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Energies 2018, 11(1), 125; https://doi.org/10.3390/en11010125

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

Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA
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Received: 15 December 2017 / Revised: 30 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
(This article belongs to the Section Energy Storage and Application)
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Abstract

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. View Full-Text
Keywords: 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 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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kim, T.; Makwana, D.; Adhikaree, A.; Vagdoda, J.S.; Lee, Y. Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems. Energies 2018, 11, 125.

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