MOEV is implementing a system that automatically manages the recharging of battery electric buses at the City of Gardena's GTrans to minimize the electric bill, improve operational efficiency, reduce operator and driver stress, and extend the buses' battery life.
The system is backed by a $3.3 million grant from the California Energy Commission (CEC).
Additionally, the system will maximize the use of energy from solar panels at the transit agency and helps improve grid resilience.
"GTrans is excited to work with MOEV as we plan to convert our entire 58-bus fleet to zero-emission buses by 2035," said Ernie Crespo, MOEV's partner and GTrans' director of transportation. "We look forward to integrating this smart technology into our system and are thankful for this investment by the CEC."
The system is powered by software that resides on the cloud and employs Artificial Intelligence (AI) to optimize the timing, power levels, and sequence for charging the buses. It will intergrate real-time data such as a battery's state of charge, weather forecasts, the driving patterns and needs of the transit agency, and the current energy price from the electric grid.
The computer system limits grid charging to periods of low demand by shaping and shifting the electric-vehicle charging profiles.
MOEV's system also uses its AI to predict each vehicle's energy requirements based on continuous monitoring of its weight, speed, driving route and schedule, traffic, the air temperature, and the grade of the route. A machine-learning algorithm controls the charging of the electric buses.
GTrans is providing the testbed for the project. Six of its buses will participate in the initial phase of the MOEV project, and this number will be increased to 13 over the three years of the project.
"When it comes to the future of electric transportation, charging infrastructure is probably the most challenging component. MOEV's advanced AI technology continuously learns from data on electric charging and operations of a specific fleet operator and uses machine learning on the data gathered to customize an optimized charging strategy to remove stress and minimize cost," said Dr. Rajit Gadh, MOEV's co-founder.