  Despite the dependency of electric vehicle (EV) fleets on charging station
availability, charging infrastructure remains limited in many cities. Three
contributions are made. First, we propose an EV-to-charging station user
equilibrium (UE) assignment model with a M/D/C queue approximation as a
nondifferentiable nonlinear program. Second, to address the
non-differentiability of the queue delay function, we propose an original
solution algorithm based on the derivative-free Method of Successive Averages.
Computational tests with a toy network show that the model converges to a UE. A
working code in Python is provided free on Github with detailed test cases.
Third, the model is applied to the large-scale case study of NYC DCAS fleet and
EV charging station configuration as of July 8, 2020, which includes unique,
real data for 563 Level 2 chargers and 4 DCFCs owned by NYC and 1484 EVs owned
by NYC fleets distributed over 512 TAZs. The arrival rates of the assignment
model are calibrated in the base scenario to fit an observed average
utilization ratio of 7.6% in NYC. The model is then applied to compare charging
station investment policies of DCFCs to Level 2 charging stations based on two
alternative criteria. Results suggest a policy based on selecting locations
with high utilization ratio instead of with high queue delay.
