Artificial Intelligence Based Heavy-Duty Fleet Charging to enable Distributed Energy Resource Integration
Integrating artificial intelligence and machine learning with smart charging and distributed energy resources to accelerate electrification of medium and heavy duty fleets.
MOEV Inc.
Recipient
Los Angeles, CA
Recipient Location
26th
Senate District
54th
Assembly District
$2,593,886
Amount Spent
Active
Project Status
Project Update
Since agreement execution, MOEV has been demonstrating its smart EV fleet charging management technology and gathering operational data to verify its effectiveness. The Gardena Transit site has acquired 3 mobile chargers to support 7 new EV buses. Gardena Transit is undergoing review by Southern California Edison's Charge Ready Transport program to support the new permanent charging infrastructure to be installed by the fall of 2025. The associated solar and storage DER system is expected to be fully operational in mid-2027.
The Issue
This project will demonstrate an artificial intelligence- based fleet charging management system with integrated DERs including EVs, chargers, battery energy storage system and solar photovoltaic, for a medium and heavy duty fleet electrification application. The project will evaluate cost management, resilience, and renewable integration of integrating battery electric transit buses at Gardena Transit (GTrans) with onsite PV solar and a battery energy storage system. Project partner, LBNL will use its system HEVI-LOAD for modeling and simulation of MDHD fleets, and project partner ETAP will support modeling of the battery energy storage system and controls.
Project Innovation
This project will demonstrate an artificial intelligence-based fleet charging management system with integrated DERs including EVs, chargers, battery energy storage system and solar photovoltaic, for a medium- and heavy-duty fleet electrification application. The project will evaluate cost management, resilience, and renewable integration of integrating battery electric transit buses at Gardena Transit (GTrans) with onsite PV solar generation and a battery energy storage system. Project partner, LBNL will use its system HEVI-LOAD for modeling and simulation of MDHD fleets, and project partner ETAP will support modeling of the battery energy storage system and controls.
Project Goals
Reduce greenhouse gas (GHG) emissions by converting Gardena City Transit to an electric bus fleet.
Maximize the utilization of renewable energy source by performing AI-based management of charging with solar PV and BESS.
Demonstrating a smart charging platform that enables quick replicability and scalability for MDHD electric vehicle fleets.
Project Benefits
This agreement will result in benefits including greater reliability and lower costs by deploying a smart charging technology combined with the integration of DERs to enable flexible support capability of the grid. The innovative artificial intelligence based machine learning charging technology is designed to effectively manage the DERs including onsite solar, battery storage, and electric transit buses. This creates the ability to optimize the management of MDHD EV charging to minimize operating expenses and flexibility to shift loads based on utility signals and grid needs.
Affordability
Affordability
This project will evaluate the potential to reduce the costs of medium and heavy duty electrification through smart charging and the integration of distributed energy resources including PV solar and battery storage.
Reliability
Reliability
The AI smart charging technology will enable efficient demand site management in combination with distributed generation asset management to lower overall peak demand and shift loads during critical times.
Key Project Members
Dr. Peter Chu
Senior VP Engineering and Co-Founder
MOEV, Inc.
Subrecipients
South Bay Cities Council of Governments
Lawrence Berkeley National Laboratory
Gardena City Transit (GTrans)
IIT Alumni Association of Southern California
Match Partners
Gardena City Transit (GTrans)
