Study on HEV’s driving condition recognition method based on PSO algorithm

• Published in: Journal of intelligent & fuzzy systems Vol. 35; no. 1; pp. 87 - 98
• Main Authors: Hailong, Guo, Yi, Wei
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2018; Sage Publications Ltd
• Subjects: Algorithms; Driving; Driving conditions; Hybrid electric vehicles; Parameter identification; Particle swarm optimization; Recognition; Slopes; road grade estimation; particle swarm optimization(PSO); series –parallel HEV; Least squares methods; mass estimation
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-169570

In the interest of the hybrid electric vehicle(HEV) real-time road gradient and vehicle load(driving condition) effective identification during the running process, this work takes the series–parallel HEV as the research object and studies on the dynamic identification mechanism of slope and load, based on the analysis of its structural parameters. Firstly, vehicle’s driving condition identification model is developed, and the optimization goal function is established using the least square method. Secondly, six different kinds of particle swarm optimization(PSO) algorithm are used for the recognition of vehicle’s driving condition, and the results show that hybrid PSO algorithm based on hybrid training algorithm has better calculation accuracy for this problem. Finally, Experiments are carried out to verify the driving condition recognition method based on PSO algorithm. Through the acquisition of a real vehicle data during the running process, road grade and vehicle mass are estimated by using the proposed method, and the effectiveness of the proposed method is proved through comparison of errors between recognition results and true value.