Predictive sustainability analysis applied to an automotive design case study.

Abstract

The paper deals with a predictive sustainability analysis applied to the design of automotive components in lightweight perspective. The analysis is conducted through the integration of the traditional Life Cycle Assessment (LCA) methodology with tailored forecasting algorithms able to provide a predictive evaluation of Climate Change (CC) by elaboration data contained in commercial environmental datasets. The comparison is referred to the entire Life Cycle (LC) of the system (including production, use and End-of-Life) according to a “from cradle to grave approach”.A medium-class car rear crash management system is used as case study, assessing the potential benefits related to the substitution of conventional steel with 6000/7000 series aluminium alloys, along with other minor design changes. Particularly, the study compares the environmental profile of the two solutions based on the CC impact category in application to both an Internal Combustion Engine Vehicle (ICEV) and a Battery Electric Vehicle (BEV).The results show the potentiality of the proposed methodology, highlighting possible improvements/worsenings: new materials and manufacturing technologies adopted in the lightweight rear crash management system entail contrasting environmental effects depending on LC phases, that is, increased CO2eq in production (around 125% - mainly due to the strong energy intensity of aluminium supply chain) and reduced burdens in use and EoL (primarily provided by component mass reduction). That said, the influence of different boundary conditions on the predictive models is significant only for the electricity produced to energize the BEV configuration, leading to an overall variability of comparison results ranging within 3-30% for the BEV case study.