Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertainties

TL;DR

This paper presents a distributed coordination framework for electric truck charging that reduces waiting times and operational costs by handling travel uncertainties through communication and forecasting.

Contribution

It introduces a novel distributed approach that coordinates charging schedules considering travel uncertainties and station capacities, improving efficiency over traditional methods.

Findings

Reduces average waiting time by 46.1% compared to non-coordinated plans.

Achieves a 33.8% reduction in waiting time over zero-wait assumptions.

Demonstrates effectiveness through simulations with 1,000 trucks over 40 days.

Abstract

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks' nontrivial charging times and the stations' limited charging facilities. Our goal is to reduce the trucks' waiting times at the stations while minimizing individual trucks' operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for $1,000$ trucks traversing the Swedish road network for $40$ days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by $46.1\%$ compared to offline charging plans computed by the trucks without coordination and update, and by $33.8\%$ compared to the coordination scheme assuming zero waiting times at distant stations.