Title :
Design optimization of a parallel hybrid electric powertrain
Author :
Gao, Wenzhong ; Porandla, Sachin Kumar
Author_Institution :
Center for Adv. Vehicular Syst., Mississippi State Univ., MS, USA
Abstract :
The design of a hybrid electric vehicle (HEV) involves a number of variables that must be optimized for better fuel economy and vehicle performance. In this paper, global optimization algorithms-DIRECT (Divided RECTangles), simulated annealing, and genetic algorithm are used for the design optimization of a parallel hybrid electric vehicle. Powertrain system analysis toolkit (PSAT) is used as the vehicle simulator for this study. The objective of this study is to increase the overall fuel economy of a parallel HEV on a composite of city and highway driving cycle and to improve the vehicle performance. A hybrid algorithm is also developed and is applied to Rosenbrook´s Banana Function for the examination of its efficiency.
Keywords :
design engineering; electrical engineering computing; fuel economy; genetic algorithms; hybrid electric vehicles; mechanical engineering computing; power transmission (mechanical); simulated annealing; DIRECT; Divided RECTangles; Rosenbrook´s Banana Function; fuel economy; genetic algorithm; global optimization algorithm; hybrid algorithm; hybrid electric vehicle; parallel hybrid electric powertrain; powertrain system analysis toolkit; simulated annealing; vehicle simulator; Analytical models; Cities and towns; Design optimization; Fuel economy; Genetic algorithms; Hybrid electric vehicles; Mechanical power transmission; Road transportation; Road vehicles; Simulated annealing; DIRECT; Design Optimization; Genetic Algorithm; Hybrid Electric Vehicle; Hybrid Optimization Algorithm; Simulated Annealing;
Conference_Titel :
Vehicle Power and Propulsion, 2005 IEEE Conference
Print_ISBN :
0-7803-9280-9
DOI :
10.1109/VPPC.2005.1554609
