A battery modeling platform for broad, consistent, and automated life cycle assessments and cost studies (B-LEAP)

Abstract

Lithium-ion batteries are essential for consumer electronics, stationary storage systems, and especially, electromobility, but are expensive and have a substantial environmental footprint. To improve the sustainability and cost-effectiveness of batteries, innovative battery design and production processes across the entire value chain are currently under development. Life cycle assessment and cost analysis can help support such developments by providing direct feedback and optimizing technical decisions from an economic and environmental point of view. Batteries and their production, however, are complex and characterized by many technical parameters that influence their performance. Current sustainability assessment models lack the engineering refinement to capture the influence of relevant parameters and simulate battery manufacturing processes that are useful for technical decision-making. In this work, a new Python-based modeling platform for technical, environmental, and economic assessments of batteries is presented. The platform aims to offer decision support in different use cases from optimization of specific parameters to broader strategic analysis. Furthermore, due to its flexible and modular structure, models of new battery technologies, machines, and process routes can be easily integrated in the framework. After describing the platform structure and models, the capabilities are illustrated by performing an environmental and economic assessment considering different battery chemistries and material sourcing strategies following the plans for a planned gigafactory in France. The results highlight the potential of the platform to become a powerful tool for future development of batteries from a research, industrial, and policy-making perspective.