Search Results

The powertrain simulation tools are nowadays an efficient support to optimize cost and duration of the whole engine technological developments. They can deliver optimized simulator versions for various targets such as system understanding, design investigation, non-measurable value access or virtual bench use for control and calibration. Under the condition of an accurate modelling and simulation know-how to take into account the simulator using constraints, the simulation can become an undisputable support for powertrain design as the test bed already is. The goal of this paper is to present the large range of the powertrain simulation capabilities for the specific application of a downsized turbocharged GDI engine with twin VVT embedded in a concept car. The modelling framework is first presented and different items are laid-out. A first part is dedicated to the engine air path and in particular to the modelling of gas exchange phenomena such as back-flow.

Due to more and more complex powertrain architectures and the necessity to optimize them on the whole driving conditions, simulation tools are becoming indisputable for car manufacturers and suppliers. Indeed, simulation is at the basis of any algorithm aimed at finding the best compromise between fuel consumption, emissions, drivability, and performance during the conception phase. For hybrid vehicles, the energy management strategy is a key driver to ensure the best fuel consumption and thus has to be optimized carefully as well. In this regard, the coupling of an offline hybrid strategy optimizer (called HOT) based on Pontryagin’s minimum principle (PMP) and an online equivalent-consumption-minimization strategy (ECMS) generator is presented. Additionally, methods to estimate the efficiency maps and other overall characteristics of the main powertrain components (thermal engine, electric motor(s), and battery) from a few design parameters are shown.

This paper presents a methodology to design the powertrain of an electrical vehicle (EV) in an optimal way. The electric vehicle optimal design is carried out using multiobjective genetic optimization algorithm. The developed methodology is based on the coupling of a genetic algorithm with powertrain component models. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all the components interactions. In this way, the components can be sized taking into account the whole system behavior in an optimal global design. The developed methodology is performed on the European driving cycle (NEDC) to estimate energy consumption gains but also powertrain mass reduction in comparison with a classical step-by-step methodology. This optimal procedure is notably important to increase electric vehicle range or reduce battery size and thus electric vehicle cost.