Insight Generation from Information‐Dense Formation Protocols
Accelerated formation protocols that utilize pulsed charging offer an unprecedented wealth of electrochemical data. Herein, methods are presented to extract diagnostic data relating to pseudodiffusion coefficients, internal resistance, and others that give live insight into solid electrolyte interphase (SEI) growth. Specifically, a purely mathematical method is used to track formation progression in near‐real time and chart a path toward incorporation of adjustable pulse parameters for targeted SEI synthesis. The method and analysis are performed on 3 mAh cells but can also be applied to higher‐capacity cells.
Authors
- Leon Merker (ORCID: https://orcid.org/0000-0001-8959-3161)
- Bojing Zhang (ORCID: https://orcid.org/0000-0001-8709-3040)
- Helge S. Stein (ORCID: https://orcid.org/0000-0002-3461-0232)
- Shanling Ji (ORCID: https://orcid.org/0000-0001-6659-3767)
- Jun Yuan (ORCID: https://orcid.org/0000-0001-5936-3058)
Institutions
- Karlsruhe Institute of Technology (DE)
- Robotics Research (United States) (US)
- Helmholtz-Institute Ulm (DE)
- Max Planck Computing and Data Facility (DE)
- Bavarian Center for Applied Energy Research (DE)
- Technical University of Munich (DE)
Publication Details
- Journal
- Batteries & Supercaps
- Published
- 2025-06-03
- DOI
- https://doi.org/10.1002/batt.202500153
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- Center of Excellence for Learning in Education, Science and Technology
- European Commission
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung