Developed a data-driven prognostic model using the Long short-term memory (LSTM) algorithm to predict the state of charge (SoC) and state of health (SoH) of the lithium-ion battery where the dataset was taken from the NASA Repository. The proposed LSTM algorithm was compared against other deep learning algorithms based on RMSE value.

The NASA Prognostics As-A-Service (PaaS) Sandbox is a simplified implementation of a Software Oriented Architecture (SOA) for performing prognostics (estimation of time until events and future system states) of engineering systems. The PaaS Sandbox is a wrapper around the Prognostics Algorithms Package and Prognostics Models Package, allowing one or more users to access the features of these packages through a REST API. The package is intended to be used as a research tool to prototype and benchmark Prognostics As-A-Service (PaaS) architectures and work on the challenges facing such architectures, including Generality, Communication, Security, Environmental Complexity, Utility, and Trust.