Green Manufacturing and Direct Recycling of Lithium-Ion Batteries

According to the International Energy Agency, the global Electric Vehicle (EV) sales are experiencing approximately 24% annual growth and the total market could reach 4 million in 2020 and 21.5 million by 2030. However, the mass production of lithium-ion batteries (LIBs) to power EV creates concerns over environmental impacts and the long-term sustainability of critical elements for producing the major battery components. Although much investment has been made, it is still imperative to develop an effective LIB production and recycling process.
This dissertation demonstrates a green and sustainable paradigm for LIBs where the batteries are manufactured and direct recycled to form a closed loop. The water-based cathode electrode delivers comparable cycle life and rate performance to the ones from the conventional organic solvent-based process. The direct recycling process has the advantages to regenerate the cathode material from electrode instead of decomposing into elements. Utilization of a water-soluble binder enables separating the cathode compound from spent electrodes using water, which is then successfully regenerated to deliver comparable electrochemical performance to the pristine one. When scaled up, the degraded cathode material can be directly regenerated by an optimized relithiation thermal synthesis (RTS) method to resynthesize the homogeneous cathode powder of high quality. The key factors and sintering procedures are studied to ensure the performance of the product. The pilot scale test successfully scales up to Kg-level with recycled output materials delivering good electrochemical performance. To automate the direct recycling process and improve the efficiency, machine learning and sensors are utilized in a novel battery disassembly platform. It can classify different batteries based on their types and sizes. The processing temperature is instantly monitored using thermal imager, and the prediction model is trained to give the prediction for measures taken by a closed loop control system. Furthermore, the image recognition is employed for quality control after the cutting process and the defect can be mitigated to ensure effective dismantling of End-of-life (EOL) batteries. The integration of machine learning techniques makes the elaborate dismantling process safer and more efficient.