  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.
