State of health estimation with federated learning for battery-based energy storage systems

We have aspired for a green and intelligent future, where humans, the built environment, and nature are interconnected as a cyber-physical system. To such an Internet of Things, the sustainability and robustness of the power system are crucial, and the reliable operation of the battery-based energy storage systems is essential because of their abilities in power smoothing and shifting. However, estimating the state of health (SOH) for batteries is challenging due to privacy concerns. In this project, we aim to design a SOH estimation scheme that enables multiple energy storage systems to build a common, robust estimation model without sharing measurement data, thus addressing critical issues such as data privacy and data migration cost. Increasing the research into the SOH estimation of batteries will directly guide KORE Power to replace the battery in time and reduce economic losses. This research will support KORE Power’s position as a leader in the technology industry in North America and Canada’s position as one of the pioneer countries in clean energy industries.

Faculty Supervisor:

Yuanzhu Chen

Student:

Partner:

KORE Power

Discipline:

Computer science

Sector:

Manufacturing

University:

Queen's University

Program: