Refine Your Search

Search Results

Viewing 1 to 4 of 4
Technical Paper

Design Optimization of Bladed Disk Roots

1987-05-01
871052
A procedure for obtaining the optimum disk serration and blade root geometry is presented. The procedure uses the -finite element method, a suitable objective function and a standard mathematical programming technique as its basis. The objective functions investigated are the mean von Mises stress concentration factor, the coefficient of efficiency, and the hoop stress and radial stress concentration factors. The mathematical programming techniques considered are the Steepest Descent Technique, the Hill Algorithm and the Box Method. Results presented in this paper include the relative cost and the degree of success achieved by the design optimization procedure.
Technical Paper

ESS Design Process Overview and Key Outcomes of Year Two of EcoCAR 2: Plugging in to the Future

2014-04-01
2014-01-1922
EcoCAR 2: Plugging in to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 30 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The EcoCAR 2 VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.
Technical Paper

Improving Fuel Economy of Thermostatic Control for a Series Plugin-Hybrid Electric Vehicle Using Driver Prediction

2016-04-05
2016-01-1248
This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.
Technical Paper

Modeling and Simulation of Inverter Switching Characteristics for HEV BLDC Motors

2012-04-16
2012-01-1189
Although many simulations and analyses of three-phase insulated gate bipolar transistor (IGBT) switching devices exist in the offline and post processing arenas, real-time simulation environments require varying levels of fidelity of real-time capable models, depending on the task at hand. This paper presents a comparison between existing basic real-time modeling techniques and more advanced techniques capable of simulating complex electrical characteristics in high fidelity, while retaining the capability of real-time simulation. Model development, simulation, and analysis of results was performed at Mississippi State University in an effort to better understand the effects of multiple brushless direct current (BLDC) IGBT inverters operating on the same high-voltage bus.
X