There is great value in an accurate prediction of a trip’s energy consumption, particularly if the prediction can be made for many trips in a population of drivers and vehicles. This is particularly true as electric vehicles become more common and regions work to limit air pollution and greenhouse gas emissions. Currently, trip energy consumption is often either estimated based simply on the vehicle's rated fuel economy and a trip's distance. Alternatively, these estimates are done through complex, black-box simulations that require detailed information on the trip characteristics which cannot be predicted in advance. These simulations are also slow to run and difficult to operate at scale for large datasets, in real time. TripEnergy combines the analytic rigor of simulation methods with ease of use and flexibility and is particularly geared towards predictive estimates of trips’ energy consumption across a population.