公开(公告)号：US20230304816A1

公开(公告)日：2023-09-28

申请号：US17701782

申请日：2022-03-23

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Bharatkumar HEGDE ,  Ibrahim HASKARA ,  Shailendra KAUSHIK ,  Chen-Fang CHANG

IPC: G01C21/34 ,  B60W40/08

CPC classification number: G01C21/3492 ,  G01C21/3484 ,  G01C21/3461 ,  B60W40/08 ,  B60W2040/0872

Abstract: A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

公开(公告)号：US20230294718A1

公开(公告)日：2023-09-21

申请号：US17695909

申请日：2022-03-16

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Ibrahim HASKARA ,  Bharatkumar Hegde ,  Chen-Fang Chang

IPC: B60W50/06 ,  G05B13/02

CPC classification number: B60W50/06 ,  G05B13/0265 ,  B60W2050/0075

Abstract: A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.