Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries

doi:10.1007/s41918-026-00284-1

Electrochemical Energy Reviews ›› 2026, Vol. 9 ›› Issue (2): 9-.doi: 10.1007/s41918-026-00284-1

Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries

Weizhuo Li¹, Zhiming Bao^2,3, Dingjian Wang¹, Yang Wang⁴, Yinsheng Yu¹, Hang Li¹, Qing Du^2,3, Zunlong Jin¹, Kui Jiao^2,3

1. School of Mechanical and Power Engineering, Zhengzhou University, 100 Science Rd, Zhengzhou, 45001, Henan, China;
2. State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin, 300350, China;
3. National Industry-Education Platform for Energy Storage, Tianjin University, 135 Yaguan Rd, Tianjin, 300350, China;
4. Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing, 400044, China

收稿日期:2025-07-21 修回日期:2025-08-29 接受日期:2026-02-07 出版日期:2026-06-20 发布日期:2026-04-04
通讯作者: Qing Du Email:E-mail:duqing@tju.edu.cn;Zunlong Jin Email:E-mail:zljin@zzu.edu.cn;Kui Jiao Email:E-mail:kjiao@tju.edu.cn E-mail:duqing@tju.edu.cn;zljin@zzu.edu.cn;kjiao@tju.edu.cn
基金资助:
This research is supported by the National Natural Science Foundation of China (Grant No. 52306118, 52506109), the Postdoctoral Fellowship Program of CPSF (Grant No. GZC20250402), and the China Postdoctoral Science Foundation (Grant No. 2025M780596).

Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries

Weizhuo Li¹, Zhiming Bao^2,3, Dingjian Wang¹, Yang Wang⁴, Yinsheng Yu¹, Hang Li¹, Qing Du^2,3, Zunlong Jin¹, Kui Jiao^2,3

1. School of Mechanical and Power Engineering, Zhengzhou University, 100 Science Rd, Zhengzhou, 45001, Henan, China;
2. State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin, 300350, China;
3. National Industry-Education Platform for Energy Storage, Tianjin University, 135 Yaguan Rd, Tianjin, 300350, China;
4. Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing, 400044, China

Received:2025-07-21 Revised:2025-08-29 Accepted:2026-02-07 Online:2026-06-20 Published:2026-04-04
Contact: Qing Du Email:E-mail:duqing@tju.edu.cn;Zunlong Jin Email:E-mail:zljin@zzu.edu.cn;Kui Jiao Email:E-mail:kjiao@tju.edu.cn E-mail:duqing@tju.edu.cn;zljin@zzu.edu.cn;kjiao@tju.edu.cn
Supported by:
This research is supported by the National Natural Science Foundation of China (Grant No. 52306118, 52506109), the Postdoctoral Fellowship Program of CPSF (Grant No. GZC20250402), and the China Postdoctoral Science Foundation (Grant No. 2025M780596).

摘要/Abstract

摘要： Lithium-ion batteries (LIBs) have changed our world and underpinned a wide spectrum of technologies, from consumer electronics and electric vehicles to grid-scale energy storage, low-altitude aircraft, and aerospace systems. As demands for power density, reliability, and safety continue to increase across diverse scenarios, the traditional trial-and-error research and development (R&D) paradigm is no longer suitable for today’s fast-paced innovation environment. Digital modeling, which excels in probing fundamental mechanisms, optimizing battery design, and enhancing management strategies, has become a powerful enabler for accelerating innovation and iterative development in battery technology. This paper presents a comprehensive review on the multi-scenario modeling and simulation of LIBs. We begin with an overview of equivalent-circuit modeling (Sect. 2) and electrochemical modeling (Sect. 3) for performance prediction, followed by thermal modeling and electrical-thermal coupling frameworks (Sect. 4) to improve model accuracy. Next, we summarize battery degradation and failure mechanisms, including battery aging (Sect. 5) and thermal runaway modeling (Sect. 6). We then explore mesoscale phase field (PF) modeling for dendrite growth, phase separation, and crack propagation (Sect. 7), followed by molecular dynamics (MD) simulations for probing electrode/electrolyte structures, ion transport, and interface reaction mechanisms (Sect. 8). Finally, we offer insights into current challenges and outline future directions. The deep integration of multiscale modeling, artificial intelligence (AI) and cloud-edge-end frameworks is poised to drive the next generation of intelligent, robust, and adaptive battery modeling platforms, accelerating the development of next-generation battery technologies.

关键词: Lithium-ion battery, Multi-scenario modeling, System macroscale, Mesoscale, Microscale

Abstract: Lithium-ion batteries (LIBs) have changed our world and underpinned a wide spectrum of technologies, from consumer electronics and electric vehicles to grid-scale energy storage, low-altitude aircraft, and aerospace systems. As demands for power density, reliability, and safety continue to increase across diverse scenarios, the traditional trial-and-error research and development (R&D) paradigm is no longer suitable for today’s fast-paced innovation environment. Digital modeling, which excels in probing fundamental mechanisms, optimizing battery design, and enhancing management strategies, has become a powerful enabler for accelerating innovation and iterative development in battery technology. This paper presents a comprehensive review on the multi-scenario modeling and simulation of LIBs. We begin with an overview of equivalent-circuit modeling (Sect. 2) and electrochemical modeling (Sect. 3) for performance prediction, followed by thermal modeling and electrical-thermal coupling frameworks (Sect. 4) to improve model accuracy. Next, we summarize battery degradation and failure mechanisms, including battery aging (Sect. 5) and thermal runaway modeling (Sect. 6). We then explore mesoscale phase field (PF) modeling for dendrite growth, phase separation, and crack propagation (Sect. 7), followed by molecular dynamics (MD) simulations for probing electrode/electrolyte structures, ion transport, and interface reaction mechanisms (Sect. 8). Finally, we offer insights into current challenges and outline future directions. The deep integration of multiscale modeling, artificial intelligence (AI) and cloud-edge-end frameworks is poised to drive the next generation of intelligent, robust, and adaptive battery modeling platforms, accelerating the development of next-generation battery technologies.

Key words: Lithium-ion battery, Multi-scenario modeling, System macroscale, Mesoscale, Microscale

Weizhuo Li, Zhiming Bao, Dingjian Wang, Yang Wang, Yinsheng Yu, Hang Li, Qing Du, Zunlong Jin, Kui Jiao. Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries[J]. Electrochemical Energy Reviews, 2026, 9(2): 9-.

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Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries

Multi-Scenario Digital Modeling and Simulation of Lithium-Ion Batteries

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