Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction
This addresses the problem of carbon emissions from freight trucks by enhancing battery-swapping services, though it is incremental as it applies existing prediction and optimization methods to a new domain.
The paper tackles the challenge of limited battery energy and long charging times for heavy-duty electric trucks by proposing a battery-swapping service, using spatial-temporal demand prediction and optimization to improve efficiency, with analysis on a 2,500-mile highway network showing that mobile stations are favored initially but fixed stations become preferred as the system matures.
Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred.