Machine learning shows gap trends in EV adoption

Converging simulation, high performance computing (HPC), and artificial intelligence (AI), Altair applied its data analytics and visualization tools to create a predictive model of electric vehicle (EV) adoption across the United States. Leveraging the power of machine learning, Altair used publicly available data for 15 states, and ran various data modeling outcomes focused on key indicators to complete the missing datasets for the remaining 35 states.

Altair’s EV adoption model used key indicators and variables in consumer and county profiles (income, education, commute, availability of charging stations, and more) to show where adoption trends are strongest and weakest across the country. According to the results, certain areas of the country, including the coastlines, show aggressive EV adoption rates, while others like the Midwest barely register on the adoption map.

Some of the key adoption factors include:

• State incentives: Nearly 50% of states with high adoption rates have 10+ laws and incentives intended to drive EV growth

• Family income: 70% of counties with $53,650+ in median family income have high adoption rate

• Individual demographics: Typical EV adopter lives in a metro area, is college educated, commutes 30 miles or more, and lives in a blue state

“EV adoption rates have important implications environmentally, economically, socially, and politically,” noted Sam Mahalingam, chief technology officer at Altair. “Applying advanced data modeling and analytics technology to historical data can inform vital decision-making for EV manufacturers, policy makers, and other key stakeholders on what could make or break further adoption rates.”

Electric vehicle adoption is seen as a critical component to the nation’s expanding green economy and plans to overhaul the nation’s infrastructure. National Drive Electric Week provides a platform for EV topics, including some of the challenges to EV adoption and how manufacturers, municipalities, and businesses can help reduce them. Altair is contributing to this important ongoing conversation by capturing geographic and demographic data and using its powerful data analytics and machine learning solutions to process and present that data in a rich and visually compelling model.