Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems

被引:88
作者
Kim, Taesic [1 ]
Makwana, Darshan [1 ]
Adhikaree, Amit [1 ]
Vagdoda, Jitendra Shamjibhai [1 ]
Lee, Young [1 ]
机构
[1] Texas A&M Univ Kingsville, Dept Elect Engn & Comp Sci, MSC 192,700 Univ Blvd, Kingsville, TX 78363 USA
关键词
battery management system (BMS); cloud computing; condition monitoring; fault diagnosis; Internet of Things (IoT); large-scale lithium-ion battery energy storage systems; lithium-ion battery; MANAGEMENT-SYSTEMS; STATE; PARAMETER; HEALTH; SAFETY; MODEL; PACKS;
D O I
10.3390/en11010125
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.
引用
收藏
页数:15
相关论文
共 49 条
[1]   Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications [J].
Al-Fuqaha, Ala ;
Guizani, Mohsen ;
Mohammadi, Mehdi ;
Aledhari, Mohammed ;
Ayyash, Moussa .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2015, 17 (04) :2347-2376
[2]   Distance-based detection and prediction of outliers [J].
Angiulli, F ;
Basta, S ;
Pizzuti, C .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2006, 18 (02) :145-160
[3]  
[Anonymous], 2010, Battery Management Systems for Large Lithium Ion Battery Packs
[4]   The Balance of Renewable Sources and User Demands in Grids: Power Electronics for Modular Battery Energy Storage Systems [J].
Bragard, Michael ;
Soltau, Nils ;
Thomas, Stephan ;
De Doncker, Rik W. .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2010, 25 (12) :3049-3056
[5]  
Dey S., 2015, DYN SYST CONTR C AM
[6]   Model-based real-time thermal fault diagnosis of Lithium-ion batteries [J].
Dey, Satadru ;
Biron, Zoleikha Abdollahi ;
Tatipamula, Sagar ;
Das, Nabarun ;
Mohon, Sara ;
Ayalew, Beshah ;
Pisu, Pierluigi .
CONTROL ENGINEERING PRACTICE, 2016, 56 :37-48
[7]  
Dinger A., 2010, ENERGY ENV PUBLICATI, V87, P18
[8]  
Furtht B., 2010, HDB CLOUD COMPUTING
[9]   High-Capacitance Hybrid Supercapacitor Based on Multi-Colored Fluorescent Carbon-Dots [J].
Genc, Rukan ;
Alas, Melis Ozge ;
Harputlu, Ersan ;
Repp, Sergej ;
Kremer, Nora ;
Castellano, Mike ;
Colak, Suleyman Gokhan ;
Ocakoglu, Kasim ;
Erdem, Emre .
SCIENTIFIC REPORTS, 2017, 7
[10]  
Han J, 2012, MOR KAUF D, P1