Utilidata Partners With The University Of Michigan Transportation Research Institute (UMTRI) On Electric Vehicle Charging Behavior Study
May 23, 2023
(ANN ARBOR, MI – May 23, 2023) Utilidata, an industry leading grid-edge technology company, today announced a research partnership with the University of Michigan Transportation Research Institute (UMTRI) to study the relationship between electric vehicle (EV) driving and charging behaviors to better understand how those behaviors impact the electric grid. Utilizing Utilidata’s smart grid chips, a first-of-its-kind distributed artificial intelligence (AI) platform, researchers have installed the technology on several EV charging stations across University of Michigan’s (U-M) campus to collect data on the impact on the grid.
Utilidata’s smart grid chips are collecting real-time voltage, current, and power data at the edge of the grid, allowing researchers to analyze and detect EV charging patterns at each location. This data will be analyzed alongside vehicle data from a group of participants within the research study who have a vehicle monitoring device installed on their EV. Data from the monitoring device includes start and stop time for charging, location of charging, trips taken, and acceleration/deceleration. Closely analyzing driving and charging behavior will lead to a better understanding of how to manage EV demand on the grid and help utilities develop customer smart charging programs. As UMTRI researchers continue to collect and analyze data, they’ll have access to the recently announced U-M Electric Vehicle Center for further collaboration. Results from the UMTRI study are anticipated later this year.
“As more people invest in electric vehicles, our electric grid needs to be ready to support the influx in energy demand. We’re thrilled to have partners like UMTRI whose research and studies have made major, lasting impacts on the transportation industry,” said Josh Brumberger, Utilidata’s Chief Executive Officer. “Access to real-time insights of when EVs are charging will help utilities identify charging locations and design better EV programs for customers.”
The study with UMTRI follows the recent news on Utilidata’s new innovation lab and its commercial manufacturing partnership in the state of Michigan.
“As we transition to electric transportation, the industry needs cutting edge technologies, like Utilidata’s smart grid chip, to meet the moment and bring us into the future”, said Jim Sayer, UMTRI director. “Partnering with Utilidata allows us to combine their energy and grid expertise with our decades of experience in conducting large scale research projects, data collection, and deployments that lead to a safer, more efficient, and equitable transportation and mobility future.”
EVs are projected to make up almost 50 percent of all car sales by 2030, and Michigan’s goal is to build the infrastructure necessary to support 2 million EVs on its roads by that time. As the transition to cleaner technologies continues, specifically in the automotive space, understanding the potential impact of these new technologies on the grid will be crucial. Utilidata’s smart grid chip, powered by the NVIDIA Jetson platform, is a new distributed AI platform installed alongside electric meters to integrate more distributed energy resources (DERs) including solar, battery storage, and EVs and enhance resiliency of the electric grid. Leveraging NVIDIA’s accelerated computing and AI technology, the smart grid chip collects and analyzes large amounts of data at the edge of the grid to provide utilities with real-time visibility of grid conditions.
Utilidata is a technology company bringing distributed artificial intelligence (AI) to the edge of the electric grid to accelerate decarbonization and better serve utility customers. The company’s smart grid chip, powered by NVIDIA, is the utility industry’s first scalable distributed AI solution, and provides an open, scalable and future-proof platform for grid operations. For more information, visit utilidata.com or follow @Utilidata on Twitter.