Implementation for a cloud battery management system based on the CHAIN framework
Autor: | Hanchao Cheng, Shichun Yang, Mingyue Wang, Billy Wu, Zhengjie Zhang, Heping Ling, Yubo Lian, Yonglin Li, Lisheng Zhang, Binbin Chen, Rui Cao, Xinhua Liu, Yinan Jiang |
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Přispěvatelé: | The Faraday Institution |
Rok vydání: | 2021 |
Předmět: |
Battery (electricity)
Adaptive control Computer science media_common.quotation_subject Big data Automotive industry Battery Cloud computing QA76.75-76.765 Artificial Intelligence Control theory Computer software Function (engineering) Engineering (miscellaneous) media_common end-edge-cloud architecture Battery management system business.industry TK1-9971 General Energy Upgrade SOX estimation Systems engineering CHAIN Electrical engineering. Electronics. Nuclear engineering business Cloud |
Zdroj: | Energy and AI, Vol 5, Iss, Pp 100088-(2021) |
ISSN: | 2666-5468 |
Popis: | Summary An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management. |
Databáze: | OpenAIRE |
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