Search Results

Predicting fuel economy during early stages of concept development or feasibility study for a new type of powertrain configuration is an important key factor that might affect the powertrain configuration decision to meet CAFE standards. In this paper an efficient model has been built in order to evaluate the fuel economy for a new type of charge sustaining series hybrid vehicle that uses a Genset assembly (small 2 cylinders CNG fueled engine coupled with a generator). A first order mathematical model for a Li-Ion polymer battery is presented based on actual charging /discharging datasheet. Since the Genset performance data is not available, normalized engine variables method is used to create powertrain performance maps. An Equivalent Consumption Minimization Strategy (ECMS) has been implemented to determine how much power is supplied to the electric motor from the battery and the Genset.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

For NASA's Constellation Program, an Intravehicular Activity (IVA) and Extravehicular Activity (EVA) Life Support Umbilical System (LSUS) will be required to provide environmental protection to the suited crew during Crew Exploration Vehicle (CEV) cabin contamination or depressurization and contingency EVAs. The LSUS will provide the crewmember with ventilation, cooling, power, communication, and data, and will also serve as a crew safety restraint during contingency EVAs. The LSUS will interface with the Vehicle Interface Assembly (VIA) in the CEV and the Suit Connector on the suit. This paper describes the effort performed to develop concept designs for IVA and EVA umbilicals, universal multiple connectors, handling aids and stowage systems, and VIAs that meet NASA's mission needs while adhering to the important guiding principles of simplicity, reliability, and operability.

Lunar dust exposures occurred during the Apollo missions while the crew was in the lunar module on the moon's surface and especially when micro-gravity conditions were attained during rendezvous in lunar orbit. Crews reported that the dust was irritating to the eyes, and in some cases, respiratory symptoms were elicited. NASA's current vision for lunar exploration includes stays of 6 months on the lunar surface hence the health effects of periodic exposure to lunar dust in the habitat need to be assessed. NASA is performing this assessment with a series of in vitro and in vivo tests with authentic lunar dust. Our approach is to “calibrate” the intrinsic toxicity of lunar dust by comparison to a relatively low toxicity dust (TiO2) and a highly toxic dust (quartz) using intrapharyngeal instillation of the dusts to mice. A battery of indices of toxicity is assessed at various time points after the instillations.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

The primary objectives of this study were to determine the factors that affect stability during locomotion in both lunar and martian gravity environments and to determine the criteria needed to enhance stability and traction. This study tested the effects of changing the speed of locomotion and the pattern of locomotion under three gravity conditions. The results showed that as the gravity level decreased, the amount of vertical and horizontal forces dropped significantly. The results also showed that there are some similarities across gravity levels with regard to changing the speed as well as the pattern of locomotion. In general, an increase in the speed resulted in an increase in the vertical and the horizontal forces. A change in the pattern of locomotion showed that even at reduced gravity, it will be more difficult to stop than compared to continue or start the motion.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.

As next generation space suit concepts enable extravehicular activity (EVA) mission capability to extend beyond anything currently available today, revolutionary advances in life support technologies are required to achieve anticipated NASA mission profiles than may measure years in duration and require hundreds of sorties. Since most life support systems require power, increased mass and volume efficiency of the energy storage materials can have a dramatic impact on reducing the overall weight of next generation space suits. ITN Energy Systems, in collaboration with Hamilton Sundstrand and the NASA Johnson Space Center's EVA System's Team, is developing multifunctional fiber batteries to address these challenges. By depositing the battery on existing space suit materials, e.g. scrim fibers in the thermal micrometeoroid garment (TMG) layers, parasitic mass (inactive materials) is eliminated leading to effective energy densities ∼400 Wh/kg.

As next generation space suit concepts enable extravehicular activity (EVA) mission capability to extend beyond anything currently available today, revolutionary advances in life support technologies are required to achieve anticipated NASA mission profiles that may measure years in duration and require hundreds of sorties. Since most life support systems require power, increased mass and volume efficiency of the energy storage materials can have a dramatic impact on reducing the overall weight of next generation space suits. This paper details the development of a multifunctional fiber battery to address these needs.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey.

Capacitance sensing is the technology that detects the presence of nearby objects by measuring the change in capacitance. A change in capacitance is triggered either by a change in dielectric constant, area of overlap or distance of separation between the electrodes of the capacitor. It is a technology that finds wide use in applications such as touch screens, proximity sensing etc. Drawing motivation from such applications, this paper investigates how capacitive sensing can be employed to detect the presence and posture of occupants inside vehicles. Compared to existing solutions, the proposed approach is low-cost, easy to deploy and highly efficient. The sensing system consists of a capacitance-sensing mat that is embedded with copper foils and an associated sensing circuitry. Inside the mat the foils are arranged in rows and columns to form several touch-nodes across the surface of the mat.

With the increasing attention towards electric vehicles (EV), power electronics technology has become more prominent on vehicular systems. EV requires compact energy conversion and control technology to improve system efficiency and optimize the sizing of power components. Therefore, it is important to reduce thermal losses, while supplying an adequate amount of power to different EV devices. Silicon carbide (SiC)-based power semiconductors provide performance improvements such as lower power losses, higher junction temperature and higher switching frequency compared to the conventional silicon (Si)-based switching devices. High-frequency switching is preferred for power converters to minimize the necessity of passive filters; however, high-frequency switching causes additional thermal stress on semiconductor switches due to the increase in switching losses. The degradation of switching devices in power converters are primarily related to the junction temperature.

Thermal barrier coatings (TBCs) have long been studied as a potential pathway to achieve higher thermal efficiency in spark ignition engines. Researchers have studied coatings with different thicknesses and thermophysical properties to counteract the volumetric efficiency penalty associated with TBCs in spark ignition. To achieve an efficiency benefit with minimal charge heating during the intake stroke, low thermal inertia coatings characterized by their larger temperature swings are required. To study the impact of low thermal inertia coatings in spark ignition, coatings were applied to the cylinder head, piston crown, intake and exhaust valve faces, and intake and exhaust valve backsides. Tier III EEE E10 certification gasoline was used to keep the experiments relevant to the present on-road vehicles. This study is aimed at analyzing durability of the coatings as well as efficiency and emissions improvements.

Catalytic converters, which are commonly used for after-treatment in SI engines, exhibit poor performance at lower temperatures. This is one of the main reasons that tailpipe emissions drastically increase during cold-start periods. Thermal inertia of turbocharger casing prolongs the catalyst warm-up time. Exhaust enthalpy management becomes crucial for a turbocharged direct injection spark ignition (DISI) engine during cold-start periods to quickly heat the catalyst and minimize cold-start emissions. Thermal barrier coatings (TBCs), because of their low thermal inertia, reach higher surface temperatures faster than metal walls, thereby blocking heat transfer and saving enthalpy for the catalyst. The TBCs applied on surfaces that exchange heat with exhaust gases can increase the enthalpy available for the catalyst warm-up.

Range anxiety and lack of adequate access to fast charging are proving to be important impediments to electric vehicle (EV) adoption. While many techniques to fast charging EV batteries (model-based & model-free) have been developed, they have focused on a single Lithium-ion cell. Extensions to battery packs are scarce, often considering simplified architectures (e.g., series-connected) for ease of modeling. Computational considerations have also restricted fast-charging simulations to small battery packs, e.g., four cells (for both series and parallel connected cells). Hence, in this paper, we pursue a model-free approach based on reinforcement learning (RL) to fast charge a large battery pack (comprising 444 cells). Each cell is characterized by an equivalent circuit model coupled with a second-order lumped thermal model to simulate the battery behavior. After training the underlying RL, the developed model will be straightforward to implement with low computational complexity.

This paper proposes a novel reconfigurable battery balancing topology and reinforcement learning-based intelligent balancing management system. The different degradations cause a significant loss of battery pack available capacity, as the pack power output relies on the weakest cell due to the relevant physical requirements. To handle this capacity drop issue, a reconfigurable battery topology is adopted to improve the usability of the heterogeneous battery. There are some existing battery reconfigurable topologies in the literature. However, these studies rely on the limited options of topology designs, and there is a lack of study on the reconfigurability of these designs and other possible new designs. Also, it is rare to find an optimal management system for the reconfigurable battery topology. To fill these research gaps, this paper explores existing battery reconfigurable topology designs and proposes a new reconfigurable topology for battery balancing.

Designing an efficient cooling system with low power consumption is of high interest in the automotive engineering community. Heat generated due to the propulsion system and the on-board electronics in ground vehicles must be dissipated to avoid exceeding component temperature limits. In addition, proper thermal management will offer improved system durability and efficiency while providing a flexible, modular, and reduced weight structure. Traditional cooling systems are effective but they typically require high energy consumption which provides motivation for a paradigm shift. This study will examine the integration of passive heat rejection pathways in ground vehicle cooling systems using a “thermal bus”. Potential solutions include heat pipes and composite fibers with high thermal properties and light weight properties to move heat from the source to ambient surroundings.