Research on the Adaptive Data Modeling System and Key Technologies for the Future Factory of Lead-acid Batteries

doi:10.3901/JME.260098

Abstract

Abstract: As the requirements for battery custom design, intelligent manufacturing, and green recycling continue to increase in the manufacturing industry, how to adapt to customer personalized needs, multi-disciplinary collaborative design, intelligent and flexible production, and green recycling requirements in the production process has become a key issue to be solved in the transformation and upgrading of lead-acid battery factories. Therefore, the role of data and models in the transformation and upgrading of factories is analyzed, and a future factory adaptability data model system for lead-acid batteries is proposed. Four key technologies involved in the system are expounded, including customized design to meet the individual needs of customers, virtual and real interaction to adapt to multi-disciplinary collaborative design, control and traceability to adapt to the whole process of intelligent production, and process upgrade to adapt to green recycling. Taking the whole process production information traceability of lead-acid batteries as an example, the application of computer vision and other technologies in production control and traceability is analyzed to prove the effectiveness of the proposed method. The future factory development of new energy enterprises represented by lead-acid batteries is prospected, providing a reference for the transformation and upgrading of the domestic new energy industry.

Key words: intelligent manufacturing, future factory, new energy, adaptability, data model system

CLC Number:

TP278
F425

CHENG Dinghao, HU Bingtao, FENG Yixiong, YANG Chen, WANG Fei, YUAN Fubang, WANG Yong, TAN Jianrong. Research on the Adaptive Data Modeling System and Key Technologies for the Future Factory of Lead-acid Batteries[J]. Journal of Mechanical Engineering, 2026, 62(3): 458-478.

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