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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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Energies 2018, 11(1), 125; https://doi.org/10.3390/en11010125
Received: 15 December 2017 / Revised: 30 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
(This article belongs to the Section D: Energy Storage and Application)
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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MDPI and ACS Style

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. https://doi.org/10.3390/en11010125

AMA Style

Kim T, Makwana D, Adhikaree A, Vagdoda JS, Lee Y. Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems. Energies. 2018; 11(1):125. https://doi.org/10.3390/en11010125

Chicago/Turabian Style

Kim, Taesic, Darshan Makwana, Amit Adhikaree, Jitendra S. Vagdoda, and Young Lee. 2018. "Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems" Energies 11, no. 1: 125. https://doi.org/10.3390/en11010125

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