"""
EV Adoption Curve Tracker — Monitor electric vehicle adoption rates, sales data,
market penetration, and transition metrics globally using free public data sources.
Tracks BEV/PHEV sales, charging infrastructure growth, battery cost trends,
and adoption S-curves by country.
"""
import json
import urllib.request
from datetime import datetime
from typing import Any
def get_global_ev_adoption_data() -> dict[str, Any]:
"""Get global EV adoption statistics from IEA Global EV Data Explorer."""
# IEA publishes annual Global EV Outlook data
url = "https://api.iea.org/evs?parameters=EV+sales&category=Historical&mode=Cars&csv=false"
try:
req = urllib.request.Request(url, headers={"User-Agent": "QuantClaw/1.0"})
with urllib.request.urlopen(req, timeout=15) as resp:
data = json.loads(resp.read())
return {
"timestamp": datetime.utcnow().isoformat(),
"source": "IEA Global EV Data Explorer",
"data": data[:50] if isinstance(data, list) else data,
}
except Exception as e:
# Fallback with curated data
return {
"timestamp": datetime.utcnow().isoformat(),
"source": "IEA/BloombergNEF estimates",
"error": str(e),
"fallback_data": _get_curated_ev_data(),
}
def _get_curated_ev_data() -> dict[str, Any]:
"""Curated EV market data from public sources."""
return {
"global_ev_sales_millions": {
"2020": 3.0, "2021": 6.6, "2022": 10.5,
"2023": 14.2, "2024": 17.5, "2025_est": 20.0,
},
"market_share_percent": {
"2020": 4.1, "2021": 8.6, "2022": 14.0,
"2023": 18.0, "2024": 22.0, "2025_est": 25.0,
},
"top_markets_2024": [
{"country": "China", "ev_share_pct": 45, "sales_millions": 10.5},
{"country": "Europe", "ev_share_pct": 24, "sales_millions": 3.5},
{"country": "USA", "ev_share_pct": 10, "sales_millions": 1.8},
{"country": "South Korea", "ev_share_pct": 12, "sales_millions": 0.3},
{"country": "Japan", "ev_share_pct": 3, "sales_millions": 0.15},
],
}
def get_ev_stock_universe() -> dict[str, Any]:
"""Get the investable EV stock universe — pure-play and legacy OEMs."""
stocks = {
"pure_play_ev": [
{"ticker": "TSLA", "name": "Tesla", "market": "US", "focus": "BEV + Energy"},
{"ticker": "RIVN", "name": "Rivian", "market": "US", "focus": "EV trucks/SUVs"},
{"ticker": "LCID", "name": "Lucid Group", "market": "US", "focus": "Luxury BEV"},
{"ticker": "NIO", "name": "NIO Inc", "market": "US/China", "focus": "Premium BEV + BaaS"},
{"ticker": "XPEV", "name": "XPeng", "market": "US/China", "focus": "Smart BEV"},
{"ticker": "LI", "name": "Li Auto", "market": "US/China", "focus": "EREV/BEV"},
],
"legacy_oem_ev_leaders": [
{"ticker": "VWAGY", "name": "Volkswagen", "ev_brand": "ID series"},
{"ticker": "BMWYY", "name": "BMW", "ev_brand": "iX/i4/i5"},
{"ticker": "HMC", "name": "Hyundai", "ev_brand": "Ioniq"},
{"ticker": "GM", "name": "General Motors", "ev_brand": "Ultium/Equinox EV"},
{"ticker": "F", "name": "Ford", "ev_brand": "Mustang Mach-E/F-150 Lightning"},
],
"supply_chain": [
{"ticker": "ALB", "name": "Albemarle", "focus": "Lithium"},
{"ticker": "SQM", "name": "SQM", "focus": "Lithium"},
{"ticker": "CHPT", "name": "ChargePoint", "focus": "Charging infra"},
{"ticker": "BLNK", "name": "Blink Charging", "focus": "Charging infra"},
{"ticker": "QS", "name": "QuantumScape", "focus": "Solid-state batteries"},
],
}
return {
"timestamp": datetime.utcnow().isoformat(),
"universe": stocks,
"total_tickers": sum(len(v) for v in stocks.values()),
}
def get_battery_cost_curve() -> dict[str, Any]:
"""Track lithium-ion battery pack cost decline — key driver of EV adoption."""
cost_per_kwh = {
"2010": 1183, "2011": 899, "2012": 726, "2013": 599,
"2014": 540, "2015": 373, "2016": 295, "2017": 226,
"2018": 181, "2019": 157, "2020": 140, "2021": 132,
"2022": 151, "2023": 139, "2024": 115, "2025_est": 100,
}
return {
"timestamp": datetime.utcnow().isoformat(),
"source": "BloombergNEF Battery Price Survey",
"cost_per_kwh_usd": cost_per_kwh,
"parity_threshold": 100,
"parity_note": "$100/kWh widely considered ICE cost parity point",
"decline_pct_2010_2024": round((1 - 115 / 1183) * 100, 1),
"chemistry_trends": [
"LFP gaining share (lower cost, no cobalt)",
"Sodium-ion emerging for low-cost segment",
"Solid-state batteries expected post-2027",
"Cell-to-pack designs reducing pack-level costs",
],
}
def calculate_adoption_s_curve(country: str = "global") -> dict[str, Any]:
"""Model EV adoption S-curve and estimate inflection points."""
# S-curve parameters based on historical technology adoption
curves = {
"norway": {"current_share": 90, "phase": "saturation", "inflection_year": 2019},
"china": {"current_share": 45, "phase": "rapid_growth", "inflection_year": 2023},
"europe": {"current_share": 24, "phase": "early_majority", "inflection_year": 2024},
"usa": {"current_share": 10, "phase": "early_adopters", "inflection_year": 2026},
"japan": {"current_share": 3, "phase": "innovators", "inflection_year": 2028},
"india": {"current_share": 2, "phase": "innovators", "inflection_year": 2029},
"global": {"current_share": 22, "phase": "early_majority", "inflection_year": 2025},
}
curve = curves.get(country.lower(), curves["global"])
return {
"timestamp": datetime.utcnow().isoformat(),
"country": country,
"current_ev_share_pct": curve["current_share"],
"adoption_phase": curve["phase"],
"estimated_inflection_year": curve["inflection_year"],
"phases": {
"innovators": "0-5% market share",
"early_adopters": "5-16% market share",
"early_majority": "16-50% market share",
"late_majority": "50-84% market share",
"saturation": "84%+ market share",
},
"key_catalysts": [
"Battery cost below $100/kWh",
"Charging infrastructure density",
"Government ICE ban timelines",
"Used EV market maturation",
"Grid capacity for mass charging",
],
}
