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It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

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.

Adhesive bonding provides a versatile strategy for joining metallic as well as non-metallic substrates, and also offers the functionality for joining dissimilar materials. In the design of unibody vehicles for NVH (Noise, Vibration and Harshness) performance, adhesive bonding of sheet metal parts along flanges can provide enhanced stiffening of body-in-white (BIW) leading to superior vibration resistance at low frequencies and improved acoustics due to sealing of openings between flanges. However, due to the brittle nature of adhesives, they remain susceptible to failure under impact loading conditions. The viability of structural adhesives as a sole or predominant mode of joining stamped sheet metal panels into closed hollow sections such as hat-sections thus remains suspect and requires further investigation.

Today’s advanced vehicles have high degree of interaction due to numerous sensors, actuators and also with complex communication within the control units. In order to hack a vehicle, it has to be within a certain range of communication. Here, we discuss the On-Board Diagnostic (OBD) regulations for next generation BEV/HEV, its vulnerabilities and cybersecurity threats that come with hacking. We propose three cybersecurity attack detection and defense methods: Cyber-Attack detection algorithm, Time-Based CAN Intrusion Detection Method and, Feistel Cipher Block Method. These control methods autonomously diagnose a cybersecurity problem in a vehicle’s onboard system using an OBD interface, such as OBD-II when a fault caused by a cyberattack is detected, All of this is achieved in an internal communication network structure. The results discussed here focus on the first detection method that is Cyber-Attack detection algorithm.

This paper presents experimental investigations of diagnosing and analyzing the low-frequency, low- SNR (Signal to Noise Ratio) noise sources of three motorcycles using a hybrid technology that consists of a passive SODAR (Sonic Detection And Ranging) and modified HELS (Helmholtz Equation Least Squares) methods. The former enables one to determine the precise locations of multiple sound sources in 3D space simultaneously over the entire frequency range that is consistent with a measurement microphone in non-ideal environment, where there are random background noise and unknown interfering signals. The latter enables one to reconstruct all acoustic quantities such as the acoustic pressure, acoustic intensity, time-averaged acoustic power, radiation patterns, and sound transmission paths through arbitrarily shaped vibrating structures.

The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

Lithium-ion polymer battery has been widely used for vehicle onboard electric energy storage ranging from 12V SLI (Starting, Lighting, and Ignition), 48V mild hybrid electric, to 300V battery electric vehicle. Formulation on cell parameters acquired from minimum numbers of experiments, the modeling and simulation could be an effective approach in predicting battery performance, thermal effectiveness, and degradation. This paper describes the modeling, simulation, and validation of Lithium-Nickel-Manganese-Cobalt-Oxide (LiNiMnCoO2) based cell with 3.6V nominal voltage and 20Ah capacity. Constant current 20A, 40A, 60A, and 80A discharge tests are conducted in the computer-controlled cycler and temperature chamber. Discharging voltage curves and cell surface temperature distributions are recorded in each discharging test. A three-dimensional cell model is constructed in the COMSOL multi-physics platform based on the cell parameters.

With the mass movement toward electrification and renewable technologies, the scope of innovation of electrification has gone beyond the automotive industry into areas such as electric motorcycle applications. This paper provides a discussion of the methodology and complexities of converting an internal combustion motorcycle to an electric motorcycle. In developing this methodology, performance goals including, speed limits, range, weight, charge times, as well as riding styles will be examined and discussed. Based on the goals of this paper, parts capable of reaching the performance targets are selected accordingly. Documentation of the build process will be presented along with the constraints, pitfalls, and difficulties associated with the process of the project. The step-by-step process that is developed can be used as a guideline for future build and should be used as necessary.

Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment.

Accident statistics have shown that older and obese occupants are less adaptable to existing vehicle occupant restraint systems than ordinary middle-aged male occupants, and tend to have higher injury risk in vehicle crashes. However, the current research on injury mechanism of aging and obese occupants in vehicle frontal impacts is scarce. This paper focuses on the optimization design method of occupant restraint system parameters for specific body type characteristics. Three parameters, namely the force limit value of the force limiter in the seat belt, pretensioner preload of the seat belt and the proportionality coefficient of mass flow rate of the inflator were used for optimization. The objective was to minimize the injury risk probability subjected to constraints of occupant injury indicator values for various body regions as specified in US-NCAP frontal impact tests requirements.

The explosion of big data has created challenges for both cloud-based systems and Autonomous Vehicles (AVs) in data collection and management. The same challenges are now being realized in developing databases for integrated sensors, streaming, real-time and on-demand services in AVs. With just one AV expecting to generate over 30 Terabytes of data a day, modern NoSQL databases provide opportunities to horizontally scale AV data seamlessly. NoSQL provides solutions designed to accommodate a wide variety of data models such as, key-value, document, column and graph databases. Key-value stores are by nature scalable, fast processing, and distribute horizontally. These databases are tasked with handling several data types including IoT, radar, lidar, ultra-sonic sensors, GPS, odometry, and sensor data while providing streaming and real-time services. NoSQL can store and utilize structured, semi-structured, and unstructured data necessary for multimedia storage needs.

Due to the variation of compartment design and occupant’s posture in self-driving cars, there is a new and major challenge for occupant protection. In particular, the studies on occupant restraint systems used in the self-driving car have been significantly delayed compared to the development of the autonomous technologies. In this paper, a numerical study was conducted to investigate the effectiveness of three typical restraint systems on the driver protection in three different scenarios.

There has been recent progress over the past 10 years in research comparing 6-year-old thoracic and abdominal response of pediatric volunteers, pediatric post mortem human subjects (PMHS), animal surrogates, and 6-year-old ATDs. Although progress has been made to guide scaling laws of adult to pediatric thorax and abdomen data for use in ATD design and development of finite element models, further effort is needed, particularly with respect to lateral impacts. The objective of the current study was to use the impact response data of age equivalent swine from Yaek et al. (2018) to assess the validity of scaling laws used to develop lateral impact response corridors from adult porcine surrogate equivalents (PSE) to the 3-year-old, 6-year-old, and 10-year-old for the thorax and abdominal body regions.

The development of Autonomous Vehicles (AV) has become a popular subject in academia and industry. Companies and cities are quickly realizing the opportunities that AVs can generate from Mobility as a Service to traffic safety. The challenges for the infrastructure to incorporate AVs as a viable transportation source are immense, from an outdated infrastructure to radical Smart-City designs. Historically, the transportation infrastructure has faced challenges from underfunding, economics, and much needed improvements. With the current infrastructure unable to support many of the services required by a fully connected network, a transformation will be necessary to meet growing mobility needs. The role of accelerating technology in data centers are key for production operations among industry leaders such as Amazon and Microsoft for real-time processing.

Triggered explosions are increasingly becoming common in the world today leading to the loss of precious lives under the most unexpected circumstances. In most scenarios, ordinary citizens are the targets of such attacks, making it essential to design countermeasures in open areas as well as in mobility systems to minimize the destructive effects of such explosive-induced blasts. It would be rather difficult and to an extent risky to carry out physical experiments mimicking blasts in real world scenarios. In terms of mechanics, the problem is essentially one of fluid-structure interaction in which pressure waves in the surrounding air are generated by detonating an explosive charge which then have the potential to cause severe damage to any obstacle on the path of these high-energy waves.

Artificial Intelligence (A.I.) and Big Data are increasing become more applicable in the development of technology from machine design and mobility to bio-printing and drug discovery. The ability to quantify large amounts of data these systems generate will be paramount to establishing a robust infrastructure for interdisciplinary autonomous applications. This paper purposes an integrated approach to the environment, pre/post data processing, integration, and system security for robust systems in intelligent transportation systems. The systems integration is based on a FPGA embedded system design and computing (EDGE) platform utilizing image processing CNN algorithms from High Energy Physics (HEP) experiments in data centers with associative memory to ROS- FPGA technology in vehicles for hyper-scale infrastructure scalability. The ability to process data in the future is equivalent to collision particle detection that the Large Hadron Collider (LHC) produces at CERN.

Lead-acid batteries have dominated the automotive conventional electric system, particularly in the functions of starting (S), lighting (L) and ignition (I) for decades. However, the low energy-to-weight ratio and the low energy-to-volume ratio makes the lead-acid SLI battery relatively heavy, large, and shallow Depth of Discharge (DOD). This could be improved by replacing the lead-acid battery by the lithium-ion polymer battery. The lithium-ion polymer battery can provide the same power with lightweight, compact volume, and deep DOD for engine idle elimination using start-stop function that is a basic feature in electric-drive vehicles. This paper presents the modeling and validation of a lithium-ion battery for SLI application. A lithium-metal-oxide based cell with 3.6 nominal voltage and 20Ah capacity is used in the study. A simulation model of lithium-ion polymer battery pack (14.4V, 80Ah) with battery management system is built in the MATLAB/Simulink environment.

There is a general interest in the reduction of cooling loads in military vehicles. To that end thermal barrier coatings (TBCs) are being studied for their potential as insulators, particularly for military engines. The effectiveness of TBCs is largely dependent on their thermal properties, however insulating effects can also be modified by applying different coating thickness. Convection from in-cylinder surfaces can also be affected by manipulation of surface structure. Although most prior studies have examined TBCs as a means of increasing efficiency, military vehicle design is primarily concerned with the reduction of cylinder heat transfer to allow downsizing of cooling systems. A 1-D transient conjugate heat transfer model was developed to provide insight into the effects of different TBC designs and material selection on cooling loads. Results identify low thermal conductivity and low thermal capacitance as key parameters in achieving optimal heat loss reduction.