Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery researchShow others and affiliations
2023 (English)In: Digital Discovery, E-ISSN 2635-098X, Vol. 2, no 3, p. 799-808Article in journal (Refereed) Published
Abstract [en]
Battery cell assembly and testing in conventional battery research is acknowledged to be heavily time-consuming and often suffers from large cell-to-cell variations. Manual battery cell assembly and electrolyte formulations are prone to introducing errors which confound optimization strategies and upscaling. Herein we present ODACell, an automated electrolyte formulation and battery assembly setup, capable of preparing large batches of coin cells. We demonstrate the feasibility of Li-ion cell assembly in an ambient atmosphere by preparing LiFePO4‖Li4Ti5O12-based full cells with dimethyl sulfoxide-based model electrolyte. Furthermore, the influence of water is investigated to account for the hygroscopic nature of the non-aqueous electrolyte when exposed to ambient atmosphere. The reproducibility tests demonstrate a conservative fail rate of 5%, while the relative standard deviation of the discharge capacity after 10 cycles was 2% for the studied system. The groups with 2 vol% and 4 vol% of added water in the electrolyte showed overlapping performance trends, highlighting the nontrivial relationship between water contaminants in the electrolytes and the cycling performance. Thus, reproducible data are essential to ascertain whether or not there are minor differences in the performance for high-throughput electrolyte screenings. ODACell is broadly applicable to coin cell assembly with liquid electrolytes and therefore presents an essential step towards accelerating research and development of such systems.
Place, publisher, year, edition, pages
RSC Publishing, 2023. Vol. 2, no 3, p. 799-808
National Category
Materials Chemistry
Identifiers
URN: urn:nbn:se:ri:diva-66443DOI: 10.1039/d3dd00058cOAI: oai:DiVA.org:ri-66443DiVA, id: diva2:1794391
Note
This research was financially supported by the Swedish Energy Agency (Grant 50119-1), Stiftelsen för Strategisk Forskning (SSF, FFL18-0269), Knut and Alice Wallenberg (KAW) Foundation (Grant 2017.0204) and StandUp for Energy for base funding. This research was also supported by Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the KAW Foundation.
2023-09-052023-09-052024-02-29Bibliographically approved