Uncovering the impact of battery design parameters on health and lifetime using short charging segments
Battery design is linked to real-world applications using a battery-design-aware machine learning framework built on a digital twin model, ultimately contributing to safer and longer-lasting next-generation batteries.
Authors
- Søren Byg Vilsen (ORCID: https://orcid.org/0000-0002-9718-8147)
- Wendi Guo (ORCID: https://orcid.org/0000-0001-5832-8971)
- Daniel Brandell (ORCID: https://orcid.org/0000-0002-8019-2801)
- Daniel‐Ioan Stroe (ORCID: https://orcid.org/0000-0002-2938-8921)
- Yaqi Li
- Ashima Verma (ORCID: https://orcid.org/0000-0003-3750-0597)
Institutions
- Uppsala University (SE)
- Aalborg University (DK)
Publication Details
- Journal
- Energy & Environmental Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ee03268g
- Citations
- 3
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 2.05
Funders
- Energimyndigheten
- Nordic Energy Research