Product-requirement-model to approach the identification of uncertainties in battery systems development

Abstract

Electric mobility is on the verge of becoming a mass market. Major automotive OEMs have initiated programs to electrify their product portfolio. This transition poses new challenges and requires new innovative concepts in automotive development processes, especially for battery systems as the key component within electric powertrains. Battery system costs account for up to 40% of the electric vehicle’s total costs. Additionally, development cycles of battery systems for automotive applications are characterized by long development periods. Hence, the initiatives to advance electrification result in numerous development projects affiliated with significant development expenses. Battery systems can be referred to as mechatronic and electrochemical systems. They require a complex interaction of diverse scientific and engineering disciplines. Fast innovation cycles have effects regarding product requirements and assumptions towards their allocation. Hereby, uncertainties can lead to risks within development projects, especially in terms of time and costs. In current development processes, necessary changes are only dealt with reactively, causing unplanned additional expenses and delays. Thus, there is need for handling potential changes proactively, i.e. managing uncertainties leading to those changes as early as possible. New methods are necessary to identify and handle uncertainties of complex product systems within requirements engineering. An approach towards comprehensive uncertainty management is taken within this publication.

Productization and product structure enabling BIM implementation in construction

“…PS is commonly used in various fields as product architecture (Belkadi et al, 2016), configuration model (Li et al, 2010;Mannisto et al, 1998), PS tree (Li et al, 2013) and modular PS (Windheim et al, 2017). A generic PS is an organised hierarchical and structural representation (Bri ere-Côt e et al, 2010; Chu et al, 2009;Heimes et al, 2020;Pinqui e et al, 2015) illustrating product's composition and assembly relationships (Belkadi et al, 2016;Binbin et al, 2011;Christ et al, 2013;Lu et al, 2011;S ¸enaltun and Cangelir, 2013). PS consists of either abstract or physical elements (Riesener et al, 2020;Sarinko et al, 2005), cover entire product families, show which modules and part types or classes are used in products or product families (Christ et al, 2013;Magnusson and Pasche, 2014;Mannisto et al, 1998;Venkatesh, 2015).…”

Battery cost forecasting: a review of methods and results with an outlook to 2050

“…[5][6][7] Since both battery applications are supporting the combat against climate change, the increase of their market share is currently supported by the regulation of policy makers. [8][9][10][11] Yet, during the last years, battery costs and especially those of lithium-ion batteries (LIBs), the state-of-the-art technology for EVs 12,13 and potentially the most cost-effective technology for stationary energy storage, 14,15 has rapidly declined driven by improvements in cell chemistry, process technology and the increase of production scale. 1,3,16,17 Additionally, beside the optimization of LIBs, advanced lithium-based concepts such as solid-state batteries (SSBs), lithium-sulfur batteries (LSBs) and lithium-air batteries (LABs) are discussed intensely regarding 2.…”