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Deep charge and discharge cycling of 24 Volt battery strings composed of two 12 Volt VRLA batteries wired in series affects reliability and life expectancy. This is a matter of interest in vehicle power source applications. These cycles include those specific operational cases requiring the delivery of the full storage capacity during discharge. The concern here is related to applications where batteries serve as a primary power source and the energy content is an issue. It is a common practice for deep cycling a 24 volt battery string to simply add the specified limit voltages during charge and discharge for the individual 12 Volt batteries. In reality, the 12 Volt batteries have an inherent capacity variability and are not identical in their performance characteristics. The actual voltages of the individual 12 Volt batteries are not identical.

Lithium-Ion batteries are the standard portable power solution to many consumers and industrial applications. These batteries are commonly used in laptop computers, heavy duty devices, unmanned vehicles, electric and hybrid vehicles, cell phones, and many other applications. Charging these batteries is a delicate process because it depends on numerous factors such as temperature, cell capacity, and, most importantly, the power and energy limits of the battery cells. Charging capacity, charging time and battery pack temperature variations are highly dependent on the charging method used. These three factors can be of special importance in applications with strict charging time requirements or with limited thermal management capabilities. In this paper, three common charging methods are experimentally studied and analyzed. Constant-current constant-voltage, the time pulsed charging method, and the multistage constant current charging methods were considered.

This work presents a method for estimating human body orientation using a combination of convolutional neural network (CNN) and stereo camera in real time. The approach uses the CNN model to predict certain human body keypoints then transforms these points into a 3D space using the stereo vision system to estimate the body orientations. The CNN module is trained to estimate the shoulders, the neck and the nose positions, detecting of three points is required to confirm human detection and provided enough data to translate the points into 3D space.

In this paper, a finite element methodology is given in which finite element models of a three-weld Al-Cu plate is created with support and loading conditions emulating those seen in an optical lab. Harmonic response is sought for the models under the presumption that various defective welds are present. The numerical results are carefully examined to determine the guideline frequency range so the actual optical experiment can be carried out more efficiently.

One of the major problems that the automotive industry faces is reducing friction to increase efficiency. Researchers have shown that 30% of the fuel energy was consumed to overcome the friction forces between the moving parts of any automobile, Holmberg et al. [1]. The interface of the piston pin and pin bore is one of the areas that generate high friction under severe working conditions of high temperature and lack of lubrication. In this research, experimental investigation and theoretical simulation have been carried out to analyze the motion of the floating pin against pin bore. In the experimental study, the focus was on analyzing the floating pin motion by using a bench test rig to simulate the floating pin motion in an internal combustion engine. A motion data acquisition system was developed to capture and record the pin motion. Thousands of images were recorded and later analyzed by a code written by MATLAB.

In practice, the piston wrist pin is either fixed to the connecting rod or floats between the connecting rod and the piston. The tribological behavior of fixed wrist pins have been studied by several researchers, however there have been few studies done on the floating wrist pin. A new bench rig has been designed and constructed to investigate the tribological behavior between floating pins and pin bore bearings. The experiments were run using both fixed pins and floating pins under the same working conditions. It was found that for fixed pins there was severe damage on the pin bore in a very short time (5 minutes) and material transfer occurs between the wrist pin and pin bore; however, for the floating pin, even after a long testing time (60 minutes) there was minimal surface damage on either the pin bore or wrist pin.

An existing challenge in current state-of-the-art autonomous vehicles is the process of safely transferring control from autonomous driving mode to manual mode because the driver may be distracted with secondary tasks. Such distractions may impair a driver’s situational awareness of the driving environment which will lead to fatal outcomes during a handover. Current state-of-the-art vehicles notify a user of an imminent handover via auditory, visual, and physical alerts but are unable to improve a driver’s situational awareness before a handover is executed. The overall goal of our research team is to address the challenge of providing a driver with relevant information to regain situational awareness of the driving task. In this paper, we introduce a novel approach to estimating a driver’s visual focus of attention using a 2D RGB camera as input to a Multi-Input Convolutional Neural Network with shared weights. The system was validated in a realistic driving scenario.

Application of cervical spine range of motion data and related anthropometric measures of the head and neck include physical therapy, product design, and computational modeling. This study utilized the Cervical Range of Motion device (CROM) to define the normal range of motion of the cervical spine for subjects five (5) through ten (10) years of age. And, the data was collected and analyzed with respect to anatomical measures such as head circumference, face height, neck length, and neck circumference. This study correlates these static anthropometric measures to the kinematic measurement of head flexion, extension, lateral extension, and rotation.

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.

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.

Proper valve angles and concentric valve seats are critical to performance of an engine. If the valve seat were not right, the valve is not going to seat properly resulting in reduced power output. Although the performance of CNC machines is accurate, unavoidable human errors at the part loading position have serious repercussions on engine performance. A solution algorithm presented in this paper employs the principles of inverse kinematics wherein a faulty compound-hole angle axis in space caused by the translational and rotational errors at the part loading position is identified with an imaginary true axis in space by enforcing identity through a modified machine axes.

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.

Shearography has been proved to be highly effective for nondestructive testing (NDT), especially for NDT of composite materials used in the automotive and aerospace engineering. While its application in material testing and material research has already achieved more and more acceptance in research and industry, its applications are mainly limited to the inspection and testing of an object surface which can directly be observed by a shearographic camera. Its application is mainly limited to inspect and test an object surface which can directly be observed by a shearographic camera. It is impossible to inspect an internal surface of a container. If the reflected light of the surface, which has to be examined, can’t reach the shearographic camera there is still no inspection possible. This paper presents the development of a rigid shearographic endoscope. The development enabled shearographic inspection on both external and internal surfaces of objects.

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.

Engineering practice routinely involves decision making under uncertainty. Much of this decision making entails reconciling multiple pieces of information to form a suitable model of uncertainty. As more information is collected, one expectedly makes better and better decisions. However, conditional probability assessments made by human decision makers, as new information arrives does not always follow expected trends and instead exhibits inconsistencies. Understanding them is necessary for a better modeling of the cognitive processes taking place in their mind, whether it be the designer or the end-user. Doing so can result in better products and product features. Quantum probability has been used in the literature to explain many commonly observed deviations from the classical probability such as: question order effect, response replicability effect, Machina and Ellsberg paradoxes and the effect of positive and negative interference between events.

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.

This work presents a method to estimate the human body orientation using 2D images from a person view; the challenge comes from the variety of human body poses and appearances. The method utilizes OpenPose neural network as a human pose detector module and depth sensing module. The modules work together to extract the body orientation from 2D stereo images. OpenPose is proven to be efficient in detecting human body joints, defined by COCO dataset, OpenPose can detect the visible body joints without being affected by backgrounds or other challenging factors. Adding the depth data for each point can produce rich information to the process of 3D construction for the detected humans. This 3D point’s setup can tell more about the body orientation and walking direction for example. The depth module used in this work is the ZED camera stereo system which uses CUDA for high performance depth computation.

Fretting is an important phenomenon that happens in many mechanical parts. It is the main reason in deadly failures in automobiles, airliners, and turbine engines. The damage is noticed between two surfaces clamped together by bolts or rivets that are nominally at rest, but have a small amplitude oscillation because of vibration or local cyclic loading. Fretting damage can be divided into two types. The first type is the fretting fatigue damage where a crack would initiate and propagate at specific location at the interface of the mating surfaces. Cracks usually initiate in the material with lower strength because of the local cyclic loading conditions which eventually lead to full failure. The second type is the fretting wear damage because of external vibration. Researchers have investigated this phenomenon by theoretical modeling and experimental approaches. Although a lot of research has been done on fretting damage, some of the parameters have not been well studied.

Future fully autonomous and partially autonomous cars equipped with Advanced Driver Assistant Systems (ADAS) should assure safety for the pedestrian. One of the critical tasks is to determine if the pedestrian is crossing the road in the path of the ego-vehicle, in order to issue the required alerts for the driver or even safety breaking action. In this paper, we investigate the use of 2D pose estimators to determine the direction and speed of the pedestrian crossing the road in front of a vehicle. Pose estimation of body parts, such as right eye, left knee, right foot, etc… is used for determining the pedestrian orientation while tracking these key points between frames is used to determine the pedestrian speed. The pedestrian orientation and speed are the two required elements for the basic path estimation.