ev-flow 3.0.2

Synthetic plug-in electric vehicle charging dataset pipeline and library API.

ev-flow

Synthetic plug-in electric vehicle (PEV) charging dataset pipeline and library API.

ev-flow generates realistic, fleet-scale charging behavior for residential and workplace EVs, grounded in the National Household Travel Survey (NHTS) and a regional sales-mix model. It exposes both a low-level pipeline (NHTS loading, donor matching, travel-week building, plug-in modeling, state-of-charge trajectory, hourly rasterisation) and a clean Fleet / Profile library API for downstream studies.

Install

pip install ev-flow

Then set PEV_SYNTH_DATA_ROOT to point at your data tree — see the next section. Without that step, generate_profiles(...) will raise FileNotFoundError because the wheel does not bundle the cached fleet bundles.

Data directory

ev-flow ships only the Python package; the cached fleet bundles (NHTS-derived parquets etc.) are not bundled in the wheel. Point the package at your local data directory via the PEV_SYNTH_DATA_ROOT environment variable:

export PEV_SYNTH_DATA_ROOT=/path/to/your/ev-flow-data

The directory should contain the pev/processed/<region>/<profile_type>_ev_synth/ layout that python -m pev_synth.cache_regen one ... writes. If PEV_SYNTH_DATA_ROOT is unset, the package falls back to <repo_root>/data/ — only useful in a pip install -e . dev checkout where the data/ tree sits next to src/.

First run / bootstrap (dev checkout)

The cached fleet bundles are not in the repo and not in the wheel — you build them from NHTS 2017 microdata, which is also not bundled. For a fresh pip install -e . dev checkout the one-time sequence is:

# (a) one-time: download (~84 MB ORNL zip) + process NHTS 2017.
#     Writes the California parquets and the national hhpub.csv/vehpub.csv
#     to data/pev/raw/nhts2017/.
python -m pev_synth.nhts_loader

# (b) build a cache for the (region, profile_type) you want.
#     Subcommands are `one`, `batch`, `audit`.
python -m pev_synth.cache_regen one --region bay_area --profile-type residential

# (c) now the library API works:
python -c "import pev_synth as ps; print(ps.generate_profiles('residential', n=10, region='bay_area'))"

Step (a) runs once: the loader persists the national hhpub.csv / vehpub.csv, so non-CA regions (boston, chicago, dallas_fort_worth, new_york_metro, seattle) are then handled automatically by cache_regen one without re-downloading.

Pip-installed (non-dev) users do not run the bootstrap — instead point PEV_SYNTH_DATA_ROOT at a prebuilt data tree as described above.

Quick start

import pev_synth as ps

ps.list_regions()
# ['bay_area', 'boston', 'chicago', 'dallas_fort_worth',
#  'la_basin', 'new_york_metro', 'seattle', 'us_national']

ps.list_profile_types()
# ['residential', 'workplace']

fleet = ps.generate_profiles('residential', n=1000, region='bay_area', seed=42)
prof  = fleet[0]

pa   = prof.generate_presence_absence('2001-01-01', '2001-01-08', freq='15min')
sess = prof.charging_sessions('2001-06-01', '2001-06-08')
soc  = prof.soc_trajectory('2001-06-01', '2001-06-08', freq='15min')

The PyPI distribution name is ev-flow but the Python import name is pev_synth (this mirrors the scikit-learn / sklearn convention).

Workplace caveat

In v2.0 the workplace cluster centres are fit from the 105-vehicle public EVWatts cohort, whose plug-in median is ~12:00 LT — approximately 3 hours later than the literature-canonical workplace median of ~09:00 LT. The W1-W4 validator checks flag this divergence as EXPLAINED_FAIL rather than as a bug. pev_synth surfaces this caveat as a RuntimeWarning at Fleet.__init__ whenever profile_type == 'workplace'. See src/pev_synth/plug_in_model.py:42-48 for the full discussion.

Modules

Module	Purpose
nhts_loader	National Household Travel Survey 2017 public-use file loader
vehicle_archetypes	N-EV archetype sampler
donor_matcher	NHTS donor-vehicle matcher
travel_week_builder	One-year travel sequence builder
plug_in_model	Session plug-in / dwell sampler
soc_trajectory	Continuous-time state-of-charge ledger + session extraction
hourly_resampler	15-minute and hourly plug-status rasteriser
validation_bounds_curator	Bound curation
validator	Validation runner + report writer (11 §10 + 3 integration + 1 DST + 1 winter + 10 workplace + 1 workplace-optim checks)
regions	8-region registry

Full library API reference and methodology rationale live in the documentation/ folder (expanding ahead of the docs-site launch).

Development

git clone https://github.com/bertravacca/ev-flow
cd ev-flow
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

Versioning

ev-flow follows Semantic Versioning. See documentation/versioning.md for what counts as a major/minor/patch change, the deprecation policy, and the distinction between the package version and a cache's methodology_version.

Data sources & attribution

ev-flow is grounded in public data sources. Two upstream notices are required:

• SPEECh Original Model (Powell, Cezar & Rajagopal, Mendeley Data, 2021) is licensed CC BY 4.0. ev-flow consumes a modified (pickle→JSON, reweighted) subset of its driver-group mixtures. https://doi.org/10.17632/gvk34mybtb.1
• This product uses the U.S. Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Full per-source licensing, citations, and the modification statement are in ATTRIBUTION.md (NHTS, Census ACS PUMS, EPA fueleconomy.gov, EV WATTS, CVRP, NYSERDA, Argonne EV-FACTS, NOAA, NREL).

License

MIT. See LICENSE. Note that the MIT license covers the ev-flow code; the upstream data sources retain their own licenses — see ATTRIBUTION.md.