Enhanced state of health prediction of battery using machine learning and digital twin

Abstract

In the digital age, use of batteries has increased manifold and thus, our dependence on them. It becomes critical that we are able to accurately predict the remaining charge and the state of health of the battery for safe and efficient operation. We can achieve this using Machine Learning models and technologies such as Digital Twin.