Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction

TL;DR

This paper explores how spatial-temporal demand prediction can optimize battery-swapping services for freight trucks, improving deployment strategies and operational efficiency in large highway networks.

Contribution

It introduces a combined demand prediction and optimization framework tailored for freight truck battery swapping, highlighting the transition from mobile to fixed stations over time.

Findings

Mobile stations are preferred initially for battery swapping.

Fixed stations become more advantageous as the system matures.

Machine learning models effectively predict traffic patterns for better planning.

Abstract

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.