Disruptive generational leap: an embedded AI battery management system for power and energy storage systems AITranslate
Abstract AITranslate
Battery management systems (BMSs) play an undeniably critical role in both power and energy storage systems. As applications continue to expand into various complex scenarios, BMSs have increasingly become the key determinant of the overall performance of advanced battery systems. Beyond conventional basic performance metrics, lifetime and safety gradually emerge as core concerns for battery systems. However, existing BMSs are increasingly inadequate in supporting these two aspects. The development of high-safety, long-lifetime BMSs has become a common focus in battery systems across various application scenarios. This paper reviews the current state of BMS technology, analyzes its shortcomings in both hardware and software, and summarizes the latest technological advancements across four key areas: multi-dimensional parameter measurement, multi-modal fusion modeling, active management, and embedded artificial intelligence (AI) deployment. It objectively evaluates the strengths and weaknesses of these technologies for future BMS applications. Finally, the paper innovatively proposes the fundamental concept of an embedded AI BMS, highlighting its potential for deployment in both power battery and energy storage battery applications. As the core conclusion of this paper, the embedded AI BMS plays a significant role in next-generation battery systems. The discussion of key technologies in this paper also provides comprehensive references and analytical insights for the development of the next-generation smart BMS.
KeyWords AITranslate
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Basic Information:
DOI:10.23919/CHAIN.2026.000013
Chinese Library Classification Number:
Citation Information:
Battery management systems (BMSs) play an undeniably critical role in both power and energy storage systems. As applications continue to expand into various complex scenarios, BMSs have increasingly become the key determinant of the overall performance of advanced battery systems. Beyond conventional basic performance metrics, lifetime and safety gradually emerge as core concerns for battery systems. However, existing BMSs are increasingly inadequate in supporting these two aspects. The development of high-safety, long-lifetime BMSs has become a common focus in battery systems across various application scenarios. This paper reviews the current state of BMS technology, analyzes its shortcomings in both hardware and software, and summarizes the latest technological advancements across four key areas: multi-dimensional parameter measurement, multi-modal fusion modeling, active management, and embedded artificial intelligence (AI) deployment. It objectively evaluates the strengths and weaknesses of these technologies for future BMS applications. Finally, the paper innovatively proposes the fundamental concept of an embedded AI BMS, highlighting its potential for deployment in both power battery and energy storage battery applications. As the core conclusion of this paper, the embedded AI BMS plays a significant role in next-generation battery systems. The discussion of key technologies in this paper also provides comprehensive references and analytical insights for the development of the next-generation smart BMS.
quote
| GB/T 7714-2015 | [1] Xueyuan Wang, Yuguang Li, Zihan Jia, et al. Disruptive generational leap: an embedded AI battery management system for power and energy storage systems[J]. Chain, 2026, 3(3): 271-312. DOI:10.23919/CHAIN.2026.000013. |
| MLA | [1] Xueyuan Wang, et al., "Disruptive generational leap: an embedded AI battery management system for power and energy storage systems." Chain, vol. 3, no. 3, 2026, pp. 271-312, https://doi.org/10.23919/CHAIN.2026.000013. |
| APA | [1] Xueyuan Wang, Yuguang Li, Zihan Jia, Cenyu Wang, Yaqi Wang, Bo Jiang, Jiangong Zhu, Xuezhe Wei, & Haifeng Dai. (2026). Disruptive generational leap: an embedded AI battery management system for power and energy storage systems. Chain, 3(3), 271-312. https://doi.org/10.23919/CHAIN.2026.000013 |
| IEEE | [1] Xueyuan Wang, Yuguang Li, Zihan Jia, Cenyu Wang, Yaqi Wang, Bo Jiang, Jiangong Zhu, Xuezhe Wei, and Haifeng Dai, "Disruptive generational leap: an embedded AI battery management system for power and energy storage systems," Chain, vol. 3, no. 3, pp. 271-312, 2026, doi: 10.23919/CHAIN.2026.000013. keywords: {active management;embedded AI BMS;multi-modal fusion modeling;multi-dimensional measurement;smart battery} |
