Control Strategy Optimization Method Based on Driving Cycle Recognition for HEV

doi:10.12068/j.issn.1005-3026.2017.04.020

Abstract

Abstract: Taking a parallel hybrid bus as research object， four kinds of typical working condition models were established， and the ant colony optimization algorithm was used to optimize the charge and discharge equivalent factor for each working condition in minimal equivalent fuel consumption control strategy. The relation between road gradient and adjustment of battery SOC target range was analyzed， and the corresponding gradient adaptive module was designed. A control strategy optimization method was proposed based on driving cycle recognition for HEV. The results of simulation and comparison analysis under typical working conditions showed that the method has very well driving condition adaptability， and its fuel economy is significantly higher than that of other several typical HEV control strategies.

Key words: HEV(hybrid electric vehicle), driving cycle recognition, ant colony optimization, SOC target range, control strategy

CLC Number:

U469.72

LIAN Jing， FAN Wu-ming， LI Lin-hui， YUAN Lu-shan. Control Strategy Optimization Method Based on Driving Cycle Recognition for HEV[J]. Journal of Northeastern University Natural Science, 2017, 38(4): 551-556.

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