EVREX - EV Resource eXchange

A Python library for electric vehicle fleet electrification assessment, adoption modeling, V2G potential analysis, battery degradation modeling, and grid impact assessment.

Installation

pip install evrex

With optional dependencies:

pip install evrex[fetch]   # OSM, World Bank, IMF data fetchers
pip install evrex[viz]     # Matplotlib visualization
pip install evrex[all]     # Everything

Quick Start

from evrex import (
    TransportContext,
    EVMacroData,
    run_ev_bass_diffusion,
    run_ev_tco_parity,
    generate_charging_profiles,
    compute_v2g_potential,
    compute_battery_degradation,
    assess_grid_impact,
)

# Define transport context
transport = TransportContext(
    fleet_by_category={"light": 5000, "medium": 800, "heavy": 200, "buses": 100},
    charging_stations=120,
    population=500_000,
)

# Run Bass diffusion adoption model
curve = run_ev_bass_diffusion(transport, base_year=2025, target_year=2050)
print(f"EV penetration by 2050: {curve.penetration[-1]:.1%}")
print(f"Peak charging demand: {curve.peak_charging_mw[-1]:.1f} MW")

# Battery degradation analysis
deg = compute_battery_degradation(v2g_cycles_per_day=0.5, chemistry="LFP")
print(f"Degradation: {deg.total_degradation_pct_per_year:.2f}%/year")
print(f"Break-even V2G rate: ${deg.breakeven_compensation:.0f}/MWh")

Adoption Models

Four methods for projecting EV fleet evolution:

Method	Function	Key Drivers
Logistic	run_ev_logistic_adoption()	GDP, fuel price, EV cost, infrastructure
Bass Diffusion	run_ev_bass_diffusion()	Innovation (p) and imitation (q)
TCO-Parity	run_ev_tco_parity()	Total cost of ownership comparison
Policy-Driven	run_ev_policy_driven()	ICE bans, emission targets, scrappage

Modules

• evrex.core - Adoption models, charging profiles, V2G, degradation, grid impact
• evrex.data - OSM, World Bank, IMF, IEA (bundled), BNEF (bundled) data

License

MIT