  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.
