US 12,115,874 B2
Dynamic multiple bi-directional supply and demand matching for EV charging
Alaa M. Khamis, Courtice (CA); Roddi L. MacInnes, Bowmanville (CA); and Vyacheslav Berezin, Newmarket (CA)
Assigned to GM Global Technology Operations LLC, Detroit, MI (US)
Filed by GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed on Aug. 16, 2022, as Appl. No. 17/888,580.
Prior Publication US 2024/0059170 A1, Feb. 22, 2024
Int. Cl. G06F 17/00 (2019.01); B60L 53/62 (2019.01); B60L 53/63 (2019.01); B60L 53/68 (2019.01); B60L 58/12 (2019.01); B60L 58/16 (2019.01); B60L 58/24 (2019.01); G01C 21/00 (2006.01); G06Q 10/04 (2023.01); G06Q 30/0202 (2023.01); G06Q 30/0235 (2023.01)
CPC B60L 53/62 (2019.02) [B60L 53/63 (2019.02); B60L 53/68 (2019.02); B60L 58/12 (2019.02); B60L 58/16 (2019.02); B60L 58/24 (2019.02); G01C 21/3811 (2020.08); G06Q 10/04 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0235 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for matching, in real time, a dynamically changing energy demand of a plurality of electric vehicles (EVs) with a dynamically changing energy supply of a plurality of charging stations within a geofenced perimeter, the method comprising:
establishing the geofenced perimeter;
receiving telematics data, via a cloud-based server, as a set of EV information from each respective one of the EVs, the set of EV information including an uploaded current GPS location and an uploaded present state of charge (SoC) of each respective one of the EVs that are located in or traveling through the geofenced perimeter;
receiving, via the cloud-based server, as a set of charging station information from each respective one of the charging stations, the set of charging information including an uploaded current GPS location, current load, relative station use level, predicted time slot availability, and estimated time to charge of each respective charging station in the plurality of charging stations;
generating a state of charge (SoC) map and a charging station power map from the set of EV information and the set of charging station information, respectively, via the cloud-based server;
predicting the dynamically changing energy supply and the dynamically changing energy demand within the geofenced perimeter as a predicted energy supply and a predicted energy demand, respectively, via the cloud-based server using the SoC map and the charging station power map, including determining a potential total energy demand of the EVs located within the geofenced perimeter;
dynamically matching the EVs to at least one of the charging stations in the geofenced perimeter using (i) a reward function, (ii) the predicted energy supply within the geofenced perimeter, and (iii) the predicted energy demand within the geofenced perimeter, including generating a rank-ordered listing for each respective one of the EVs and/or each respective one of the charging stations to thereby maximize an expected discounted future reward; and
adaptively routing each respective one of the EVs to the respective one of the charging stations.