EV Adoption Analysis

BY: Barbie Jindal | LinkedIn | Dashboard

Multi-tool descriptive analysis of US electric vehicle adoption trends (1999–2026) using SAS, BigQuery, Python, and R — mapping state, county, and city-level registration patterns, manufacturer market share, and vehicle characteristics across high and low adoption regions.

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Business Questions Answered

#	Question
Q1	How has EV adoption grown over time? (BEV vs PHEV breakdown)
Q2	Which counties and cities lead EV adoption?
Q3	Which manufacturers dominate the EV market, and how does this vary by region?
Q4	Do high-adoption regions have higher electric range vehicles? What is the EV type and CAFV eligibility mix?

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Tech Stack

Tool	Purpose
SAS Studio	Data import, cleaning, and initial SQL-based aggregations
Google BigQuery	Cloud SQL — running advanced queries on the full dataset at scale
Python (pandas, matplotlib, google-cloud-bigquery)	BigQuery pipeline, data transformation, and chart generation
R (ggplot2, dplyr)	Statistical visualization and exploratory data analysis
Git	Version control

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Key Findings

• Tesla dominates with 40.77% market share — nearly 6x the next competitor (Chevrolet at 7.02%)
• King County (Seattle area) accounts for 133,815 registered EVs — more than 4x the next county (Snohomish at 33,766)
• High-adoption counties show a significantly higher share of long-range BEVs vs low-adoption counties, which skew toward PHEVs
• EV registrations grew exponentially from 1999, with the steepest growth post-2018

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Project Structure

EV_Adoption_Project/
│
├── ev_adoption_analysis.sas        # SAS: data import, cleaning, SQL aggregations
├── ev_adoption_bigquery.sql        # BigQuery SQL: all queries translated to cloud
├── ev_bigquery_pipeline.py         # Python: BigQuery pipeline + chart generation
├── r coding_ev_adoption_analysis.r # R: ggplot2 visualizations
│
├── make_share.csv                  # Manufacturer market share output
├── top_counties.csv                # Top counties by EV count output
│
└── outputs/                        # Generated charts
    ├── ev_adoption_over_time.png
    ├── bev_vs_phev.png
    ├── top_counties.png
    ├── top_cities.png
    ├── manufacturer_share.png
    └── range_by_adoption_group.png

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How to Run

BigQuery Pipeline (Python)

# Install dependencies
pip install google-cloud-bigquery pandas matplotlib db-dtypes

# Set up Google Cloud (one-time)
# 1. Go to console.cloud.google.com
# 2. Create a project and enable BigQuery API
# 3. Create dataset 'ev_project' and upload the CSV as table 'ev_population'
# 4. Download your service account key JSON

# Run the pipeline
python ev_bigquery_pipeline.py

SAS

Open ev_adoption_analysis.sas in SAS Studio and run all sections.

R Visualizations

# Install required packages
install.packages(c("readr", "dplyr", "ggplot2", "scales"))

# Run the script
source("r coding_ev_adoption_analysis.r")

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Data Source

Electric Vehicle Population Data — Washington State Department of Licensing via data.gov