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In contrast to conventional powertrains from internal combustion engine vehicles (ICEV), the tribological performance of powertrains of electric vehicles (EVs) must be further evaluated by considering new critical operating conditions such as electrical environments. The operation of any type of electric motor produces shaft voltages and currents due to various hardware configurations and factors. Furthermore, the common application of inverters intensifies this problem. It has been reported that the induced shaft voltages and currents can cause premature failure problems in tribological components such as bearings and gears due to accelerated wear and/or fatigue. It is ascribed to effects of electric discharge machining (EDM), also named, sparking wear caused by shaft currents and poor or increasingly diminishing dielectric strength of lubricants. A great effort has been done to study this problem in bearings, but it has not yet been the case for gears.

A Dual Power Split Electronic Continuously Variable Transmission (DPS-ECVT) with an input-split, output coupled, split-power-path configuration is proposed for improving overall system efficiency and range for electric vehicles. By modulating the power split ratio between the mechanical (planetary gear meshes) and electrical (Motor Generator Units) driveline components, a continuous range of gear ratios operating at higher efficiency is obtained. The proposed concept leverages two power-split units that lead to significantly reduced power flow through the electrical drivelines (compared with single speed EV transmissions as well as single power-split E-CVTs) while providing the same overall ratio spread for transmission operation.

Object detection and tracking is a central aspect of perception for autonomous vehicles. While there has been significant development in this field in recent years, many perception algorithms still struggle to provide reliable information in challenging weather conditions which include night-time, direct sunlight, glare, fog, etc. To achieve full autonomy, there is a need for a robust perception system capable of handling such challenging conditions. In this paper, we attempt to bridge this gap by proposing an algorithm that combines the strength of automotive radars and infra-red thermal cameras. We show that these sensors complement each other well and provide reliable data in poor visibility conditions. We demonstrate the advantages of a thermal camera over a visible-range camera in these situations and employ YOLOv3 for object detection.

As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis proposal presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses.

This paper discusses different computer vision techniques investigated by the authors for identifying Emergency Vehicles (EV). Two independent EV identification frameworks were investigated: (1) A one-stage framework where an object detection algorithm is trained on a custom dataset to detect EVs, (2) A two-stage framework where an object classification algorithm is implemented in series with an object detection pipeline to classify vehicles into EVs and non-EVs. A comparative study is conducted for different multi-spectral feature vectors of the image, against several classification models implemented in framework 2. Additionally, a user-defined feature vector is defined and its performance is compared against the other feature vectors. Classification outputs from each of the frameworks are compared to the ground truth, and results are quantitatively listed to conclude upon the ideal decision rule.

Future electronics and photonics systems, weapons systems, and environmental control systems in aircraft will require advanced thermal management technology to control the temperature of critical components. Two-phase Thermal Management Systems (TMS) are attractive because they are compact, lightweight, and efficient. However, maintaining stable and reliable cooling in a two-phase flow system presents unique design challenges, particularly for systems with parallel evaporators during thermal transients. Furthermore, preventing ingress of liquid into a vapor compressor during variable-gravity operation is critical for long-term reliability and life. To enable stable and reliable cooling, a highly stable two-phase system is being developed that can effectively suppress flow instability in a system with parallel evaporators. Flow stability is achieved by ensuring that only single-phase liquid enters the evaporators.

A major decision to make in design projects is the selection of the best technology to provide some needed system functionality. In making this decision, the designer must consider the range of technologies available and the performance of each. During the useful life of the product, the technologies composing the product evolve as research and development efforts continue. The performance evolution rate of one technology may be such that even though it is not initially a preferably technology, it becomes a superior technology after a few years. Quantifying the evolution of these technologies complicates the technology selection decision. The selection of energy storage technology in the design of an electric car is one example of a difficult decision involving evolving technologies.

Tradeoff studies are a common part of engineering practice. Designers conduct tradeoff studies in order to improve their understanding of how various design considerations relate to one another and to make decisions. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly under the given criteria and explore more carefully those that remain. One limitation of current practice is that designers cannot combine the results of preexisting tradeoff studies under uncertainty. For deterministic problems, designers can use the Pareto dominance criterion to eliminate inferior designs. Prior work also exists on composing tradeoff studies performed under certainty using an extension of this criterion, called parameterized Pareto dominance.

As emission standards become more stringent, many studies have been carried out to understand and reduce the emissions from diesel combustion engines, among which nitric oxide (NO) emissions and soot are known to have the trade-off relation during combustion processes. One aspect of this trade-off is manifested by the role radiation heat transfer plays on post-flame gas temperature, thus affecting NO formation. For example, a decrease in in-cylinder soot decreases radiation heat transfer causing an increase in post-flame gas temperature and partially contributing to the corresponding soot-NO relationship with an increase in NO formation. This topic has re-emerged with the increased use of biodiesel; a potential explanation for the so-called "biodiesel NOx penalty" is biodiesel's inherently reduced in-cylinder soot.

The need of upfront modeling, simulation and design optimization has been ever increasing during full vehicle product development process. The overall vehicle system and component subsystem performances remain critical considerations for making final product release decision. With these challenges in mind, the work of this paper discusses the development of feasible CAE methods, tools, and processes for multi-objective design optimization. A full integrated tractor trailer truck vehicle is used as an example to demonstrate this capability. The proposed approach allows several design objectives to be simultaneously optimized, which might otherwise be extremely difficult to achieve with experimental methods.

Military and civilian vehicles are moving towards more electrification, in response to the increasing demand for multi-mode missions, fuel consumption and emissions reduction, and dual use electrical and electronic components. Consequently, the vehicle electric load is increasing rapidly. For military vehicles, these electrical loads include: the loads for electric traction (EV and HEV), cabin climate conditioning, vehicle control and actuation, actuation by wire (X by wire), sensors, reconnaissance, communications, weapons etc. All these requirements need to be supported by an efficient, fast responding and high capacity energy storage system. The electric load of a vehicle can be decomposed into two components--- static and dynamic loads. The static component is slowly varying power with limited magnitude, whereas the dynamic load is fast varying power with large magnitude. The energy storage system, accordingly, comprises of two basic elements.

A concept of “driving situation awareness”-driven energy management system for parallel hybrid electric vehicles (HEVs) is introduced. The essential feature of the proposed energy management system is to assess the driving environment (in terms of facility type combined with traffic congestion level) using long and short term statistical features of the drive cycle. Subsequently, this knowledge is provided to a system that makes intelligent decisions with respect to the torque distribution and charge sustenance tasks. Simulation work was carried out for the validation of proposed system, and the results reveal its viability for energy management of parallel hybrid vehicles.

Due to their harsh operating environments, military vehicle drive trains have special requirements. These special requirements are usually represented by hill climbing ability, obstacle negotiation, battlefield cross country travel, hard acceleration, high speed, etc. These special requirements need the vehicle drive train to have a wider torque and speed range characteristics than commercial vehicles. We have proved that larger constant power ratio in electric motor can significantly enhance the vehicle acceleration performance. In other words, for the same acceleration performance, large constant power ratio can minimize the power rating of the traction motor drive, thus minimizing the power rating of the power source (batteries for instance). Actually, extension of the constant power range can also significantly enhance the gradeability, which is crucial for military vehicles.

A fuzzy logic technique is presented in this paper for sensorless speed and position identification of vector-controlled PWM inverterfed PMSM drives used in EV/HEV propulsion systems. Fuzzy logic is used to estimate the rotor speed and position. Operation of the drive is studied by numerical simulation. The performance for different drive conditions is also analyzed and the results are shown in the paper. Simulation results show that the proposed control method can be effectively used in controlling the PMSM drives with high performance for EV/HEV applications. It is found that the fuzzy logic system is reliable without using any speed or position sensor.

Short circuit incidents on traction motors can cause ‘wheel-locking’ on the vehicle, and may have an adverse impact on vehicle stability. This paper investigates the necessity of fault-tolerant motors for EV and HEV traction applications. Reaction of resulting fault torques differ along with electric motor types and fault variety. The paper analyzes the short-circuit behavior of three basic motor types: permanent magnet, induction and switched reluctance motor. The analysis is based on the transient simulation of the three most common inverter short-circuit cases and their effect on vehicle stability.

The recent growing interest in electric vehicle (EV) and hybrid electric vehicle (HEV) demands for an efficient, reliable and economical motor drive for electric propulsion. However, searching for a suitable traction motor becomes quite involved when vehicle dynamics and system architecture are considered. This paper makes an in-depth investigation on two highly important traction motor characteristics, extended speed range-ability and energy efficiency, from vehicular system perspective. The influences of these two motor drive features on a pure EV, a post-transmission, and two pre-transmission parallel HEV with 20% and 50% hybridization are studied in this paper. Two EV-HEV software packages ‘V-ELPH’ developed by Texas A&M University and ‘ADVISOR’ from NREL are used for simulation purposes. Based on the results in this paper, a systematic method is developed regarding the selection of traction drives for EV and HEV propulsion systems.

There is a clear trend towards Hybrid Electric Vehicles (HEV) due to the environmental concerns. On the other hand, with increasing hotel and ancillary loads and replacement of more engine driven mechanical and hydraulic loads with electrical loads, automotive systems are becoming more electric. This is the concept of More Electric Cars (MEC) which necessitates going to a higher voltage such as 42V for conventional cars. Can the evaluation of the 42V MEC smoothly lead to the Hybrid Electric Vehicles (HEV) and More Electric Hybrid Vehicles (MEHV)? In this paper, we investigate the feasibility of 42V & 14+42V electrical power systems for MEHV. Technical issues of such a solution are explored in detail.

Vehicle dynamics requires extended-speed, constant-power operation from the propulsion system in order to meet the vehicle's operating constraints (e.g., initial acceleration and gradeability) with minimum power. Decrease in power rating will decrease the volume of the energy storage system. However, extending the constant power operating range of the electric drives increases its rated torque, thereby, increasing motor volume and weight. This paper investigates the effect of extended constant power operation on battery driven electric vehicle (BEV) propulsion system taking the change in motor weight and battery volume into account. Five BEV systems with five traction drive having different base speeds are simulated for this study. The performances of the BEVs are obtained using FUDS and HWYFET drive cycles. Two EV-HEV software packages ‘V-ELPH’ developed by Texas A&M University and ‘ADVISOR’ from NREL are used for simulation testing.

Two parallel HEV control concepts: ‘thermostat’ and ‘power split’ are compared in this paper. To achieve a substantial improvement in fuel economy, the ‘thermostat’ or ‘on/off’ control technique intended to improve the fuel efficiency of a series HEV has been adopted and designed for parallel HEV. Among different ‘power split’ concepts developed for parallel hybrids only the ‘electrically assist’ algorithm is considered in this paper. These two control concepts are compared for three parallel HEV architectures: pre-transmission, post-transmission and continuous variable transmission hybrids. The comparison study also includes the effect of hybridization factor-the ratio of the electric power to the total propulsion power. The matrices of comparison are level of performance, energy consumption and exhaust emissions. The SAE J1711 partial charge test procedure is followed.

The purpose of this paper is to present an assessment of the negative impedance instability concept of the constant power loads in the More Electric Aircraft (MEA) power systems. We address the fundamental problems faced in the stability studies of these multi-converter power electronic systems. An approach to the design of sliding-mode controllers for PWM DC/DC converters with constant power loads is presented. Because of the negative impedance destabilizing characteristics of constant power loads, conventional linear control methods have stability limitations around the operating points. However, the proposed controllers improve large-signal stability and dynamic responses. The proposed controllers are simulated and their responses under different operations are discussed. Finally, we verify the stability of the controllers using the second theorem of Lyapunov.