Cost-optimal, robust charging of electrically-fueled commercial vehicle fleets via machine learning

• Main Authors: Shah, Jigar, Nielsen, Matthew, Reid, Andrew, Shane, Conner, Mathews, Kirk, Doerge, David, Piel, Richard, Anderson, Roger, Boulanger, Albert, Wu, Leon, Bhandari, Vaibhav, Gagneja, Ashish, Kressner, Arthur, Xiaohu Li, Sarkar, Somnath
• Format: Conference Proceeding
• Language: English
• Subjects: artificial intelligence; Buildings; controls; Electric vehicle (EV); Electric vehicles; Fuels; infrastructure; Learning systems; Load modeling; machine learning; optimization; peak demand; smart grid; supervisory control and data acquisition (SCADA); support vector machine (SVM); support vector regression (SVR)
• DOI: 10.1109/SysCon.2014.6819237

Electrification for commercial vehicle fleets presents opportunity to cut emissions, reduce fuel costs, and improve operational metrics. However, infrastructure limitations in urban areas often inhibit the ability to charge a significant number of electric vehicles, especially under one roof. This paper highlights a novel controls approach developed at GE Global Research in conjunction with Columbia University to fulfill the stated needs for intelligent charging of a commercial fleet of electric vehicles. This novel approach combines traditional control techniques with machine learning algorithms to adapt to customer behavior over time. The stated controls system is designed to regulate the charging rate of multiple electric vehicle supply equipment devices (EVSEs) to facilitate cost-optimal charging subject to past and predicted building load, vehicle energy requirements, and current conditions. In this embodiment, the system is primarily designed to mitigate electric demand charges that may otherwise occur due to charging at inopportune times. The system will be deployed at a New York City FedEx Express delivery depot in partnership with the local utility, Consolidated Edison Company of New York.