How AI could change electric vehicle charging

New AI tools could provide utilities with real-time data to make the power grid and electric vehicle charging more reliable, a very small value. to study by the University of Michigan Transportation Research Institute (UMTRI) and startup Utilidata suggests.

Researchers are using AI to analyze EV charging behavior, hoping this knowledge can improve drivers’ experience and help utility companies prepare for increased electricity demand. So far, they have found that charging electric vehicles can consume power inconsistently and decrease power quality, which can wear out charging equipment.

These underlying issues waste energy and can lead to broken electric vehicle chargers, which have become a bane for drivers. Therefore, the ability to immediately detect and even predict these issues with AI could be a game changer. AI models could give utilities an idea of ​​how charging might impact the electric grid, the authors write. And they can also advise drivers on where and when to charge and help EV charging companies better maintain their equipment.

UMTRI initially contacted Utilidata for this pilot study, which aims to inform the design of a larger research project investigating the same questions. UMTRI says it is already working with the North American Electric Reliability Council to address its initial findings.

For this study, researchers installed electric meter adapters equipped with Utilidata’s Karman AI platform at six EV charging stations at the University of Michigan. Karman analyzed voltage, current, power and other dynamics between March and June of last year. The study authors also installed devices in the vehicles of 10 drivers who frequent the university campus to monitor their charging habits.

Although this project is still in its early stages, researchers are hopeful that it can help people prepare for the challenges that arise with the electrification of vehicle fleets. In the US, aging electrical grids are already forcing to accommodate growing electricity demand from AI data centers, crypto mining and clean energy technologies. But compared to a data center, utilities have a harder time predicting when and where EVs will be connected to the grid.

Utilities must deal with this unpredictability without real-time data to help them adjust. These blind spots are becoming a bigger problem in the “grid edge”, where customers are increasingly connecting their own devices to the grid, such as batteries for electric vehicles and solar panels.

“AI plays a big role at the edge of the network,” says Siobhan Powell, a postdoctoral researcher at ETH Zürich who was not involved in the study. “That didn’t used to be the case, right? There wasn’t a lot of interesting stuff going on, and now that we have a chance to control it, there’s more opportunity and more value in knowing what’s going on.”

One problem researchers identified in this study was inconsistent, short-cycle power consumption from vehicles that stopped and started charging even after the battery was fully charged. Not only does this burn energy inefficiently, it can also overheat wires and transformers. They also found that EV charging reduces power quality when electricity deviates from ideal voltage and frequency ranges. Flickering is a telltale sign of poor power quality, which can also cause more wear and tear on equipment.

“The biggest takeaway, I think, is that we confirmed that there are a lot of electric vehicle behaviors that are not known to anyone – not known to car owners, not known to grid operators, not known to charger OEMs,” vice president of Utilidata. of product solutions, says Yingchen Zhang. “So there’s a huge need to really open up all this data.”

The study authors cautiously argue that locations with lots of unmanaged electric vehicle charging could have greater impacts on the electric grid. In a worst-case scenario, they say it could impact power supply to other customers. But Zhang is quick to say that the chance of a power outage as a result is very low.

“It’s good to know exactly how these new charges affect voltage and power quality issues locally, but I wouldn’t jump to outages,” says Powell, because there are many steps utilities can take to avoid outages. And again, this is a very small study of unpredictable billing behavior, so it’s still too early to make sweeping statements about broader grid impacts from these initial findings.

Both Powell and Zhang want to avoid causing undue alarm about the impact EV charging could have on the grid — especially as EV adoption faces partisan attacks. “Many of the fears are due to the fact that people do not know the real behavior of the EV,” says Zhang. “So actually revealing this information will alleviate a lot of those fears.”

The rise of AI has also raised concerns about increasingly power-hungry data centers overloading the grid. Zhang says his company is thinking about this too, using custom Nvidia chips to consume less power than more generic AI chips. And using machine learning in this way to analyze data generally consumes much less energy than generative AI models that output text and images.

It all comes down to preparedness as the key to reinforcing the power grid against new technologies that change the way we live, work and get around. Fleets of electric vehicle batteries could even help bolster the grid, acting as virtual power plants that feed into the grid when needed. Automakers are already testing this, in part to make EVs more affordable for customers. “We need electric vehicles. We need this transition to happen. And there are things we have to do to prepare the network, but we can do them,” says Powell.