US 11,780,346 B2
Scheduling pre-departure charging of electric vehicles
Ariel Telpaz, Givat Haim Meuhad (IL); Barak Hershkovitz, Herzliya (IL); Nadav Baron, Herzliya (IL); Ravid Erez, Hod-Hasharon (IL); Boris Kabisher, Yokneam (IL); and Omer Zerbib, Kfar Saba (IL)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed on Mar. 29, 2021, as Appl. No. 17/215,587.
Prior Publication US 2022/0305941 A1, Sep. 29, 2022
Int. Cl. H02J 7/00 (2006.01); B60L 53/66 (2019.01); B60H 1/00 (2006.01); G06N 3/045 (2023.01); G06N 20/00 (2019.01); B60L 53/60 (2019.01)
CPC B60L 53/66 (2019.02) [B60H 1/0073 (2019.05); B60L 53/60 (2019.02); G06N 3/045 (2023.01); G06N 20/00 (2019.01); H02J 7/0048 (2020.01); H02J 7/0071 (2020.01); B60L 2240/34 (2013.01); B60L 2250/14 (2013.01); B60L 2260/46 (2013.01); B60L 2260/58 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory device; and
one or more hardware processors configured by machine-readable instructions for scheduling pre-departure charging for electric vehicles, the one or more hardware processors configured to:
predict a user-departure time based on a first machine learning prediction model, wherein the user-departure time represents when a user initiates driving an electric vehicle;
determine a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model;
determine a battery-temperature of a battery of the electric vehicle to be set at the user-departure time based on a third machine learning prediction model;
determine a present charge level of a battery of the electric vehicle;
compute a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature; and
start charging the battery at the charging start-time.