Showing papers by "Giorgio Rizzoni published in 2000"

Mechatronic design and control of hybrid electric vehicles

Abstract: The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV). Toward these ends, four issues are explored. First, the development of HEV is presented. This synopsis includes a novel definition of degree of hybridization for automotive vehicles. Second, a load-leveling vehicle operation strategy is developed. In order to accomplish the strategy, a fuzzy logic controller is proposed. Fuzzy logic control is chosen because of the need for a controller for a nonlinear, multidomain, and time-varying plant with multiple uncertainties. Third, a novel technique for system integration and component sizing is presented. Fourth, the system design and control strategy is both simulated and then implemented in an actual vehicle. The controller examined in this study increased the fuel economy of a conventional full-sized vehicle from 40 to 55.7 mi/h and increased the average efficiency over the Federal Urban Driving Schedule from 23% to 35.4%. The paper concludes with a discussion of the implications of intelligent control and mechatronic systems as they apply to automobiles.

Optimal energy management in series hybrid electric vehicles

Abstract: This paper deals with the optimization of the instantaneous electrical generation/electrical storage power split in series hybrid electric vehicles (SHEV). Optimal energy management is related to the optimization of the instantaneous generation/storage power split in SHEV. Previously, a power split type solution of the series hybrid energy management problem has been attempted using a rule-based approach. Our approach performs a dynamic programming solution of the problem of determining the optimal power split between both sources of energy, with realistic cost calculation for all considered power trajectories for the combined APU/generator, electric machines and battery efficiencies, and a penalty function formulation for the deviation of the ideal state-of-charge to be sustained over the length of time considered. The discrete state formulation of this dynamic programming approach makes the computation very efficient. Results are obtained for series hybrids for the FUDS drive cycle.

The OSU-GATE program: Development of a graduate program in hybrid vehicle drivetrains and control systems at the Ohio State University

Abstract: This paper describes the development of a Department of Energy DOE) Graduate Automotive Technology Education (GATE) Center focusing on Hybrid Drivetrains and Control Systems. A hybrid electric vehicle (HEV) combines an electric drivetrain with an auxiliary power unit (APU). The APU is usually an internal combustion engine (ICE) or fuel cell. Properly designed HEVs synergistically maximize the advantages of their combined power plants while minimizing the disadvantages. HEVs offer excellent potential for reduced emissions and lower energy usage. Three major objectives have driven the development of the GATE graduate program: First, is the establishment of a laboratory environment that includes computer workstations for design and analysis, data acquisition and control hardware, a hybrid powertrain dynamometer test cell and a chassis (vehicle) dynamometer. Second, is the development of two new courses (one entitled HEV Energy Analysis of Hybrid-Electric Vehicles: and the other entitled Modeling, Simulation and Control of Hybrid Electric Vehicles). Third, is the integration of hybrid vehicle education into the MS Graduate Specialization in Automotive Systems Engineering.