电池(电)
可再生能源
储能
可靠性(半导体)
计算机科学
可靠性工程
高效能源利用
能源管理
能量(信号处理)
功率(物理)
汽车工程
系统工程
电气工程
工程类
统计
物理
数学
量子力学
作者
George Suciu,Andreea Badicu,Cristian Beceanu,M.Serdar Yumlu,Yusuf Kaya,Kadir Gurkan Kizak,Fatih Tahtasakal
标识
DOI:10.1109/atee52255.2021.9425328
摘要
In recent years, energy storage systems have rapidly transformed and evolved because of the pressing need to create more resilient energy infrastructures and to keep energy costs at low rates for consumers, as well as for utilities. Among the wide array of technological approaches to managing power supply, Li-Ion battery applications are widely used to increase power capabilities and to better integrate renewable energy sources. The improvement of Li-Ion batteries' reliability and safety requires BMS (battery management system) technology for the energy systems' optimal functionality and more sustainable batteries with ultra-high performances. This paper aims to introduce the need to incorporate information technology within the current energy storage applications for better performance and reduced costs. Artificial intelligence based BMSs facilitate parameter predictions and state estimations, thus improving efficiency and lowering overall maintenance costs.
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