Automated Electrolyte Formulation and Coin Cell Assembly for High-Throughput Lithium-Ion Battery Research
Authors: Yik Jackie , Zhang Leiting , Sjölund Jens , Hou Xu , Svensson Per , Edström Kristina , Berg Erik J. 
Published: February 2024

Battery research involving performance evaluation by battery cycling is a time-consuming process with sometimes large cell-to-cell variations. To enhance the rate of testing and reproducibility, automation is one accelerator that can be used. In this study, we introduce ODACell1 (Figure 1), an automated system for high-throughput electrolyte screening and Li-ion cell assembly.

ODACell enables the assembly of Li-ion battery cells in ambient atmosphere. We use LiFePO4||Li4Ti5O12-based full cells with a dimethyl sulfoxide-based model electrolyte for demonstrating the automated system. We investigate the reproducibility of the automated system on this system as well as the influence of water on the performance of the model electrolyte when exposed to ambient air. Our system is uniquely equipped for combinatorial experimentation, combining electrolyte formulation, batch coin cell assembly, and cycling into a single system. The integration of multiple setups into a single unit is made possible, overcoming the incompatibility issues with integrating multiple, different automated systems together.

While there are several other robotic systems besides ODACell for battery material acceleration, ODACell’s unique combination of robots incorporated in the robotic setup allow a broader set of tasks to be automated. For example, Clio by Dave et al.
2 focus on electrolyte formulation and characterization, lacking assembly of battery cells; AutoBASS by Zhang et al.
3 only automates battery assembly; and Poseidon by Svensson et al.
4 can automate electrolyte formulation, characterization, and coin cell assembly, but lack high-throughput batch processing.

ODACell ensures high reproducibility, which is needed to establish robust models and determine accurate electrochemical and physical properties. The system assembled 131 coin cells, with a conservative fail rate of only 5%. The relative standard deviation of the discharge capacity after 10 cycles was 2% for the studied system. The investigation into the influence of water on cycling performance show overlapping performance trends between coin cells with 2 vol% and 4 vol% of water in the electrolyte highlighting the nontrivial relationship between water and cycling performance.

In conclusion, ODACell is a promising tool for high-throughput electrolyte screening and Li-ion cell assembly. It builds upon previously published robotic setups by automating more of the research workflow. Our study highlights the importance of reproducibility in battery research and the complex relationship between water contaminants in electrolytes and cycling performance. ODACell has the potential to be a useful tool for advancing battery research by enhancing the rate of testing and reducing the variation of results.

References:

[1]J. T. Yik, et al., Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research, ChemRxiv, 2022. This content is a preprint and has not been peer-reviewed.

[2] A. Dave, et al., Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning, Cell Reports Physical Science, 2020, 1, 100264.

[3] B. Zhang, et al., Robotic cell assembly to accelerate battery research, Digital Discovery, 2022, 1, 755–762.

[4] P. H. Svensson, et al., Robotised screening and characterisation for accelerated discovery of novel Lithium ion battery electrolytes: Building a platform and proof of principle studies, Chemical Engineering Journal, 2023, 455, 140955.

Figure 1