ALBATROSS: A robotised system for high-throughput electrolyte screening via automated electrolyte formulation, coin-cell fabrication, and electrochemical evaluation

As battery technologies advance toward higher stability and energy density, the need for extensive cell-level testing across various component configurations becomes critical. To evaluate performance and understand the operating principles of batteries in laboratory scale, fabrication and evaluation of coin cells are essential processes. However, the conventional coin-cell assembly and testing processes require significant time and labor from researchers, posing challenges to high-throughput screening research. In this study, we introduce an Automated Li-ion BAttery Testing RObot SyStem (ALBATROSS), an automated system capable of electrolyte formulation, coin-cell assembly, and electrochemical evaluation. The system, integrated within a argon-filled glovebox, enables fully automated assembly and testing of up to 48 cells without researcher intervention. By incorporating custom-designed robot gripper and 3D-printed structures optimized for precise cell handling, ALBATROSS achieved high assembly reliability, yielding a relative standard deviation (RSD) of less than 1.2% in discharge capacity and a standard deviation of less than 3 Ω in EIS measurements for NCM811||Li half cells. Owing to its high reliability and automation capability, ALBATROSS allows for the acquisition of high-quality coin-cell datasets, which are expected to accelerate the development of next-generation electrolytes.

💡 Research Summary

The paper presents ALBATROSS, an integrated robotic platform that fully automates electrolyte formulation, 48‑cell coin‑cell assembly, and electrochemical testing—including both charge‑discharge cycling and electrochemical impedance spectroscopy (EIS)—inside an argon‑filled glovebox. The system combines a six‑axis robot arm (xArm6), an OT‑2 liquid‑handling workstation, a customized gripper (vacuum and parallel modes), and a modified coin‑cell crimper. Electrolyte preparation is performed by heating solid solvents (e.g., EC) to 60 °C, dispensing precise volumes, removing tip residue, and mixing 20 times over three minutes to ensure homogeneity.

Cell assembly is streamlined by arranging components in modular, stackable plates that reduce the robot’s calibration points from 336 to eight, dramatically simplifying re‑calibration within the confined glovebox. The robot sequentially picks up the can, separator, anode, cathode, spring, spacer, and cap, dispenses 70 µL of electrolyte, and crimps the cell. A dedicated plate‑stacker mechanism lifts component plates to a robot‑accessible height while minimizing footprint.

After assembly, cells are transferred to two gantry‑based testing stations equipped with 48 potentiostat channels and two EIS channels. Each cell undergoes two formation cycles (0.1 C, 3.0–4.3 V) followed by 50 main cycles (1 C, 3.0–4.2 V). At each C‑rate (0.5, 1, 2, 3 C) the system pauses for 30 minutes, then records an EIS spectrum from 200 kHz to 0.1 Hz. All operations are orchestrated by a programmable logic controller, allowing completely hands‑free execution.

Performance validation shows a 97.7 % assembly success rate (85/87 cells) and a cycle‑to‑test time of roughly four minutes per cell, enabling processing of the full 48‑cell batch in about 200 minutes. Over a six‑day cycling period, the platform can generate data for up to 240 cells per month. Reproducibility tests comparing 40 automatically assembled cells with 40 manually assembled cells (same electrolyte: 1 M LiPF₆ in EC:EMC 3:7 % + 2 wt % VC) reveal a discharge capacity relative standard deviation (RSD) of less than 1.2 % and an EIS resistance standard deviation below 3 Ω, confirming high consistency.

The authors argue that ALBATROSS removes the major bottlenecks in high‑throughput electrolyte screening: manual cell assembly, limited cycling capacity, and scarce impedance data. By delivering high‑quality, large‑scale datasets, the system can feed machine‑learning pipelines for electrolyte design, accelerate formation‑protocol optimization, and support fast‑charge research. Its modular, 3D‑printed components and open architecture also allow future extensions to other battery chemistries, SEI engineering studies, and broader autonomous laboratory workflows.