Effect of regenerative braking energy on battery current balance in a parallel hybrid gasoline-electric vehicle under FTP-75 driving mode

In recent years, a hybrid electric vehicle (HEV) has been considered a successful technology. Especially, in case of a full HEV, the motor can drive the vehicle by itself at low velocity or assist the engine at high load. To improve the hybrid electric vehicle’s efficiency, a regenerative braking system is also applied to recover from kinetic energy. In this study, an experimental control apparatus was set up with a parallel hybrid electric vehicle mounted on a chassis dynamometer to measure ECU (engine control unit) and MCU (motor control unit) signals, including the current and state of charge in the battery. In order to analyze regenerative braking characteristics, user define braking driving cycle was introduced and carried out using different initial velocities and braking times. The FTP 75 driving cycle was then adapted under different initial SOC (state of charge) levels. The experiment data was analyzed in accordance with the vehicle velocity, battery current, instant SOC level, motor RPM, engine RPM, and then vehicle driving mode was decided. In case of braking driving cycle, it was observed that SOC were increased up to 1.5 % when the braking time and the velocidy were 6 second and 60 km/h, respectively. In addition, using the FTP 75 driving cycle, mode 1 was most frequently operated at SOC 65 conditions in phase 1. In phase 2, due to frequent stop-go hills, percentage of mode 1 was increase by 22 %. Eventually, despite of identity, it was shown that the characteristics of phase 3 differed from phase 1 due to the evanishment of the effects of initial SOCs.     

Novel large scale simulation process to support dot’s cafe modeling system

This paper demonstrates a new process that has been specifically designed for the support of the U.S. Department of Transportation’s (DOT’s) Corporate Average Fuel Economy (CAFE) standards. In developing the standards, DOT’s National Highway Traffic Safety Administration made use of the CAFE Compliance and Effects Modeling System (the “Volpe model” or the “CAFE model”), which was developed by DOT’s Volpe National Transportation Systems Center for the 2005–2007 CAFE rulemaking and has been continuously updated since. The model is the primary tool used by the agency to evaluate potential CAFE stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The Volpe model relies on numerous technology-related and economic inputs, such as market forecasts, technology costs, and effectiveness estimates; these inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology “decision trees”. Part of the model’s function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technology to specific vehicles in its product line. A significant number of inputs to the Volpe decision-tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Argonne National Laboratory has developed a fullvehicle simulation tool named Autonomie, which has become one of the industry’s standard tools for analyzing vehicle energy consumption and technology effectiveness. Full-vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, and hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual and combined fuel-saving technologies. The Large-Scale Simulation Process accelerates and facilitates the assessment of individual technological impacts on vehicle fuel economy. This paper will show how Argonne efficiently simulates hundreds of thousands of vehicles to model anticipated future vehicle technologies.     

Identifying peer states for transportation system evaluation &; policy analyses

The majority of comparisons between state transportation systems do not control for characteristics that may vary greatly between states (e.g., vehicle miles traveled). A shortcoming of such analyses is that a state’s individual characteristics can be highly influential in determining how transportation policy is set and funds are spent. The purpose of this paper is to extend previous efforts to create groups of similar peer states by developing a new methodological framework that incorporates demographic, temporal, and locational variability into the peer group delineations. We collected historical data for 42 variables on transportation infrastructure, population, economy, growth, topography and weather. To examine trends before and after the passage of ISTEA we gathered data over two time periods: 1985 through 1990 and 1995 through 2000. Using principal components analysis (PCA) we reduced variables into seven components, and then statistically clustered states into peer groups for each time period based on the components and the remaining variables. We identified a range of cluster solutions and demonstrate how cluster statistics help to describe the contextual basis behind the peer grouping. The results of this study are to provide government agencies, researchers and the public with a systematic methodological framework for identifying peer states that reflect similar attributes contributing to the development and maintenance of state transportation systems.

A probabilistic framework for weather-based rerouting and delay estimations within an Airspace Planning model

In this paper, we develop a novel severe weather-modeling paradigm to be applied within the context of a large-scale Airspace Planning and collaborative decision-making model in order to reroute flights with respect to a specified probability threshold of encountering severe weather, subject to collision safety, airline equity, and sector workload considerations. This approach serves as an alternative to the current practice adopted by the Federal Aviation Administration (FAA) of adjusting flight routes in accordance with the guidelines specified in the National Playbook. Our innovative contributions in this paper include (a) the concept of “Probability-Nets” and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service; (c) the generation of flight plans that circumvent severe weather phenomena with specified probability threshold levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Additionally, we conduct an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. Computational results and insights are presented based on flight test cases derived from the Enhanced Traffic Management System data provided by the FAA and using weather scenarios derived from the Model Output Statistics forecast data provided by the National Weather Service.     

Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida

In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging population-focused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.     

New nonlinear bushing model for general excitations using Bouc-Wen hysteretic model

Because the characteristics of rubber bushing significantly affect the accuracy of vehicle dynamics simulations, they should be accurately modeled in the vehicle suspension model. In this paper, a new nonlinear bushing model for automotive bushing components is developed to improve the accuracy of vehicle dynamics analysis. Bushing components were first tested to capture the nonlinear and hysteretic behavior of typical elements by using a MTS 3-axis elastomer tester. A simple Bouc-Wen hysteretic differential model was modified to generate a more precise rubber bushing model. A sine wave, step input, and random excitations are imposed on the bushing. The ADAMS program is used to calculate sensitivity and the VisualDOC program is employed to find the optimal parameters for the bushing model. An error function is designed to find optimal parameters of the model. Parameter identification is carried out to satisfy the static and dynamic characteristics due to sine and step excitation inputs. It was proved that the proposed model could predict the bushing forces under sine, step, and random inputs well. The errors are within 10% in the overall range. To show the validity of the proposed model, a numerical example was also carried out. Because the bushing forces due to random excitation input show good agreement with experiments, the proposed bushing model is available in the vehicle dynamics simulation.     

Fatigue life prediction based on the rainflow cycle counting method for the end beam of a freight car bogie

This paper presents a system for treating of the actual measured data for load histories. The approach consists of two steps: stress analysis and fatigue damage prediction. Finite element analysis is conducted for the component in question to obtain detailed stress-strain responses. A significant number of failures occurred in a brake end beam which led to economic losses and disruption of service. The cracks appeared to be fatigue cracks caused by the dynamic load produced in the loaded bogie frame. Strain gauge data were analyzed, and fatigue cycles were calculated from this data. Rainflow cycle counting was used to estimate cumulative damage of the end beam under in-service loading conditions. The fatigue life calculated with the rainflow cycle counting method, the P-S-N curve, and the modified Miner’s rule agreed well with actual fatigue life within an error range of 2.7%~31%.     

EXPRESSION CLONING OF A PROTECTIVE LEISHMANIA ANTIGEN

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