paper describes an application of the “virtual proving ground” approach for vehicle durability load prediction for a vehicle on proving ground road surfaces. Correlation of the results of such a series of simulations will be described, and the modeling and simulation requirements to provide accurate simulations will be presented.

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.

Countless articles and publications3,4,5 have documented and proven the efficacy, benefits and value of operating within a lean system. Furthermore, there exists common agreement amongst leading organizations successfully implementing a lean system that in order to do so it must take into consideration the entire enterprise, that is, from supplier to customer and everything in between6. One of the core issues this paper addresses is when the optimal time is to train and educate the people who currently have, or will have, influence over the ‘enterprise’.

Motivated by experiences in the Formula SAE® competition, an engine control unit (ECU) was designed, developed and tested at Oakland University. A systems approach was taken in which the designs of the electronic architecture and software were driven by the mechanical requirements and operational needs of the engine, and by the need for dynamometer testing and tuning functions. An ECU, powered by a 68HC12 microcontroller was developed, including a four-layer circuit board designed for EMC. A GUI was written with Visual C++® for communication with a personal computer (PC). The ECU was systematically tested with an engine simulator, a 2L Ford engine and a 600cc Honda engine, and finally in Oakland's 2004 FSAE vehicle.

The number of EVs are increasing in power distribution systems every day. This research analyses different penetration levels of electric vehicles in power distribution systems to provide stable energy for smart devices and observes its impacts on operational costs and environmental emissions. The supply of EV power is determined based on smart device consumption by optimal energy management of EV batteries so that both the utilities and the car owner get benefits. Utilities can save energy by reducing system loss, while EV owners can earn money by selling it to utilities at their convenient time for smart device operations. The PG&E 69-bus distribution system is used for the simulation and case studies. Case studies in this research show how the power management of EV's batteries charging and discharging characteristics benefits both utilities and EV owners. The uncertainty of the driving pattern of EVs is also considered in the research to get more accurate results.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

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.

The IC engine still plays an important role in global markets, although electrified vehicles are highly demanded in some markets. Emission requirements for stoichiometric operation are challenging. This requires the bolted joints for turbo, EGR (Exhaust Gas Recirculation) and exhaust manifold to work under much higher temperature than before. How to avoid fastener breakage due to bolt bending caused by cyclic changes of the thermal conditions in engines is a big challenge. The temperatures of the components in the exhaust, EGR (Exhaust Gas Recirculation) and turbo systems change from ambient temperature to about 800 ~ 1000 °C when engines run at peak power with wide-open throttle. The temperature change induces catastrophic cyclic bending and axial strain to the fasteners. This research describes a method to reduce the cyclic bending displacement in the fasteners using a low friction washer.

The coefficient of friction between surfaces is an important criterion for predicting metal behavior during sheet metal stamping processes. This research introduces an innovative technique to find the coefficient of friction on a lubricated aluminum sheet metal surface by simulating the industrial manufacturing stamping process while using 3D digital image correlation (3D-DIC) to track the deformation. During testing, a 5000 series aluminum specimen is placed inside a Stretch-Bend-Draw Simulator (SBDS), which operates with a tensile machine to create a stretch and bend effect. The friction coefficient at the contact point between an alloy sheet metal and a punch tool is calculated using an empirical equation previously developed. In order to solve for the unknown friction coefficient, the load force and the drawback force are both required. The tensile machine software only provides the load force applied on the specimen by the load cell.

The need for transient thermal simulations in vehicle packaging studies has grown rapidly in recent years. To date, the computational costs associated with the transient simulation of 3D conjugate heat transfer phenomena has prohibited the widespread use of full vehicle transient simulations. This paper presents results from a recent study that explored a method to circumvent the computational costs associated with long transient conjugate heat transfer simulations. The proposed method first segregates the thermal structural and fluid physics domains to take advantage of time scale differences. The two domains are then re-coupled to calculate a series of steady state conjugate heat transfer simulations at various vehicle speeds. The local convection terms are then used to construct a set of surrogate models dependent on vehicle speed, that predict the local heat transfer coefficients and the local near wall fluid temperatures.

Springback is a common phenomenon in automotive manufacturing processes, caused by the elastic recovery of the internal stresses during unloading. A thorough understanding of springback is essential for the design of tools used in sheet metal forming operations. A DBS (Draw-bead Simulator) has been used to simulate the forming process for two different sheet metals: aluminum and steel. Two levels of pulling force and two die radii have been enforced to the experimental process to get different springback. Also, the Digital Image Correlation (DIC) system has been adopted to capture the sheet contour and measure the amount of side-wall-curl (sheet springback) after deformation. This paper presents the influence of the material properties, force, and die radius on the deformation and springback after forming. A thorough understanding of this phenomenon is essential, seeing that any curvature in the part wall can affect quality and sustainability.

Engineering design-decisions often involve many attributes which can differ in the levels of their importance to the decision maker (DM), while also exhibiting complex statistical relationships. Learning a decision-making policy which accurately represents the DM’s actions has long been the goal of decision analysts. To circumvent elicitation and modeling issues, this process is often oversimplified in how many factors are considered and how complicated the relationships considered between them are. Without these simplifications, the classical lottery-based preference elicitation is overly expensive, and the responses degrade rapidly in quality as the number of attributes increase. In this paper, we investigate the ability of deep preference machine learning to model high-dimensional decision-making policies utilizing rankings elicited from decision makers.

Making a sturdy battery box or enclosure is one of the many challenging issues that the expansion of electrification entails. Many characteristics of an effective battery housing contribute to the safety of passengers and shield the battery from the harsh environment created by vibrations and shocks due to varying road profiles in the vehicle. This results in stress and deformations of different degrees. There is a need to understand and develop a correlation between structural performance and lightweight design of battery enclosure as this can increase the range of the drive and the life cycle of a battery pack. This paper investigates the following points: I) A conceptualized CAD model of battery enclosure is developed to understand the design parameters such as utilization of different material for strength and structural changes for performance against vibration and strength.