Powertrain Optimization Methodology Based on Scalable Modeling

This paper introduces a powertrain optimization methodology aimed at optimizing powertrain parameters in hybrid electric vehicles. A Simulink model of a control-oriented hybrid electric vehicle, featuring scalable powertrain components, was developed. This model serves as an evaluation function for the genetic algorithm optimization process. The parameters subjected to optimization include those of the engine, electric motor, generator, battery, and gear ratios. A heuristic energy management strategy was employed, with a tunable parameter adjusted in proportion to engine scaling. The baseline powertrain parameters were selected from ADVISOR. The results of the optimization demonstrate a significant 26.83% reduction in fuel consumption when compared to reference hybrid vehicle. Additionally, the proposed methodology proved valuable for analyzing the individual parameter’s impact on overall fuel economy.