Transformer-based Graph Neural Networks for Battery Range Prediction in AIoT Battery-Swap Services
Zhao Li, Yang Liu, Chuan Zhou, Xuanwu Liu, Xuming Pan, Buqing Cao, Xindong Wu
TL;DR
This work tackles battery range prediction for Sharing E-Bike Battery (SEB) systems by modeling rider–battery interactions as a dynamic heterogeneous graph and employing a structural Transformer, SEB-Transformer, to fuse graph topology with sequence data. A GNN handles edge-node updates while a Transformer handles range prediction, and a novel S^3IM regularization term enforces robust global structural similarity. On real-world data, SEB-Transformer outperforms nine baselines, including a vanilla Transformer, with a notable improvement of over $36.7\%$ in predictive accuracy measured by MAE, and enables real-time route and charging-station planning. Together, these advances enhance reliability, efficiency, and sustainability of AIoT-powered SEB services in urban mobility ecosystems.
Abstract
The concept of the sharing economy has gained broad recognition, and within this context, Sharing E-Bike Battery (SEB) have emerged as a focal point of societal interest. Despite the popularity, a notable discrepancy remains between user expectations regarding the remaining battery range of SEBs and the reality, leading to a pronounced inclination among users to find an available SEB during emergency situations. In response to this challenge, the integration of Artificial Intelligence of Things (AIoT) and battery-swap services has surfaced as a viable solution. In this paper, we propose a novel structural Transformer-based model, referred to as the SEB-Transformer, designed specifically for predicting the battery range of SEBs. The scenario is conceptualized as a dynamic heterogeneous graph that encapsulates the interactions between users and bicycles, providing a comprehensive framework for analysis. Furthermore, we incorporate the graph structure into the SEB-Transformer to facilitate the estimation of the remaining e-bike battery range, in conjunction with mean structural similarity, enhancing the prediction accuracy. By employing the predictions made by our model, we are able to dynamically adjust the optimal cycling routes for users in real-time, while also considering the strategic locations of charging stations, thereby optimizing the user experience. Empirically our results on real-world datasets demonstrate the superiority of our model against nine competitive baselines. These innovations, powered by AIoT, not only bridge the gap between user expectations and the physical limitations of battery range but also significantly improve the operational efficiency and sustainability of SEB services. Through these advancements, the shared electric bicycle ecosystem is evolving, making strides towards a more reliable, user-friendly, and sustainable mode of transportation.