Date of Publication :25th July 2024

Abstract:This study uses a multi objective optimization to the fuzzy management of gear changing in a car with an automatic manual transmission (AMT) Adaptive-weight genetic algorithms were used to find the best fuzzy membership functions in accordance with the input and output ranges and the weighted optimal control criteria, withthe goal of enhancing acceleration performance and loweringengine fuel consumption and emissions The car is depicted acting in a certain way. using longitudinal dynamicssimulations developed using the Simulink and the ADVISORTM fuel converter, which calculates engine emissions and fuel consumption. These calculations were based on the FTP-75 emissions test procedure that examines how the engine performs momentarily in relation to the hot and cold stages of the operating cycle. When the ideal fuzzy control hits the best balance between the optimization parameters demonstrated a 19.72% fuel savings linked to hydrocarbons (12.90%), carbon monoxide (29.50%), and nitrogen oxides (17.02%). to a typical gear shifting operation for a vehicle, there is a reduction in emissions and an improvement in acceleration performance gearbox with manual control. Moreover, the optimized fuzzy gear shifting control significantly enhances the relationship between fuel consumption and emissions as comparison to another ideal AMT control that only takes speed constraints into account.

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