US 12,233,740 B2
Machine learning for optimization of power distribution to electric vehicle charging ports
David J. Klein, Los Altos, CA (US); Andrew Forrest, San Francisco, CA (US); and Praveen K. Mandal, San Francisco, CA (US)
Assigned to Volta Charging, LLC, San Francisco, CA (US)
Filed by Volta Charging, LLC, San Francisco, CA (US)
Filed on Jun. 14, 2022, as Appl. No. 17/840,528.
Claims priority of provisional application 63/210,455, filed on Jun. 14, 2021.
Prior Publication US 2022/0396172 A1, Dec. 15, 2022
Int. Cl. B60L 53/68 (2019.01); B60L 53/64 (2019.01); B60L 53/66 (2019.01); G05B 13/02 (2006.01)
CPC B60L 53/68 (2019.02) [B60L 53/64 (2019.02); B60L 53/66 (2019.02); G05B 13/0265 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method for dynamically controlling distribution of power amongst a plurality of charging station ports that includes a particular port, comprising:
obtaining input data that includes a sequence of time intervals and a charging ramp-up time of a vehicle that is connected to the particular port in a particular time interval of the sequence of time intervals;
training, by reinforcement learning that is based on the input data, one or more machine learning engines to determine a particular power distribution, among the plurality of charging station ports, wherein the reinforcement learning comprises determining a scalar number for each objective in a plurality of objectives that include:
maximizing a number of vehicles serviced in the particular time interval,
maximizing a daily power output, and
minimizing a use of peak-time power; and
configuring the plurality of charging station ports according to the particular power distribution to:
decrease the power allocated to the particular port during a peak time and
increase the power to the particular port during a non-peak time;
wherein the method is performed by one or more computers.