DocumentCode
3283029
Title
Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms
Author
Piccolo, Antonio ; Ippolito, Lucio ; Galdi, Vincen Zo ; Vaccaro, Alfredo
Author_Institution
Dept. of Electron. & Electr. Eng., Salerno Univ., Italy
Volume
1
fYear
2001
fDate
2001
Firstpage
434
Abstract
Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can offer a sensible improvement of the overall vehicle environmental impact achieving at the same time a rational energy employment. The main task of an energy flow management unit is to split the instantaneous vehicle power demand between the internal combustion engine and the electric motor ensuring that the power sources are operated at high efficiency operating points and the related vehicle emissions are minimised. This paper presents an original methodology for the tuning of the characteristic parameters. The proposed methodology identifies, using the genetic algorithm, the value of the energy flow management parameters that minimize the cost function in terms of fuel consumption and emissions. Some interesting simulation results are discussed to prove the validity of the methodology, which contributes to a substantial reduction of the pollutant emissions from hybrid electric vehicles
Keywords
electric motors; energy management systems; genetic algorithms; internal combustion engines; road vehicles; tuning; cost function; electric motor; energy flow management; genetic algorithms; hybrid electric vehicles; internal combustion engine; optimisation; parameter tuning; pollutant emissions; Cost function; Electric motors; Employment; Energy management; Fuels; Genetic algorithms; Hybrid electric vehicles; Internal combustion engines; Mechanical power transmission; Power demand;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Intelligent Mechatronics, 2001. Proceedings. 2001 IEEE/ASME International Conference on
Conference_Location
Como
Print_ISBN
0-7803-6736-7
Type
conf
DOI
10.1109/AIM.2001.936493
Filename
936493
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