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