	US 12,319,266 B2
	Device for controlling hybrid vehicle and method thereof
Seung Woo Ha, Seoul (KR); and Jung Hwan Bang, Seoul (KR)
Assigned to Hyundai Motor Company, Seoul (KR); and Kia Corporation, Seoul (KR)
Filed by Hyundai Motor Company, Seoul (KR); and Kia Corporation, Seoul (KR)
Filed on Jul. 12, 2022, as Appl. No. 17/862,742.
Claims priority of application No. 10-2022-0003394 (KR), filed on Jan. 10, 2022.
Prior Publication US 2023/0219553 A1, Jul. 13, 2023
Int. Cl. B60W 20/11 (2016.01); B60W 40/105 (2012.01); B60W 50/00 (2006.01)

CPC B60W 20/11 (2016.01) [B60W 40/105 (2013.01); B60W 50/0097 (2013.01); B60W 2510/182 (2013.01); B60W 2520/10 (2013.01); B60W 2552/15 (2020.02)]

8 Claims

1. A device for controlling a hybrid vehicle, the device comprising:

a communication device configured to receive a plurality of data sets comprising a driving pattern and a control coefficient;

wherein the driving pattern comprises a time series combination of driving states defined based on driving data; and

wherein the driving data comprises a vehicle speed, a road slope, and a brake hydraulic pressure; and

a controller configured to:

extract speeds from the driving pattern,

learn a control coefficient prediction model by using an average and a standard deviation of the speeds, and

determine a control coefficient of the hybrid vehicle based on the control coefficient prediction model for which the learning is completed;

apply the determined control coefficient to the hybrid vehicle; wherein the controller is further configured to:

extract speeds from some initial driving states among driving states constituting the driving pattern of the hybrid vehicle;

perform a control coefficient prediction process of inputting the average and the standard deviation of the speeds into the control coefficient prediction model;

predict a plurality of control coefficients by performing the control coefficient prediction process at a reference time period; and

determine a recently predicted control coefficient among the plurality of control coefficients as an optimal control coefficient to apply to the hybrid vehicle.

2. The device of claim 1, wherein the controller is further configured to select a data set having an effective driving pattern from among the plurality of data sets.

3. The device of claim 1, wherein the controller is further configured to determine an optimal control coefficient by weighting a recently predicted control coefficient among the plurality of control coefficients.

4. The device of claim 1, wherein the controller is further configured to determine an average of the plurality of control coefficients as an optimal control coefficient.

5. A method of controlling a hybrid vehicle, the method comprising:

receiving, by a communication device, a plurality of data sets comprising a driving pattern and a control coefficient;

wherein the driving pattern comprises a time series combination of driving states defined based on driving data; and

wherein the driving data comprises a vehicle speed, a road slope, and a brake hydraulic pressure;

extracting, by a controller, speeds from the driving pattern;

learning, by the controller, a control coefficient prediction model by using an average and a standard deviation of the speeds; and

determining a control coefficient of the hybrid vehicle based on the control coefficient prediction model for which the learning is completed; and

applying the determined control coefficient to the hybrid vehicle;

wherein the determining of the control coefficient of the hybrid vehicle comprises:

operation A of extracting speeds from some initial driving states among driving states constituting the driving pattern of the hybrid vehicle; and

operation B of inputting the average and the standard deviation of the speeds into the control coefficient prediction model to predict a control coefficient;

predicting a plurality of control coefficients by performing the operation A and the operation B at a reference time period; and

determining a recently predicted control coefficient among the plurality of control coefficients as an optimal control coefficient to apply to the hybrid vehicle.