Search Results

In regard to the evaluation of the performance of a hybrid electric vehicle (HEV), there are as many simulation methods as there are developers or researchers. They adopt different operational algorithms and they use diverse techniques to realize their logic. However, the relation among the various simulation methods has not been clearly defined. Thus, it is not easy to choose a method that would bring the best consequences in the most efficient way. Here, we present three types of backward-looking simulation algorithms for evaluating the fuel efficiency of a power-split HEV. Then the results and cost-effectiveness of each algorithm are analyzed using various component ratings over a representative driving mode. Based on the comparative analysis, the algorithm that uses equivalent fuel consumption is shown to be highly cost-effective. Also, an inductive or empirical base is set up with the results for a component sizing methodology using the recommended simulation.

This paper is concerned with the energy management strategy of hybrid electric vehicle using stochastic dynamic programming. The aim is the control strategy of the power distribution for hybrid electric vehicle powertrains to minimize fuel consumption while maintaining drivability. The fuel economy of hybrid electric vehicle is strongly influenced by power management control strategy. Rule-based control strategy is popular strategy thanks to its effectiveness in real-time implementation, but rule should be designed and efficiency of entire drive trains is not optimized. Dynamic programming, one of optimization-based control strategy presents outstanding performance, but cannot be used as real-time control strategy directly, since its non-causal property and drawback that global optimal solution can only be obtained for specific driving cycle. In this paper, stochastic dynamic programming is applied to parallel hybrid electric vehicle to optimize vehicle performance in average sense.