Lithium-ion batteries (LIBs) have an established role in the consumer electronics markets with minimum risk of replacement from any other contender in the near future. The recent momentum towards electric vehicles and the renewable energy storage market is creating an increased demand for LIBs. The large amount of hazardous waste generated from the disposal of LIBs is driving research into a sustainable approach for LIB treatment and recovery. The positive electrode active materials being the main targeted component as it is the greatest cost contributor to LIBs production.
Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications.
An ability to separate battery electrode materials while preserving functional integrity is essential to close the loop of material use in lithium-ion batteries. However, a low-energy and low-cost separation system that selectively recovers electrode materials has not yet been established. In this study, froth flotation experiments were carried out with a variety of new and spent lithium-ion batteries using kerosene as the collector. The products were characterized using thermogravimetric and chemical analysis.
