Digital twin and cloud-side-end collaboration for intelligent battery management system. (January 2022)

• Record Type: Journal Article
• Authors: Wang, Yujie; Xu, Ruilong; Zhou, Caijie; Kang, Xu; Chen, Zonghai
• Is Part Of: Journal of manufacturing systems. Volume 62(2022)
• Page Start: 124
• Page End: 134
• Publication Date: 2022-01
• Subjects: Digital twin -- Battery management system -- Cloud computing -- Internet of Things -- Distributed computing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.11.006
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: The architecture of network collaboration for future battery management is presented. The digital twin model is used for management of the batteries in their entire life cycle. Online learning and model updating are used to fix model parameters. The key technologies of state estimation and battery equalization are introduced. Abstract: Nowadays the wave of digital economy has swept the world, and the competition in the field of battery management has become increasingly vigorous. The application of digital twin technology gives a new concept of networked management and service of lithium-ion batteries. In this paper, the digital twin technology and cloud-side-end collaboration for the future battery management system is discussed. A four layer networked architecture of cloud-side-end collaboration for battery management system is presented which breaks through the computing capacity and storage space limitations of the conventional battery management and enables high performance algorithms. The digital twin model of the battery is established, which enables refined and safety management of the batteries in their entire life cycle. Furthermore, the digital twin model and key technologies such as state estimation and cloud assisted equalization of the batteries are introduced. The results indicate that digital twin models are helpful for battery management and the full life cycle data are useful to build the upgrade route of the battery.