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 ^™ interface, associated with the ADVISOR ^™ 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.

This study provides a detailed analysis of an optimal drivetrain configuration, based on multi‐cycles, for a plug‐in electric vehicle (EV). The investigation aims to identify the best EV configuration according to the required power and the transmissible traction torque. The study focuses on an EV with four different combinations of drive systems among in‐wheel motors and differential ones. To find out the best EV drive system configuration, it is adopted an optimisation process by means of a genetic algorithm that defines the electric motors (EMs) torque curves and powertrain transmission ratio in order to improve vehicle travel range and performance. The vehicle power demand is divided between the drive systems following rules established by the power management control which aims to reduce the lithium‐ion battery discharges during the driving cycles: FTP‐75 (urban driving), HWFET (highway driving) and US06 (high speeds and accelerations). After the simulations, the potential of each configuration is indicated according to its respective drive system and hence the best configurations are determined.

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

The reduction of vehicle fuel consumption is one of the most important targets for the automotive industry. Plug-in electric vehicles (PHEVs) are considered a viable alternative to improve the vehicle performance and efficiency. This study presents a prototype of a hybridization kit to convert a conventional vehicle into a PHEV that results in an expressive reduction of fuel consumption. Due to this, an interactive adaptive-weight genetic algorithm optimization was applied to find out optimum configurations for the hybridization kit in order to minimize the overall cost to perform FTP-75 and US06 driving cycles and to improve performance.