Identification Method of HEV Driving Condition Based on Wavelet Filtering and PSO Algorithm

doi:10.3969/j.issn.1001-2222.2017.02.012

Vehicle Engine ›› 2017, Vol. 0 ›› Issue (2) : 67-71. DOI: 10.3969/j.issn.1001-2222.2017.02.012

GUO Hailong1,2，ZHANG Yongdong1,2， ZHANG Shengbin1

Author information +

History +

Abstract

The recognition method of driving condition for the parallel series HEV based on wavelet filtering and PSO algorithm was put forward to identify the realtime road slope and vehicle load changes effectively so that the driver could adjust his driving behavior in time through the control strategy of driving system. The identification model of vehicle driving condition was established and the optimization objective function was determined by the least square method. Then the recognition
principle of driving condition based on wavelet filtering and PSO algorithm was studied. Finally, the recognition test of driving condition with the method was
conducted. The wavelet filtering, the recognition of driving road slope and vehicle load and the wavelet refiltering of vehicle test data were further conducted. The results show that the absolute average value of relative error for vehicle load and road slope is 2.71% and 3.85% respectively. Therefore, the proposed method is feasible.

Key words

hybrid electric vehicle（HEV） / least square method / particle swarm optimization (PSO) / wavelet filtering / identification

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

GUO Hailong，ZHANG Yongdong， ZHANG Shengbin. Identification Method of HEV Driving Condition Based on Wavelet Filtering and PSO Algorithm[J]. Vehicle Engine. 2017, 0(2): 67-71 https://doi.org/10.3969/j.issn.1001-2222.2017.02.012