Distributed Charging Coordination of Electric Trucks with Limited Charging Resources

TL;DR

The paper tackles range anxiety and congestion in charging electric trucks by proposing a two-layer distributed charging coordination framework where stations provide waiting estimates and trucks adjust charging plans en route. Trucks solve local mixed-integer decisions at ramps, while stations assign ports FCFS based on port availability, resulting in a fully distributed scheme with low communication overhead. A rollout-based method enables real-time, near-optimal charging decisions, and simulations on the Swedish road network show substantial reductions in total waiting times compared to offline, unlimited-facility planning. The approach is scalable, adaptable to changing conditions, and offers practical benefits for freight operators deploying electric trucks.

Abstract

Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial. As charging resources are limited, waiting times for some trucks can be prolonged at certain stations. To facilitate the efficient operation of electric trucks, we propose a distributed charging coordination framework. Within the scheme, the charging stations provide waiting estimates to incoming trucks upon request and assign charging ports according to the first-come, first-served rule. Based on the updated information, the individual trucks compute where and how long to charge whenever approaching a charging station in order to complete their delivery missions timely and cost-effectively. We perform empirical studies for trucks traveling over the Swedish road network and compare our scheme with the one where charging plans are computed offline, assuming unlimited charging facilities. It is shown that the proposed scheme outperforms the offline approach at the expense of little communication overhead.

Figures (8)

• Figure 1: An illustration of the road network and communication scheme between trucks and charging stations. Information changes are triggered between a truck and a charging station when the truck reaches a ramp. Each station has limited charging resources, where the occupied charging ports are shown in red blocks while the available ones are shown in green.
• Figure 2: Station-truck coordination framework. Upon arriving at a ramp, the chain of communication and computation is initiated by the truck via sending anticipated arrival times to the corresponding station. The procedure is complete after the station receives the planned charging time from the truck.
• Figure 3: The route model of each truck, where charging stations along the route are denoted by $S_k$, $k\!=\!1,\dots,N$, and ramps leading the shortest detour to each station are denoted by $r_k$ with $k\!=\!1,\dots,N$. The origin and destination are denoted by $r_0$ and $r_{N+1}$, respectively.
• Figure 4: The road network of a region in Sweden, where the potential charging stations are shown by green nodes while hubs, from which the origin and destination pairs are chosen, are shown by blue nodes. Indices of the charging stations are marked in red.
• Figure 5: Real waiting times of individual trucks.