Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment


This study proposes a method for developing equipment lifespan estimators that combine physical information and numerical data, both of which may be incomplete. Physical information may not have a uniform fit to all experimental data, and health information may only be available at the initial and final periods. To address these issues, a procedure is defined to adjust the model to different subsets of available data, constrained by feasible trajectories in the health status space. Additionally, a new health model for rechargeable lithium batteries is proposed, and a use case is presented to demonstrate its efficacy. The optimistic (max–max) strategy is found to be the most suitable for diagnosing battery lifetime, based on the results.

In Array
Nahuel Costa
Nahuel Costa
Machine Learning & AI researcher