Summary of Transformer-based Graph Neural Networks For Battery Range Prediction in Aiot Battery-swap Services, by Zhao Li et al.
Transformer-based Graph Neural Networks for Battery Range Prediction in AIoT Battery-Swap Services
by Zhao Li, Yang Liu, Chuan Zhou, Xuanwu Liu, Xuming Pan, Buqing Cao, Xindong Wu
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed SEB-Transformer model uses a novel Transformer-based architecture to predict the battery range of Sharing E-Bike Batteries (SEBs). This AIoT-powered approach integrates dynamic heterogeneous graphs that capture user-bicycle interactions, enabling accurate predictions. The model also considers mean structural similarity and is evaluated on real-world datasets, outperforming nine competitive baselines. By dynamically adjusting cycling routes and charging station locations, the system optimizes the user experience and improves operational efficiency and sustainability of SEB services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Sharing E-Bike Battery (SEB) concept has become popular, but there’s a gap between users’ expectations and the reality of remaining battery range. To solve this problem, Artificial Intelligence of Things (AIoT) and battery-swap services have been integrated as a solution. A new Transformer-based model called SEB-Transformer is designed to predict the battery range of SEBs. The model uses a dynamic graph that captures user-bicycle interactions and considers mean structural similarity to make accurate predictions. This innovation helps bridge the gap between expectations and reality, making shared electric bicycles more reliable, user-friendly, and sustainable. |
Keywords
* Artificial intelligence * Transformer
