Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

被引：0

作者：

Altinpulluk, Nur Banu ^{[1
]}

Altinpulluk, Deniz ^{[1
]}

Ramanan, Paritosh ^{[2
]}

Paulson, Noah H. ^{[3
]}

Qiu, Feng ^{[4
]}

Babinec, Susan J. ^{[3
]}

Yildirim, Murat ^{[1
]}

机构：

[1] Wayne State Univ, Ind & Syst Engn, Detroit, MI 48202 USA

[2] Oklahoma State Univ, Ind Engn & Management, Stillwater, OK USA

[3] Argonne Natl Lab, Argonne Collaborat Ctr Energy Storage Sci ACCESS, Lemont, IL USA

[4] Argonne Natl Lab, Energy Syst Dept, Lemont, IL USA

来源：

CELL REPORTS PHYSICAL SCIENCE | 2024年 / 5卷 / 10期

基金：

美国国家科学基金会;

关键词：

▪▪▪;

D O I：

10.1016/j.xcrp.2024.102215

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage- time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

引用

页数：19