Automated Powder Coating Platform for Long-Life Lithium-Ion Batteries

In recent years, lithium-ion cells have become the leading rechargeable battery technology for portable devices, electric vehicles, and home battery backup for renewables, as well as a promising technology for grid-scale battery backup. As demand for lithium-ion batteries grows, so does the demand for lithium and cobalt, two metals essential for the construction of modern Li-NMC cathodes. As the extraction of these metals has significant environmental and ethical concerns, minimising the demand for new lithium and cobalt is crucial. One way of achieving this is to extend the life cycle of lithium-ion batteries, thus reducing the demand for new batteries. Although lithium-ion batteries are already considered relatively long in life (up to thousands of cycles), they still undergo degradation during charge-discharge cycles caused by particle cracking, unintended reactions with HF, and dissolution of metal ions in the electrolyte, causing a reduction in redox sites. It has been shown that surface coating cathode particles with certain oxides can reduce these degradation effects while maintaining battery functionality, however the process of discovering specific formulations and methods has so far been mostly based on trial and error due to the large number of variables and different approaches that can be taken to create the coatings. Furthermore, most experimental methods are highly manual and therefore low throughput, thus not many samples can be created and tested, leading to slow progress in discovering specific methodologies. The recent advent of automated and self-driving laboratories has opened opportunities for automated high-throughput chemical experiments. Building on Interfaces Genome Materials automation work developed as part of the EU Battery2030 project: Battery Acceleration Platform (BIG-MAP) Collaboration,1,2 this project aims to further automate the platform for powder coating of LiNi0.6Mn0.2Co0.2O2 (NMC622) particles (used as active material within the positive electrode of Li-ion cells), allowing high-throughput experimentation toward the discovery of optimal formulations for long-life batteries. A robotic platform will be developed; using a robot arm, samples will be transported between lab equipment to autonomously execute the sol-gel process, creating Li-NMC cathode particles coated in a nanocoating of an oxide material. In a later stage, the platform may be expanded to allow automated cycling of batteries using these coated cathodes, thus enabling closed loop experimentation and self-optimisation by employing machine learning methods.