Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Ting Bai, Yuchao Li, Andreas A. Malikopoulos, Karl Henrik Johansson, Jonas Mårtensson

TL;DR

This work tackles charging coordination for electric trucks faced with limited charging ports and travel-energy uncertainties. It proposes a fully distributed framework where stations forecast waiting times and trucks compute charging plans using arrival-time windows for distant stations, enabling robust decisions despite bounded disturbances. Key contributions include waiting-time forecast models, estimated arrival-time windows, and a charging-planning method with feasibility guarantees under uncertainty, validated by large-scale simulations on the Swedish road network showing substantial reductions in waiting times and operational costs. The approach improves practicality and scalability of truck-level charging coordination, offering significant real-world impact for freight electrification and infrastructure planning.

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.

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

TL;DR

This work tackles charging coordination for electric trucks faced with limited charging ports and travel-energy uncertainties. It proposes a fully distributed framework where stations forecast waiting times and trucks compute charging plans using arrival-time windows for distant stations, enabling robust decisions despite bounded disturbances. Key contributions include waiting-time forecast models, estimated arrival-time windows, and a charging-planning method with feasibility guarantees under uncertainty, validated by large-scale simulations on the Swedish road network showing substantial reductions in waiting times and operational costs. The approach improves practicality and scalability of truck-level charging coordination, offering significant real-world impact for freight electrification and infrastructure planning.

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 trucks traversing the Swedish road network for days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by compared to offline charging plans computed by the trucks without coordination and update, and by compared to the coordination scheme assuming zero waiting times at distant stations.
Paper Structure (32 sections, 2 theorems, 23 equations, 14 figures)

This paper contains 32 sections, 2 theorems, 23 equations, 14 figures.

Table of Contents

  1. Sets
  2. Parameters
  3. Variables
  4. Introduction
  5. Problem Description and Overview of the Coordination Framework
  6. Problem Description
  7. Overview of the Coordination Framework
  8. Computation by the Stations
  9. Computation by the Trucks
  10. Communication and Coordination Architecture
  11. Estimation and Computation at Charging Stations
  12. Waiting Time Forecast Model
  13. Waiting Time Computation for Nearby Trucks
  14. Waiting Time Estimation for Distant Trucks
  15. Charging Scheduling and Information Update
  16. ...and 17 more sections

Key Result

Proposition 1

Let $\underline{a}_\ell^k$ and $\bar{a}_\ell^k$ be the earliest and latest arrival times of a truck at ramp $r_{\ell}$ computed at ramp $r_k$. Suppose that the truck's effective charging powers at stations $S_k$ and $S_{k+1}$ are identical, i.e., $\min\{P_k,P_{\max}\}=\min\{P_{k+1},P_{\max}\}$, and

Figures (14)

  • Figure 1: The road network and communication scheme between trucks and charging stations. Taking the blue truck as an example, it communicates with the charging stations along its route each time it reaches a ramp. Charging stations have limited facilities, where occupied charging ports are indicated by red blocks and available ones by green. Additionally, each station maintains a forecast model to estimate waiting times for trucks.
  • Figure 2: An overview of the communication and coordination framework between each truck and the charging stations.
  • Figure 3: The route model of each truck, where the pre-planned route between the origin and destination is represented by the blue path. Ramps in the route are marked in yellow and the connected charging stations $S_k$, $k\!=\!1,\dots, N$ are shown by green labels. Green and red blocks at each station indicate the availability of the charging ports.
  • Figure 4: A flow chart illustrating the operation procedure of the coordination framework, taken from one truck's perspective. Here, est. stands for estimated, max. stands for maximum, and $t_{sys}$ represents the system time used to monitor the status of every truck.
  • Figure 5: (a) The transport flow among different regions in Sweden. (b) The potential charging stations for electric trucks, where the size of the green nodes denotes the number of charging ports (i.e., capacity) at the station. Four representative charging stations are marked in red and their waiting time plots are shown in Figure \ref{['Fig.6']}. (c) The route model of a single truck.
  • ...and 9 more figures

Theorems & Definitions (5)

  • Proposition 1
  • Remark 1
  • Remark 2
  • Remark 3
  • Theorem 1