On-line energy management for HEV based on particle swarm optimization
Author(s) -
Stéphane Caux,
D. Wanderley-Honda,
Daniel Hissel,
Maurice Fadel
Publication year - 2011
Publication title -
the european physical journal applied physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1286-0050
pISSN - 1286-0042
DOI - 10.1051/epjap/2010100248
Subject(s) - particle swarm optimization , energy management , robustness (evolution) , computer science , power flow , energy storage , mathematical optimization , control theory (sociology) , power (physics) , automotive engineering , energy (signal processing) , control engineering , engineering , electric power system , algorithm , mathematics , control (management) , gene , artificial intelligence , quantum mechanics , biochemistry , statistics , chemistry , physics
International audienceThis study considers a Hybrid Electrical Vehicle supplied by a Fuel Cell stack and supercapacitors used as Storage Element. In such an application, real time energy management is of paramount importance in order to increase autonomy and be able to deal on-line with perturbed power demand. Many offline power flow optimization principles are available but online algorithms are preferred and should be derived for optimal management of the instantaneous power splitting between the different available power sources. Based on particle swarm optimization algorithm, this study defines the parameters tuning of such algorithm. The final power splitting allows not only recovering energy braking but also is robust to some disturbances occurring during the trip. The solution provides good-quality and high-robustness results in a certain class of mission profile and power disturbance
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