Scholarlink

The intelligent battery swap recommendation (BSRec) system for e-bikes is a critical need in the development of smart urban transportation. However, challenges such as limited observable data and the complexity of cabinet-battery constraints have hindered model generalization across multiple tasks. In this paper, we propose eBASE, the first model for e-bike BSRec, which integrates a hybrid expert system with dual-tower joint optimization. Experimental results show that eBASE outperforms existing models in three real-world tasks: cabinet, battery, and cabinet-battery recommendations. Additionally, the BSRec system developed with eBASE has been deployed in over 10 cities, significantly improving the multi-dimensional satisfaction of millions of riders.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

eBASE: Real-Time Battery Swap Recommendation System for eBike Users

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Siwei Zhou,
  • Yongchun Gu,
  • Zhao Li,
  • Yangzhen Li,
  • Chengxiang Zhu,
  • Xuanwu Liu,
  • Jiaming Huang,
  • Ming Li,
  • Xuyun Zhang,
  • Minglu Li

摘要

The intelligent battery swap recommendation (BSRec) system for e-bikes is a critical need in the development of smart urban transportation. However, challenges such as limited observable data and the complexity of cabinet-battery constraints have hindered model generalization across multiple tasks. In this paper, we propose eBASE, the first model for e-bike BSRec, which integrates a hybrid expert system with dual-tower joint optimization. Experimental results show that eBASE outperforms existing models in three real-world tasks: cabinet, battery, and cabinet-battery recommendations. Additionally, the BSRec system developed with eBASE has been deployed in over 10 cities, significantly improving the multi-dimensional satisfaction of millions of riders.