  Given the rise of electric vehicle (EV) adoption, supported by government
policies and dropping technology prices, new challenges arise in the modeling
and operation of electric transportation. In this paper, we present a model for
solving the EV routing problem while accounting for real-life stochastic demand
behavior. We present a mathematical formulation that minimizes travel time and
energy costs of an EV fleet. The EV is represented by a battery energy
consumption model. To adapt our formulation to real-life scenarios, customer
pick-ups and drop-offs were modeled as stochastic parameters. A
chance-constrained optimization model is proposed for addressing pick-ups and
drop-offs uncertainties. Computational validation of the model is provided
based on representative transportation scenarios. Results obtained showed a
quick convergence of our model with verifiable solutions. Finally, the impact
of electric vehicles charging is validated in Downtown Manhattan, New York by
assessing the effect on the distribution grid.
