Environomic Design of Vehicle Integrated Energy System - Application on a Hybrid Electric Vehicle Energy System

With the increasing trend of mobility of the human population, vehicles have to face the problem of primary energy resources scarcity. The vehicles need higher efficiency and better adaptation to the alternative energy sources. The need to improve the efficiency of the vehicle energy system motivates to search for innovative solutions during the design process. The main design criteria for modern sustainable development of vehicle powertrain are the high energy efficiency of the conversion system, the competitive cost and the lowest possible environmental impacts. These objectives are most of the time antagonistic. To cope with this challenge the automotive engineers need a structured optimization methodology. A multi-objective optimization methodology is being applied as search for the best powertrain design solutions. This kind of approach named "multi-objectives optimization" is based on genetic algorithms, which are based on the process of natural selection. An innovative decision- making methodology, using this optimization technic is currently under development at PSA Peugeot Citroen. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions are optimal from a technical, economic and environmental point of view. In this article the methodology is applied on a hybrid electric vehicle study in order to define the powertrain configuration of the vehicle, estimate the cost of the powertrain equipment and show the environmental impact of the technical choices on the lifecycle perspective of the vehicle. For that a physical model of a hybrid electric vehicle is made. This model is coupled with a cost model for the vehicle and life cycle assessment (LCA) technics are used for the environmental assessment. After multi-objective optimization, the outcoming solutions from the Pareto frontiers curve are analysed.