Vehicle manufactures have adopted different strategies for improving the environmental performance of their fleet including lightweight design and alternative drivetrains such as EVs. Both strategies reduce energy during use but may result in a relative increase of the impact during other stages. To address this, a lifecycle approach is needed when vehicle design strategies are developed. The thesis explores the extent that such a lifecycle approach is adopted today and assesses the potential of these strategies to reduce the lifecycle impact of vehicles. Moreover it aims to contribute to method development for lifecycle considerations during product development and material selection. Current practices were explored in an empirical study with four vehicle manufacturers. The availability of tools for identifying, monitoring and assessing design strategies was explored in a literature review. The results of the empirical study showed that environmental considerations during product development often lack a lifecycle perspective. Regarding the use of tools a limited number of such tools were utilized systematically by the studied companies despite the numerous tools available in literature. The influence of new design strategies on the lifecycle environmental performance of vehicles was assessed in three case studies; two looking into lightweight design and one at EVs. Both strategies resulted in energy and GHG emissions savings though the impact during manufacturing increases due to the advanced materials used. Assumptions relating to the operating conditions of the vehicle e.g. lifetime distance or for EVs the carbon intensity of the energy mix, influence the level of this tradeoff. Despite its low share in terms of environmental impact EOL is important in the overall performance of vehicles. The thesis contributed to method development by suggesting a systematic approach for material selection. The approach combines material and environmental analysis tools thus increases the possibilities for lifecycle improvements while minimizing risk for sub-optimizations.
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