Using Machine Learning Models to Forecast Electric Vehicle Destination and Charging Behavior

Electric vehicle (EV) adoption rates are rising in California because of successful state and federal air pollution mitigation regulations such as the Zero Emission Vehicle mandate, Low Emission Vehicle Regulation III for CA, the U.S. Greenhouse Gas Standard, U.S. Tier 3 Ozone Standard, and the Corporate Average Fuel Economy Standard. While this is a great feat for reducing local air pollution, improperly managed EV charging can lead to peak electricity demand loads that can strain local distribution networks and potentially increase point source emissions from power plants. Managed/smart charging is one of the most promising approaches to prevent suboptimal EV charging as it enables utility operators to control EV charging to reduce load spikes and take full advantage of renewable power. This study aims to develop a Recurrent Neural Network (RNN), an artificial neural network that is highly effective at learning sequential data such as trip logs, to forecast EVs’ trip destinations and charging behavior –information that is essential for electricity load aggregators to effectively manage charging loads. The RNN will be configured using a robust dataset, consisting of trips and charges that have occurred in California between 2015 and 2020 from 358 Plug-in Electric vehicles, spanning 9 models.

• Record URL:
  https://ncst.ucdavis.edu/project/using-machine-learning-models-forecast-electric-vehicle-destination-charging-behavior

Language

• English

Project

• Status: Completed
• Funding: $112,769
• Contract Numbers:

  DOT 69A3551747114

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 
• Managing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  University of California, Davis

  1 Shields Ave
  Davis, California  United States  95616
• Project Managers:

  Iacobucci, Lauren

• Performing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  University of California, Davis

  1 Shields Ave
  Davis, California  United States  95616
• Principal Investigators:

  Nitta, Christopher

  Tal, Gil

• Start Date: 20221001
• Expected Completion Date: 20231231
• Actual Completion Date: 20240610
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Electric vehicle charging; Electric vehicles; Machine learning; Neural networks; Origin and destination
• Geographic Terms: California
• Subject Areas: Energy; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01854530
• Record Type: Research project
• Source Agency: National Center for Sustainable Transportation
• Contract Numbers: DOT 69A3551747114
• Files: UTC, RIP
• Created Date: Aug 11 2022 2:49PM