This paper reviews multiscale modeling techniques and their applications in battery health analysis, including atomic-scale computational chemistry, particle-scale reaction simulations, electrode-scale structural models, macroscale electrochemical models, and data-driven models at the system level. Multiscale modeling provides profound insights into material behavior and battery aging processes, offering valuable references for battery health estimation and management strategies. To extend battery lifespan, the paper considers the use of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end-cloud collaborative battery management systems, and the design of a multiscale simulation integration platform. A management framework aimed at extending battery life is proposed, offering a promising roadmap for addressing health analysis challenges in lithium-ion batteries (LIBs), ultimately leading to more reliable, efficient, and durable solutions for next-generation batteries. The paper discusses multiscale modeling from atomic to cell scales, focusing on the contributions of multiscale simulations to battery life prediction. At the atomic scale, first-principles-based calculations are used to analyze lithium embedding and de-embedding, diffusion energy barriers, structural stability, and reaction kinetics. These calculations provide theoretical insights into materials for high-stability, long-cycle-life LIBs and help understand battery degradation mechanisms. Theoretical voltage and electrochemical window are discussed, with DFT calculations used to determine equilibrium voltages and predict battery performance. The voltage profile from DFT calculations aligns well with experimental measurements and phase field simulations. At the particle scale, lithium diffusion and mechanical stability are analyzed. The paper discusses the effects of particle size on mechanical properties and the importance of mechanical stability in battery performance. Interface chemistry is also discussed, focusing on lithium deposition, SEI formation, and the impact of SEI on battery performance. The paper highlights the role of multiscale modeling in understanding and predicting battery degradation processes, and proposes a framework for battery health management that integrates multiscale models, artificial intelligence techniques, and edge-cloud collaboration methods.This paper reviews multiscale modeling techniques and their applications in battery health analysis, including atomic-scale computational chemistry, particle-scale reaction simulations, electrode-scale structural models, macroscale electrochemical models, and data-driven models at the system level. Multiscale modeling provides profound insights into material behavior and battery aging processes, offering valuable references for battery health estimation and management strategies. To extend battery lifespan, the paper considers the use of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end-cloud collaborative battery management systems, and the design of a multiscale simulation integration platform. A management framework aimed at extending battery life is proposed, offering a promising roadmap for addressing health analysis challenges in lithium-ion batteries (LIBs), ultimately leading to more reliable, efficient, and durable solutions for next-generation batteries. The paper discusses multiscale modeling from atomic to cell scales, focusing on the contributions of multiscale simulations to battery life prediction. At the atomic scale, first-principles-based calculations are used to analyze lithium embedding and de-embedding, diffusion energy barriers, structural stability, and reaction kinetics. These calculations provide theoretical insights into materials for high-stability, long-cycle-life LIBs and help understand battery degradation mechanisms. Theoretical voltage and electrochemical window are discussed, with DFT calculations used to determine equilibrium voltages and predict battery performance. The voltage profile from DFT calculations aligns well with experimental measurements and phase field simulations. At the particle scale, lithium diffusion and mechanical stability are analyzed. The paper discusses the effects of particle size on mechanical properties and the importance of mechanical stability in battery performance. Interface chemistry is also discussed, focusing on lithium deposition, SEI formation, and the impact of SEI on battery performance. The paper highlights the role of multiscale modeling in understanding and predicting battery degradation processes, and proposes a framework for battery health management that integrates multiscale models, artificial intelligence techniques, and edge-cloud collaboration methods.