Data-analytics for Li-ion battery health estimation

Abstract

Machine learning is gaining popularity in many applications around the world, and it is making an impact in the world. As we are in the 4th industrial revolution, machine learning is finding its way into many different industries. The use of lithium-ion batteries is rising as the demand of energy storage rises due to the adoption of renewable energy such as solar and wind power. In addition, the rise of electric vehicles also leads to the increase of lithium-ion battery usage. Lithium-ion batteries requires proper monitoring and replacement for the system to be working optimally. Therefore, this project explores the feasibility of using data-driven methods to estimate the lithium-ion state of health. This may help to ease maintenance for systems with lots of lithium-ion batteries, notifying them which is the potential battery or battery pack that requires replacement.