公开(公告)号：US20230052733A1

公开(公告)日：2023-02-16

申请号：US17656500

申请日：2022-03-25

Applicant: HERE GLOBAL B.V.

Inventor： Jerome BEAUREPAIRE ,  Dmitry KOVAL ,  Steven SCHULTING ,  Nicolas NEUBAUER ,  Remco TIMMER

IPC: B60L53/68 ,  H02J7/02 ,  G01C21/00

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

公开(公告)号：US20230051766A1

公开(公告)日：2023-02-16

申请号：US17445036

申请日：2021-08-13

Applicant: HERE GLOBAL B.V.

Inventor： Jerome BEAUREPAIRE ,  Dmitry KOVAL ,  Steven SCHULTING ,  Nicolas NEUBAUER ,  Remco TIMMER

IPC: B60L53/31 ,  B60L53/30 ,  B60L53/64 ,  H02J7/00 ,  B60L53/14

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.