Multi-speed gearbox design and shifting control optimization to minimize fuel consumption, exhaust emissions and drivetrain mechanical losses

Internal combustion engine vehicles (ICEVs) still represent a major fraction of the global vehicle market and enhancements of conventional vehicle powertrain design have been considered a viable large-scale alternative to reach short-term sustainable goals focused on the reduction of air pollutant emissions and fuel consumption. Thus, the purpose of this paper is to employ a multi-objective optimization for the ICEV drivetrain design and gear shifting control aiming at the minimization of fuel consumption, exhaust emissions and gearbox power losses. The optimization problem is solved by the Interactive Adaptive-Weight Genetic Algorithm (i-AWGA) and comprises different design variables of the multi-speed transmission and differential system, considering constructive constraints. The i-AWGA procedure also optimizes the fuzzy logic shifting controller by defining its input and output membership functions, fuzzy rules and respective weights. The vehicle model is evaluated under a combined driving cycle, therefore robust powertrain configurations can be obtained by the optimization process. The best trade-off solution results in the reduction of gas emissions in 2.32% HC, 3.44% CO and 23.78% NOx, along with the 15.6% fuel savings, facing the standard vehicle.

Automated summary

This study aims to minimize fuel consumption, emissions, and gearbox power losses through multi-objective optimization of internal combustion engine vehicle powertrain design and gear shifting control. Using the i-AWGA algorithm, the optimization process resulted in a solution that reduces emissions and saves fuel.