Comprehensive parameter and electrochemical dataset for a 1 Ah graphite/LNMO battery cell for physical modelling as a blueprint for data reporting in battery research
Christina Schmitt, August Johansson, Xavier Raynaud, Eibar Joel Flores Cede\~no, John Mugisa, Dane Sotta, Agathe Martin, Nicolas Schaeffer, C\'edric Debruyne, Yvan Reynier, Simon Clark, Dennis Kopljar

TL;DR
This paper provides a detailed dataset and validation framework for a graphite/LNMO 1 Ah battery cell, promoting standardized data reporting and supporting physical modelling efforts in battery research.
Contribution
It introduces a comprehensive, validated dataset and software for physical modelling of a graphite/LNMO battery, serving as a blueprint for data reporting in the field.
Findings
Validated dataset supports physical modelling
Openly available data and software
Blueprint for standardized data reporting
Abstract
While current technology has enabled their widespread use, further improvements are needed for stationary, portable, and mobile applications, for example by the development of novel cathode materials. Digitalization of battery development, combining both experimental and modelling efforts is extremely valuable in this development. This is addressed in the present paper, where the authors present a comprehensive dataset for a graphite/LNMO 1 Ah pouch cell, including material, design, and electrochemical data. The dataset, validated through the BattMo modelling framework, supports physical modelling and aims to benefit the battery modelling community by offering a comprehensive resource for future studies. Both the dataset and the accompanying software for numerical validation is openly available and processed in such a way that it can serve as blueprint for reporting of comparable…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvancements in Battery Materials · Fiber-reinforced polymer composites · Machine Learning in Materials Science
