AI-driven battery large model for battery industry-academia-research collaboration. The framework positions an AI-driven large model as a unified intelligence hub, connecting materials, electrochemical systems, battery design, manufacturing/quality control, and operation across the battery value chain. By integrating multi-physics and multi-scale simulations with materials genome engineering, it accelerates materials discovery and enables efficient property prediction. Electrochemical system engineering and digital twin manufacturing link virtual design with real-world production. An end-edge-cloud collaborative BMS provides real-time feedback and adaptive optimization, allowing battery working for the people and continuous learning from operational data. Lifecycle management closes the loop between laboratory innovation and field deployment, fostering an intelligent and sustainable battery ecosystem.