Engineers determine optimal placement strategy for EV charging stations

Cornell University

ITHACA, N.Y. – Cornell University engineers have come up with a solution to a tricky problem: where to install charging stations for electric vehicles so they’re convenient for drivers and profitable for investors.

The research team found that in urban settings, installing an equal mix of two different kinds of stations – one that charges at a medium speed and another that charges more quickly – and distributing them strategically increases the chances that drivers will use them. And that in turn improves the profitability for investors by 50 to 100 percent, compared to current random placement strategies.

The research published in Applied Energy.

“Placing publicly available charging stations around cities sounds like a simple thing, but mathematically, it’s actually very hard,” said lead author Yeuchen Sophia Liu, an operations researcher in the lab of civil and environmental engineering professor Oliver Gao.

That’s because simple models don’t allow for the complexity of thousands of possible driver decisions, Liu said, not to mention factors like traffic and road characteristics.

The team reached back six decades to use Bayesian optimization, a mathematics strategy that uses past attempts at optimization to inform each subsequent attempt. That results in a much faster and productive analysis. It has become popular in machine learning algorithms.

“The Bayesian optimization model algorithm allows us to simulate millions of individual behaviors, while at the same time, find answers efficiently and quickly,” Liu said.

The team set up an algorithm that used Bayesian optimization to analyze data from the Atlanta region, home to about 6 million people. They studied the behavior of 30,000 vehicles on more than 113,000 simulated trips, forecasting a variety of commuter traffic patterns.

The team found that medium speed “level-2” commercial charging stations and direct-current, fast-charging “DCFC” stations serve different needs. Drivers who park for 20 minutes – while running into a grocery store, for example – are likely to choose fast charging spots. But if someone is going to work and parking for several hours, the driver will likely select the level-2 station.

In addition, a sensitivity analysis demonstrated that factors such as the size of the battery electric vehicle market, charging preferences and charging price have significant impacts on the optimal placement and profitability of an electric vehicle charging infrastructure project.

“Economically strategic placement of charging stations could play a pivotal role in accelerating the transition to zero-emission vehicles,” Liu said.

For additional information, see this Cornell Chronicle story.

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Journal

Applied Energy

DOI

10.1016/j.apenergy.2024.123975 

Article Title

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

Article Publication Date

1-Dec-2024