IP Library Granted Patent US 12,728,864
Granted Patent B2
US 12,728,864 · App. 18/807,449 · Granted Sep 8, 2026

Vehicle control device and method

Inventors: Min Seok Song (Gwacheon-si, KR); Hyeon Jun Lee (Suwon-si, KR); Hyeon Woo Kim (Seoul, KR); Ji Seop Lee (Seoul, KR)
Assignees: HYUNDAI MOTOR COMPANY; KIA CORPORATION
B60W30/182B60L58/13B60W50/0098B60L2240/12B60L2240/16B60L2260/20B60L2260/54B60W2510/244B60W2520/105B60W2530/209B60W2554/4041B60W2554/4042B60W2556/40
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,728,864
App. No.
18/807,449
Granted
Sep 8, 2026
Kind
B2
Abstract

A vehicle control device and a method thereof are provided. The vehicle control device includes a processor, a sensor, a battery, and a memory. The processor: predicts a change in speed of a vehicle based on a route of the vehicle, using map information received from an external server, while driving the vehicle; divides the route of the vehicle into a plurality of partial routes, using the change in speed of the vehicle; obtains state of charge (SOC) information of the battery in each partial route of the plurality of partial routes, wherein the SOC information changes based on the change in speed of the vehicle; and obtains ratio information between a transit time when the vehicle passes through each partial route and a driving time of the vehicle based on a hybrid electric vehicle (HEV) mode in each partial route, using the SOC information.

Claims (68)

1 . A vehicle control device, comprising:

a processor;

a sensor;

a battery; and

a memory,

wherein the processor is configured to:

predict a change in speed of a vehicle based on a route of the vehicle, and map information received from an external server, while driving the vehicle;

divide the route of the vehicle into a plurality of partial routes, using the change in speed of the vehicle;

obtain state of charge (SOC) information of the battery in each partial route of the plurality of partial routes, wherein the SOC information changes based on the change in speed of the vehicle;

obtain ratio information between a transit time when the vehicle passes through each partial route and a driving time of the vehicle based on a hybrid electric vehicle (HEV) mode in each partial route, using the SOC information, wherein the processor is configured to obtain the ratio information from the map information, in a first layer including a dynamic programming algorithm and associated with global path planning;

predict acceleration of the vehicle, in a partial route in which the vehicle is located among the plurality of partial routes, using the sensor;

obtain fuel consumption information for minimizing fuel consumed while the vehicle is traveling along the route, in the partial route in which the vehicle is located, using acceleration information indicating the predicted acceleration of the vehicle and the ratio information, wherein the processor is configured to obtain the fuel consumption information, using the ratio information obtained in the first layer, in a second layer including at least one of an acceleration prediction model, a vehicle required power model, or a vehicle control model, and associated with local path planning; and

control an operation of at least one of a motor or an engine of the vehicle along the route, based on a mode for minimizing an amount of increase in fuel compared to an amount of increase in SOC between an electric vehicle (EV) mode or the HEV mode, using the fuel consumption information.

2 . The vehicle control device of claim 1 , wherein the processor is configured to:

predict the acceleration of the vehicle, based on at least one of a relative location of another vehicle located around the vehicle and a speed of the other vehicle, using the sensor.

3 . The vehicle control device of claim 1 , wherein the processor is configured to:

identify an average speed of the vehicle in each partial route, wherein the average speed follows the change in speed of the vehicle; and

obtain the SOC information of the battery in each partial route, the SOC information indicating an SOC of the battery, and the SOC information corresponding to the average speed of the vehicle.

4 . The vehicle control device of claim 1 , wherein the processor is configured to:

predict power, using at least one of a rolling resistance coefficient (RRC) of a wheel of the vehicle, an aerodynamic coefficient, an equivalent test weight (ETW), or any combination thereof;

predict a speed of the vehicle, the speed to be obtained based on the acceleration; and

obtain the fuel consumption information, using at least one of power information indicating the predicted power, speed information indicating the speed of the vehicle, the ratio information, or any combination thereof.

5 . The vehicle control device of claim 4 , wherein the processor is configured to:

predict a first energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the HEV mode, using the power information and the speed information;

predict a second energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the EV mode; and

obtain the fuel consumption information, using the first energy amount and the second energy amount.

6 . The vehicle control device of claim 5 , wherein the processor is configured to:

calculate another piece of SOC information indicating an SOC of the battery using the power information and the speed information, the other piece of SOC information changes when controlling the vehicle based on the EV mode; and

obtain the fuel consumption information, using the calculated other SOC information and the ratio information.

7 . The vehicle control device of claim 1 , wherein the processor is configured to:

identify the transit time when the vehicle passes through each partial route based on an average speed of the vehicle, wherein the average speed follows the change in speed of the vehicle.

8 . The vehicle control device of claim 1 , wherein the processor is configured to:

control the vehicle based on the EV mode or the HEV mode, along the partial route in which the vehicle is located, using sub-ratio information corresponding to the partial route in which the vehicle is located in the ratio information and the acceleration information.

9 . The vehicle control device of claim 1 ,

wherein the processor is configured to:

obtain the ratio information, using engine information indicating whether to drive the engine for controlling the vehicle based on the HEV mode.

10 . The vehicle control device of claim 1 , wherein the map information includes at least one of grade information of a road corresponding to the route, a speed limit of the road, traffic volume on the road, or any combination thereof.

11 . A vehicle control method, comprising:

predicting a change in speed of a vehicle based on a route of the vehicle, using map information received from an external server, while driving the vehicle;

dividing the route of the vehicle into a plurality of partial routes, using the change in speed of the vehicle;

obtaining state of charge (SOC) information of a battery in each partial route of the plurality of partial routes, wherein the SOC information changes based on the change in speed of the vehicle;

obtaining ratio information between a transit time when the vehicle passes through each partial route and a driving time of the vehicle based on a hybrid electric vehicle (HEV) mode in each partial route, using the SOC information, wherein obtaining the ratio information includes obtaining the ratio information from the map information, in a first layer including a dynamic programming algorithm and associated with global path planning, and

predicting acceleration of the vehicle, in a partial route in which the vehicle is located among the plurality of partial routes, using a sensor;

obtaining fuel consumption information for minimizing fuel consumed while the vehicle is traveling along the route, in the partial route in which the vehicle is located, using acceleration information indicating the predicted acceleration of the vehicle and the ratio information, wherein obtaining the fuel consumption information includes obtaining the fuel consumption information, using the ratio information obtained in the first layer, in a second layer including at least one of an acceleration prediction model, a vehicle required power model, or a vehicle control model, and associated with local path planning; and

controlling an operation of at least one of a motor or an engine of the vehicle along the route, based on an electric vehicle (EV) mode or the HEV, using the fuel consumption information.

12 . The vehicle control method of claim 11 , wherein the predicting of the acceleration includes:

predicting the acceleration of the vehicle, based on at least one of a relative location of another vehicle located around the vehicle and a speed of the other vehicle, using the sensor.

13 . The vehicle control method of claim 11 , wherein the obtaining of the SOC information of the battery includes:

identifying an average speed of the vehicle, the average speed following the change in speed of the vehicle, in each partial route; and

obtaining the SOC information of the battery, the SOC information indicating an SOC of the battery, and the SOC information corresponding to the average speed of the vehicle in each partial route.

14 . The vehicle control method of claim 11 , wherein obtaining the fuel consumption information includes:

predicting power, using at least one of a rolling resistance coefficient (RRC) of a wheel of the vehicle, an aerodynamic coefficient, an equivalent test weight (ETW), or any combination thereof;

predicting a speed of the vehicle, the speed to be obtained based on the acceleration; and

obtaining the fuel consumption information, using at least one of power information indicating the predicted power, speed information indicating the speed of the vehicle, the ratio information, or any combination thereof.

15 . The vehicle control method of claim 14 , wherein obtaining the fuel consumption information includes:

predicting a first energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the HEV mode, using the power information and the speed information;

predicting a second energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the EV mode; and

obtaining the fuel consumption information, using the first energy amount and the second energy amount.

16 . The vehicle control method of claim 15 , wherein the obtaining of the fuel consumption information includes:

calculating another piece of SOC information indicating an SOC of the battery using the power information and the speed information, the other piece of SOC information changes when controlling the vehicle based on the EV mode; and

obtaining the fuel consumption information, using the calculated other SOC information and the ratio information.

17 . The vehicle control method of claim 11 , further comprising:

identifying the transit time when the vehicle passes through each partial route based on an average speed of the vehicle, the average speed following the change in speed of the vehicle.

18 . The vehicle control method of claim 11 , wherein the controlling of the vehicle includes:

controlling the vehicle based on the EV mode or the HEV mode, along the partial route in which the vehicle is located, using sub-ratio information corresponding to the partial route in which the vehicle is located in the ratio information and the acceleration information.

19 . The vehicle control method of claim 11 , wherein obtaining the ratio information includes:

obtaining the ratio information, using engine information indicating whether to drive the engine for controlling the vehicle based on the HEV mode.

20 . The vehicle control method of claim 11 , wherein the map information includes at least one of grade information of a road corresponding to the route, a speed limit of the road, traffic volume on the road, or any combination thereof.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 20, 2024
From: SONG, MIN SEOK; LEE, HYEON JUN; KIM, HYEON WOO; LEE, JI SEOP
To: HYUNDAI MOTOR COMPANY; KIA CORPORATION
Reel/Frame 068346/0168 →
Priority Claims (1)
KR 10-2024-0008802 · Jan 19, 2024 · national
Continuity (1)
Related Publication 20250236299A1 · Jul 24, 2025
References Cited (32)
US 9714024B2 · Yoon · 2017 [cited by applicant]
US 10668824B2 · Ourabah · 2020 [cited by applicant]
US 10787165B2 · Park · 2020 [cited by applicant]
US 20060167784A1 · Hoffberg · 2006 [cited by examiner]
US 20150336572A1 · Makimura · 2015 [cited by examiner]
US 20160137185A1 · Morisaki · 2016 [cited by examiner]
US 20160207521A1 · Ogawa · 2016 [cited by examiner]
US 20160221567A1 · Ogawa · 2016 [cited by examiner]
US 20160362096A1 · Nikovski · 2016 [cited by examiner]
US 20170036663A1 · Kim · 2017 [cited by examiner]
US 20170096134A1 · Yoon · 2017 [cited by applicant]
US 20190001957A1 · Park · 2019 [cited by examiner]
US 20190126907A1 · Park · 2019 [cited by applicant]
US 20190389451A1 · Huang · 2019 [cited by examiner]
US 20210101582A1 · Lee · 2021 [cited by examiner]
US 20220144241A1 · Ortmann · 2022 [cited by examiner]
US 20220242390A1 · Li · 2022 [cited by examiner]
US 20230009058A1 · Huh · 2023 [cited by examiner]
US 20230036756A1 · Ogawa · 2023 [cited by examiner]
US 20230227019A1 · Ogawa · 2023 [cited by examiner]
US 20230339451A1 · Yang · 2023 [cited by examiner]
US 20240375637A1 · Nelson · 2024 [cited by examiner]
US 20250269856A1 · Gesang · 2025 [cited by examiner]
CN 111959490A · 2020 [cited by applicant]
JP 2012001168A · 2012 [cited by applicant]
JP 2015113075A · 2015 [cited by applicant]
KR 101713734B1 · 2017 [cited by applicant]
KR 20190030011A · 2019 [cited by applicant]
KR 20190049143A · 2019 [cited by applicant]
KR 20190081379A · 2019 [cited by applicant]
KR 20200099642A · 2020 [cited by applicant]
KR 20210065552A · 2021 [cited by applicant]