Optimization of HEV energy management strategy based on driving cycle modeling

Abstract

The fuel economy of hybrid electric vehicle (HEV) is sensitive to its driving cycle and energy management strategy. To improve the fuel economy of HEV, identification of driving condition and optimization of energy management strategy have drawn much attention over the last few years. Due to strong uncertainty with driving environment and traffic congestion, the Generalized Radial Neural Network (GRNN) is adopted to model and predict driving cycle in this paper. Then dynamic programming (DP) algorithm was improved and implemented in the HEV energy management strategy. Finally, simulation is carried out, and the results indicate that the fuel consumption of HEV could be decreased significantly based on the improved DP algorithm and driving cycle modeling presented in this paper.