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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.

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.

Adhesively bonded joints have been applied in the automotive industry for the past few decades due to their advantages such as higher fatigue resistance, light weight, capability of joining dissimilar materials, good energy absorption, and high torsional stiffness for overall body structure. They also provide an effective seal against noise and vibration at a low cost. There exists the challenge of defining the fatigue characteristics of adhesive joints under cyclic loading conditions, and conventional methods have limitations in detecting the crack initiation of a bonded joint. This study introduces a method of detecting crack initiation by using the frequency method. It is found that stiffness change in the system is highly correlated to change in natural frequencies. By monitoring the change in natural frequencies, the crack initiation can be detected.

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.

This research proposes an automatic computer-aided design, analysis, and optimization process of a twist beam rear suspension system. The process combines CAD (Computer-Aided Design), CAE (Computer-Aided Engineering), and optimization technologies into an automation procedure, which includes: structural design, dynamic analysis, vibration analysis, durability analysis, and multidisciplinary optimization. The automation results shown the twist beam rear suspension weight reduced, the durability fatigue life increased, and the K&C (kinematics & compliance) characteristics are improved significantly.

Technological advancement in the automotive industry necessities a closer focus on the functional safety for higher automated driving levels. The automotive industry is transforming from conventional driving technology, where the driver or the human is a part of the control loop, to fully autonomous development and self-driving mode. The Society of Automotive Engineers (SAE) defines the level 4 of autonomy: “Automated driving feature will not require the driver to take over driving control.” Thus, more and more safety related electronic control units (ECUs) are deployed in the control module to support the vehicle. As a result, more complexity of system architecture, software, and hardware are interacting and interfacing in the control system, which increases the risk of both systematic and random hardware failures.

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.

Passengers' frequent requests are for less Noise, Vibration and Harshness (NVH) in the vehicle compartment. This and the reduction of noise and vibration levels from major sources like the engine necessitate better performance of other sources of noise and vibrations in a vehicle. Some of these sources are the hydraulic circuits including the power steering system. Fluid pulses or pressure ripples, generated typically by a pump, become excitation forces to the structure of a vehicle or the steering gear and represent a considerable source of discomfort to the vehicle passengers. Current power steering technology attenuates this ripple along the pressure line connecting the pump to the steering gear. Finding the optimum design configuration for the components (hose, tuner, tube, and others) has been a matter of experience-based trial and error. This paper is a part of a program to simulate and optimize fluid borne noise in hydraulic circuits.

The Intelligent Ground Vehicle Competition (IGVC) is a multidisciplinary exercise in product realization for college engineering students. They design, build, and compete with autonomous vehicles in events ranging from lane following, obstacle avoidance, platooning, to Global Positioning System (GPS) navigation. Technologies involved include electronic controls, computer-based vision systems, object detection, rangefinding, and global positioning. The real world applications are in intelligent transportation systems, the military, and manufacturing automation. Students have been creative and have learned a great deal. Industry recruiters have been highly supportive.

Certain properties of fluid change with pressure, temperature and other operating system conditions. In automotive hydraulic systems driven by pumps, air usually enters the system as dissolved matter or very small bubbles. Such air will change certain properties of the fluid like the density, bulk modulus and viscosity. Measuring these properties for evaluating system performance in real operating conditions is one of the big challenges that face engineers. In this paper, the bulk modulus of certain power steering fluids is measured using standard impedance and flow ripple tests for pumps. The effects of pressure, temperature and speed on the bulk modulus are studied thoroughly.

In the automotive industry, performing steady-state tests on an internal combustion engine can be a time consuming and costly process, but it is necessary to ensure the engine meets performance and emissions criteria set by the manufacturer and regulatory agencies. Any measures that can reduce the amount of time required to complete these testing campaigns provides significant benefits to manufacturers. The purpose of this work is then to develop a systematic approach to minimize the time required to conduct a steady-state engine test campaign using a Savitsky-Golay filter to calculate measured signal gradients for continuous steady-state detection. Experiments were conducted on an Armfield CM11-MKII Gasoline Engine test bench equipped with a 1.2L 3-cylinder Volkswagen EA111 R3 engine. The test bench utilizes throttle position control and an eddy current dynamometer braking system with automatic PID control of engine speed.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

To ensure ride comfort, the dynamic characteristics, such as natural frequencies, of a vehicle is often tuned to a specific value by managing the magnitude and location of some masses and/or configuration of stiffeners without sacrificing the structural strength and overall fuel performance of the vehicle. We first formulate the mathematical statement of the problem in a constrained eigenvalue form. Optimal solutions are sought using various finite element techniques. A novel methodology involving genetic algorithm and Newton’s iterative method is developed to solve the constrained eigenvalue problems. Several examples, including discrete and continuous systems, are presented to demonstrate the effectiveness and accuracy of the proposed methodology. The strategy of managing the mass location and distribution to target a preferred natural frequency or frequencies is given in the conclusion.

This paper presents an optimal control co-design framework of a parallel electric-hydraulic hybrid powertrain specifically tailored for heavy-duty vehicles. A pure electric powertrain, comprising a rechargeable lithium-ion battery, a highly efficient electric motor, and a single or double-speed gearbox, has garnered significant attention in the automotive sector due to the increasing demand for clean and efficient mobility. However, the state-of-the-art has demonstrated limited capabilities and has struggled to meet the design requirements of heavy-duty vehicles with high power demands, such as a class 8 semi-trailer truck. This is especially evident in terms of a driving range on one battery charge, battery charging time, and load-carrying capacity. These challenges primarily stem from the low power density of lithium-ion batteries and the low energy conversion efficiency of electric motors at low speeds.

Full nonlinear finite element radial-ply and bias-ply tire models are developed to investigate different structured tires' transmissibility phenomena. The reaction forces of the tire axles in time domain are recorded first when the tires encounter a bump (cleat), and then the FFT algorithm is applied to examine the dynamic response information in frequency domain. The results of the radial-ply vs. bias-ply tires' transmissibility are validated against previous studies and show reasonable agreement.

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.

Current implementations of vision-based Advanced Driver Assistance Systems (ADAS) are largely dependent on real-time vehicle camera data along with other sensory data available on-board such as radar, ultrasonic, and GPS data. This data, when accurately reported and processed, helps the vehicle avoid collisions using established ADAS applications such as Forward Collision Avoidance (FCA), Autonomous Cruise Control (ACC), Pedestrian Detection, etc. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) over Dedicated Short Range Communication (DSRC) provides basic sensory data from other vehicles or roadside infrastructure including position information of surrounding traffic. Exchanging rich data such as vision data between multiple vehicles, and between vehicles and infrastructure provides a unique opportunity to advance driver assistance applications and Intelligent Transportation Systems (ITS).