This study presents a multiobjective optimisation applied to the gear shifting fuzzy control of a vehicle equipped with an automated manual transmission (AMT), aiming to improve acceleration performance and reduce engine fuel consumption and emissions. An Adaptive-Weight Genetic Algorithm was...

This study presents a multiobjective optimisation applied to the gear shifting fuzzy control of a vehicle equipped with an automated manual transmission (AMT), aiming to improve acceleration performance and reduce engine fuel consumption and emissions. An Adaptive-Weight Genetic Algorithm was employed to find optimum fuzzy membership functions, according to the input and output ranges, and also optimum control rules with their respective weights. The vehicle behaviour is represented by longitudinal dynamics simulations developed in Simulink/Matlab((TM)) interface, associated with the ADVISOR((TM)) fuel converter block, that provides the engine emissions and fuel consumption. These simulations were based on the FTP-75 emissions test procedure, that considers cold and hot phases of the driving cycle evaluating the engine transient operation as a function of the catalyst efficiency during the warm-up period. The optimum fuzzy control with the best trade-off among the optimisation criteria presented 19.72% fuel saving associated with 12.90% hydrocarbon, 29.20% carbon monoxide and 17.02% nitrogen oxides emissions reduction and an acceleration performance improvement when compared to a standard gear shifting procedure for a manual controlled gearbox. Moreover, the optimised fuzzy gear shifting control, improves the relationship between fuel consumption and emissions significantly, when compared to another optimum AMT control based on speed limits only

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ

COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

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