EVNRG is an EV electrical demand simulation package that takes in trip data and turns it into useful energy data given a set of assumptions.
This project was inspired by an earlier effort to simulate electrical demand from a theoretical electrified fleet replacement. This tool is designed to track energy usage across an arbitrarily large fleet, with an arbitrary number of EVSE, and rules that govern when charging occurs, and what type of queuing logic to use.
- Documentation: https://evnrg.readthedocs.io.
EVNRG takes in scenario definitions and interval travel data in order to generate arrays (Pandas Series) of:
- Electrical demand (per EVSE bank)
- Fuel consumption (per vehicle)
- Battery state (per vehicle)
- Deferred distance (per vehicle)
- EVSE occupancy (per EVSE bank)
- Write unit tests
- Create Dask dispatcher for parallelized simulations
- Implement geofenced rules
- Implement smart charging behavior (throttling, round-robin) for situations where a Bank's maximum power is lower the sum total of each EVSE's maximum output.
- Implement analyis (dependednt on openei-rates)
This package is Free software under the Apache Software License 2.0. See LICENSE for more information.
Initially written by Alex Campbell as part of a master's of science thesis project at the University of California, Davis Energy Graduate Group. This project falls under the auspices of the UC Davis Institute of Transportation Studies (ITS) and the UC Davis Plug-in Hybrid and Electric Vehicle Research Center. This project was funded by the Office of Naval Research NEPTUNE program.
This package's skeleton was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.