Abstract: Abstract: In order to quickly detect the failed batteries in the storage area of lithium-ion batteries, the failure modes and fire characterization factors of lithium-ion batteries in the storage area are analyzed. Failure detection test of lithium cobalt batteries, ternary batteries and ternary battery packs were carried out by using the technology of artistic intelligence (AI) based on image recognition and big data. The results show that the stacking failure of lithium-ion batteries can be divided into six stages: the sharp rise of lithium-ion battery temperature, the ripple of milky white gas along the ground plane, the rise of black smoke, the initiation of ignition, flame expansion and burnout. The fire characterization factors of lithium-ion battery stacking failure are white fog, smoke, temperature and flame. At the same time, an AI detection system based on image recognition and big data analysis suitable for lithium-ion battery storage area fire is developed, and the system can realize early warning within 1 min of white fog, and the response speed is 5 ~ 10 min faster than that of ceiling smoke fire detector.

Key words: Key words: lithium-ion battery, storage area, fire, detection, image recognition, big data