| Issue |
Natl Sci Open
Volume 4, Number 6, 2025
Special Topic: Artificial Intelligence and Energy Revolution
|
|
|---|---|---|
| Article Number | 20250054 | |
| Number of page(s) | 32 | |
| Section | Engineering | |
| DOI | https://doi.org/10.1360/nso/20250054 | |
| Published online | 05 November 2025 | |
REVIEW
Revolutionizing batteries based on digital twin through AI-simulation synergy for design, manufacturing, operation, and recycling
1
Sunwoda Mobility Energy Technology Co., Ltd., Shenzhen 518107, China
2
Battery Division, Chery Automobile Co., Ltd., Wuhu 241000, China
3
Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA
4
Semitronix Corporation, Hangzhou 311121, China
5
Department of Chemistry and Materials, State University of New York at Binghamton, Binghamton, NY 13902-6000, USA
6
Advanced Power, Belmont, NC 28012, USA
* Corresponding authors (emails: xywang008@gmail.com (Xiaoya Wang); junxu@udel.edu (Jun Xu); Peter.zheng@semitronix.com (Yongjun Zheng); stanwhit@gmail.com (M. Stanley Whittingham); liyangxingnc@163.com (Yangxing Li))
Received:
16
September
2025
Revised:
29
October
2025
Accepted:
5
November
2025
Optimizing lithium-ion batteries (LIBs) is pivotal for advancing sustainable energy solutions, particularly for portable electronics, electric vehicles and renewable energy storage systems. This review explores state-of-the-art strategies that integrate advanced digital simulations with comprehensive lifecycle management, assisted by artificial intelligence (AI), to overcome critical challenges in battery performance, safety, and durability. Our approach combines computational materials science with multi-scale modeling, bridging atomic-scale phenomena to system-level dynamics. This synergy provides new insights into materials behavior and electrochemical processes. Physics-based simulation techniques and AI-driven optimization technologies underpin these methods, enabling them to achieve accurate predictions and drive the design of next-generation batteries. Furthermore, the integration of cloud-based battery management systems (BMS) with edge computing facilitates real-time monitoring, predictive diagnostics, and proactive control, while the adoption of the “Battery Passport” concept enhances lifecycle traceability, promoting recycling and reuse. Collectively, these strategies establish a robust framework centered on standardization, modularization, and digitization, driving innovation across design, manufacturing, maintenance and recycling processes. This industry-academia-research collaborative battery large model has not only accelerated the industrialization of next-generation battery technologies but also provided strong support for the sustainable development of the sector. This review underscores the transformative potential of these integrated approaches, laying the groundwork for future breakthroughs in energy technologies and advancing global sustainability goals.
Key words: artificial intelligence / digital twin / battery large model / multi-scale simulation / battery design, manufacturing, operation and recycling
© The Author(s) 2025. Published by Science Press and EDP Sciences.
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