Research on fault prediction and diagnosis methods of energy storage system based on big data and artificial intelligence

doi:10.19799/j.cnki.2095-4239.2024.0902

Energy Storage Science and Technology ›› 2024, Vol. 13 ›› Issue (10): 3653-3655.doi: 10.19799/j.cnki.2095-4239.2024.0902

• Energy Storage Test: Methods and Evaluation • Previous Articles Next Articles

Research on fault prediction and diagnosis methods of energy storage system based on big data and artificial intelligence

Gen LI¹(), Shanshan LIU²()

^1.College of Artificial Intelligence and Big Data, Guangdong Vocational and Technical University of Business and Technology, Zhaoqing 526000, Guangdong, China
^2.College of Digital Technology, Guangdong Finance & Trade Vocational College, Qingyuan 511500, Guangdong, China

Received:2024-07-15 Revised:2024-08-15 Online:2024-10-28 Published:2024-10-30
Contact: Shanshan LIU E-mail:lg_gdgs@126.com;shan523272231@163.com

Abstract

Abstract:

With the rapid development of energy storage grid technology and new energy electric vehicle technology, the global demand for energy storage systems is increasing. However, the complexity of the application environment and the large-scale battery composition increase the probability of failure of the energy storage system. This paper describes the research on big data technology and artificial intelligence technology in energy storage system fault prediction and diagnosis from two perspectives. Big data technology can analyze a large amount of energy data, thereby improving the production and utilization efficiency of energy storage systems and reducing energy waste and loss. Artificial intelligence technology can mine the valuable information hidden behind big data, train energy data, and predict and diagnose energy storage systems. The integration of big data technology and artificial intelligence technology can process and analyze a large amount of energy data, thereby improving the efficiency of energy storage systems, predicting and diagnosing whether energy storage systems have failed, and promoting the monitoring and management of energy storage systems.

Key words: energy storage system, fault prediction and diagnosis, big data technology, artificial intelligence

CLC Number:

TM 912

Gen LI, Shanshan LIU. Research on fault prediction and diagnosis methods of energy storage system based on big data and artificial intelligence[J]. Energy Storage Science and Technology, 2024, 13(10): 3653-3655.

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Research on fault prediction and diagnosis methods of energy storage system based on big data and artificial intelligence

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