A Hierarchical Database Schema with High-Level Indicators for Recycling Processes of Lithium-ion Batteries

The presence of strategic metals and critical raw materials in spent LIB makes recycling this material stream an essential topic for industry, academics and society. Current developments in recycling technology are leading to generating a substantial amount of process data from diverse recycling strategies for LIBs. However, large amounts of these data generated during the experimental trails are currently not being saved or managed in a way that can be accessed, processed or analyzed quickly. The insufficient data handling performance in this area limits the potential of applying different digital tools for high-level process assessments and supporting decision-making for parameter optimizations.

In this study, a hierarchical database schema with high-level indicators is proposed for the ongoing digitalization of the recycling processes of Lithium-Ion Batteries. The indicators related to environmental compatibility, productivity, and product quality are quantified and linked to the corresponding recycling routes. All experiment-related data are saved into a structured database: experiment data, process-related data, initial setups, operational data, and monitoring data. From top to bottom, more technical details are stored within the defined structure and information is processed and linked to leading process indicators. Results indicate the potential for this data-handling strategy not only to maximize the recovery efficiency of strategic and critical materials but to expand the process optimization to online analytics, application of machine learning algorithms and support the decision-making in research on recycling LIBs.

This work has received funding from the Federal Ministry of Education and Research (BMBF), support code DiRectION (03XP0358A).