Matching and Optimization for Powertrain System of Parallel Hybrid Electric Vehicle

Abstract

The parameters matching of the hybrid powertrain system of the hybrid electric vehicle has a directly impact on the performance of the vehicle dynamic and the fuel economy. The preliminary match of the powertrain system base on analysis of Driving Cycle is done, then the software of AVL-Cruise and Matlab are integrated with Isight to optimize parameters of match, by using the Multi-Island GA and NLPQL to establish the combinatorial optimization algorithm. The results show that the fuel economy have been improved by 10.92% without sacrificing the dynamic performance and under the premise of ensuring the limits of the state of charge of battery.