面向铅酸蓄电池的未来工厂适应性数据模型体系及关键技术研究

doi:10.3901/JME.260098

摘要/Abstract

摘要： 随着制造业对电池定制设计、智能制造和绿色循环的要求不断提高，如何在生产过程中适应客户个性化需求、多学科协同设计、智能化灵活生产和绿色化循环回收的要求，成为铅酸蓄电池工厂转型升级需要解决的关键问题。因此，分析了数据与模型在工厂转型升级中的作用，提出了一种面向铅酸蓄电池的未来工厂适应性数据模型体系，阐述了该体系涉及的四类关键技术，包括适应客户个性化需求的定制设计、适应多学科协同设计的虚实交互、适应全流程智能生产的管控追溯和适应绿色化循环回收的工艺升级。以铅酸蓄电池的全流程生产信息追溯为例，分析了计算机视觉等技术在生产管控追溯中的应用，证明所提方法的有效性，并对以铅酸蓄电池为代表的新能源企业未来工厂发展进行展望，为国内新能源行业的转型升级提供了参考。

关键词: 智能制造, 未来工厂, 新能源, 适应性, 数据模型体系

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

中图分类号:

TP278
F425

程鼎豪, 胡炳涛, 冯毅雄, 杨晨, 王飞, 袁富邦, 汪勇, 谭建荣. 面向铅酸蓄电池的未来工厂适应性数据模型体系及关键技术研究[J]. 机械工程学报, 2026, 62(3): 458-478.

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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