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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.

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 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.

Mobility assessment for combat vehicles is often a great challenge for the military due to various subjective attributes. The attributes' characteristics vary significantly depending on the vehicle type and its operating environments such as terrain, weather, and human factors. A clear definition and relationship between multiple attributes including human factors is necessary to assess mobility. To the best of authors' knowledge, many existing mobility assessment techniques use complex analytical methods and focus on individual attributes. In this paper, for the first time, the authors propose a novel approach to define vehicle mobility and its influencing attributes using qualitative linguistic fuzzy variables, which are defined as having values between 0 and 1. The authors also propose a fuzzy logic mobility (FLM) model and a simulation approach to assess a combat vehicle's mobility.

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.

A nonlinear finite element passenger car radial-ply tire model was developed to investigate a tire's three-dimensional transmissibility in the X, Y, and Z directions. The reaction forces of the tire axle in longitudinal (X axis), lateral (Y axis), and vertical (Z axis) directions were recorded when the tire encountered a cleat, and then the FFT (Fast Fourier Transform) algorithm was applied to extract tire's transient response information in the frequency domain. The result showed that this passenger car tire has clear peaks at 47-51 and 91-92 Hz longitudinal, 41-45 Hz lateral, and 80-83Hz vertical. An analytical rigid ring model was also formulated, based on the dynamic equations of the rigid ring tire model. The characteristic equations were obtained and solved for eigenvalues and eigenvectors, which represent tire's free vibration natural frequencies and mode shapes.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

The objective of the research into modeling and simulation was to provide an improvement to the Wayne State EcoCAR 2 team’s math-based modeling and simulation tools for hybrid electric vehicle powertrain analysis, with a goal of improving the simulation results to be less than 10% error to experimental data. The team used the modeling and simulation tools for evaluating different outcomes based on hybrid powertrain architecture changes (hardware), and controls code development and testing (software). The first step was model validation to experimental data, as the plant models had not yet been validated. This paper includes the results of the team’s work in the U.S. Department of Energy’s EcoCAR 2 Advanced vehicle Technical Competition for university student teams to create and test a plug-in hybrid electric vehicle for reducing petroleum oil consumption, pollutant emissions, and Green House Gas (GHG) emissions.

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%.

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.

The demand for drive-by-wire, pre-crash warning and many other new features will require high bandwidth from the future in-vehicle networks. One way to satisfy the high bandwidth requirement of future vehicles is to use a higher bandwidth bus or multiple busses. However, the use of a higher bandwidth bus will increase the cost of the network. Similarly, the use of multiple buses will increase cost as well as the complexity of wiring. Thus, neither option is a viable solution. Another option could be the development of a higher layer protocol to reduce the amount of data to be transferred. The higher layer protocol could be acceptable provided it does not increase the message latencies. The cost of implementing the protocol will be marginal because it can be done by making changes in software. Various data reduction protocols are available in the literature. We have made changes in the existing data reduction protocols to improve the performance of the protocol.

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 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.

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 complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs.

The pia-arachnoid complex (PAC) covering the brain plays an important role in the mechanical response of the brain due to impact or inertial loading. The mechanical properties of the bovine PAC under tensile loading have been characterized previously. However, the transverse properties of this structure, such as shear and normal traction which are equally important to understanding the skull/brain interaction under traumatic loading, have not been investigated. These material properties are essential information needed to adequately define the material model of the PAC in a finite element (FE) model of human brain. The purpose of this study was to determine, experimentally, the material properties of the PAC under normal traction loading. PAC specimens were obtained from freshly slaughtered bovine subjects from various locations.

Frames are important structures found in many transportation applications such as automotive bodies and train cars. They are also widely employed in buildings, bridges, and other load bearing designs. When a frame is carrying multiple loads, it can potentially risk a catastrophic buckling failure. The loads on the frame may be non-proportional in that one force stays constant while the other is increased until buckling occurs. In this study the buckling problem is formulated as a constrained eigenvalue problem (CEVP). As opposed to other CEVP in which the eigenvectors are forced to comply with a number of the constraints, the eigenvalues in the current CEVP are subject to some equality constraints. A numerical algorithm for solving the constrained eigenvalue problem is presented. The algorithm is a simple trapping scheme in which the computation starts with an initial guess and a window containing the potential target for the eigenvalue is identified.

Experiments were conducted to investigate the characteristics of a common rail fuel injection system using a flow rate test rig and a single cylinder research diesel engine. Experiments covered speeds and loads typical to engine conditions under Hybrid Electric Vehicle operation. Different injection modes were investigated including main injection, main-post injection and pilot-main injection. The analysis indicated that the common rail fuel pressure affects all the injection parameters including the start of fuel delivery, its duration and amount under all modes of injection. Also, the pressure waves produced in the system have an impact on the operation of the nozzle-needle and fuel delivery particularly in the main-post injection mode.

The characteristics of the diesel engine unit injector were studied both theoretically and experimentally. The transient fuel pressure in the unit injector was indirectly measured by using strain gauges placed in different locations on the drive train, between the cam and plunger. The events which take place during the injection process were analyzed and the effects of several design and operating variables on the different injection parameters were determined. Computer simulation showed a fairly good agreement between computed and experimental results.

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.