Federated Battery Diagnosis and Prognosis
Nur Banu Altinpulluk Deniz Altinpulluk Paritosh Ramanan Noah Paulson Feng Qiu Susan Babinec Murat Yildirim
Battery diagnosis, prognosis and health management models play a critical
role in the integration of battery systems in energy and mobility fields.
However, large-scale deployment of these models is hindered by a myriad of
challenges centered around data ownership, privacy, communication, and
processing. State-of-the-art battery diagnosis and prognosis methods require
centralized collection of data, which further aggravates these challenges. Here
we propose a federated battery prognosis model, which distributes the
processing of battery standard current-voltage-time-usage data in a
privacy-preserving manner. Instead of exchanging raw standard
current-voltage-time-usage data, our model communicates only the model
parameters, thus reducing communication load and preserving data
confidentiality. The proposed model offers a paradigm shift in battery health
management through privacy-preserving distributed methods for battery data
processing and remaining lifetime prediction.