Hybrid electric vehicle and driving mode control method for the same
US-2019202438-A1 · Jul 4, 2019 · US
US10906553B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10906553-B2 |
| Application number | US-201816048956-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jul 30, 2018 |
| Priority date | Jul 30, 2018 |
| Publication date | Feb 2, 2021 |
| Grant date | Feb 2, 2021 |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
Methods and systems for inhibiting an acceleration event prediction includes determining a current vehicle operating condition. An acceleration event is predicted based on a plurality of stored predictions that match the current vehicle operating condition. A determination is made whether to inhibit the acceleration event prediction. The acceleration event prediction is permitted to modify an acceleration event powertrain control such that a powertrain control occurs. A driver noncompliance with the acceleration event powertrain control is stored as a machine learning data and the current vehicle operating condition is stored as machine learning data upon the driver noncompliance. The stored machine learning data is used to determine whether to inhibit a future acceleration event prediction.
Opening claim text (preview).
What is claimed is: 1. A method of controlling a powertrain of a vehicle, comprising: determining a current vehicle operating condition of the vehicle; predicting an acceleration event based on a plurality of stored predictions and the current vehicle operating condition; and inhibiting a prediction of the acceleration event so to prevent the acceleration event prediction from influencing a powertrain control of the vehicle, wherein inhibiting the prediction of the acceleration event is determined by a plurality of learned data based on a driver operation of the vehicle, and wherein the learned data is from determining a driver noncompliance with the acceleration event prediction based on the driver operation of the vehicle. 2. The method of claim 1 , wherein the learned data is from the current vehicle operating condition of the vehicle during a driver noncompliance with the acceleration event prediction based on the driver operation of the vehicle. 3. A method of controlling a powertrain of a vehicle, comprising: determining a current vehicle operating condition of a vehicle; predicting an acceleration event based on a plurality of stored predictions and the current vehicle operating condition; determining to not inhibit the acceleration event such that the acceleration event prediction is permitted to influence a powertrain control of the vehicle; determining a driver noncompliance with the acceleration event prediction; storing the driver noncompliance with the acceleration event prediction based on the driver operation of the vehicle; and storing the current vehicle operating condition during the driver noncompliance with the acceleration event prediction, wherein the determination to not inhibit the acceleration event is determined by a plurality of learned data based on a driver operation of the vehicle, and wherein the driver noncompliance with the acceleration event prediction is used as learning data to identify conditions to inhibit a plurality of future acceleration event predictions. 4. The method of claim 3 , wherein the current vehicle conditions upon the driver noncompliance with the acceleration event prediction is used as learned data to identify conditions to inhibit a plurality of future acceleration event predictions. 5. The method of claim 3 , wherein the current vehicle operation condition comprises a vehicle location data, a map information data, a traffic conditions data, a weather conditions data, and a driver reactions data. 6. The method of claim 3 , wherein the driver noncompliance is determined by monitoring the current vehicle operation and a plurality of driver inputs data. 7. The method of claim 6 , wherein the plurality of driver inputs data comprise a rate of acceleration, a rate of deceleration, a plurality of driver tendencies and habits, and a driver response to the current vehicle operation conditions. 8. A vehicle comprising: an engine; a powertrain operably coupled to the engine; and a processor, communicatively coupled to the vehicle powertrain; a non-transitory, processor-readable storage medium, the non-transitory, processor readable storage medium comprising one or more programming instructions thereon that, when executed, cause the processing device to: determine a current vehicle operating condition of a vehicle; predict an acceleration event based on a plurality of stored predictions and the current vehicle operating condition; determine to not inhibit the acceleration event based on a plurality of learned data associated with a driver operation of the vehicle, such that the acceleration event prediction is permitted to influence a powertrain control of the vehicle; determine a driver noncompliance with the acceleration event prediction; store the driver noncompliance with the acceleration event prediction based on the driver operation of the vehicle; and store the current vehicle operating condition during the driver noncompliance with the acceleration event prediction, wherein the driver noncompliance with the acceleration event prediction is used as learning data to identify conditions to inhibit a plurality of future acceleration event predictions. 9. The vehicle of claim 8 , wherein the current vehicle conditions upon the driver noncompliance with the acceleration event prediction is used as learned data to identify conditions to inhibit a plurality of future acceleration event predictions. 10. The vehicle of claim 8 , wherein the current vehicle operation condition comprises a vehicle location data, a map information data, a traffic conditions data, a weather conditions data, and a driver reactions data. 11. The vehicle of claim 8 , wherein the driver noncompliance is determined by monitoring the current vehicle operation and a plurality of driver inputs data. 12. The vehicle of claim 11 , wherein the plurality of driver inputs data comprise a rate of acceleration, a rate of deceleration, a plurality of driver tendencies and habits, and a driver response to the current vehicle operation conditions. 13. The vehicle of claim 8 , wherein the vehicle is a hybrid electric vehicle.
Historical data · CPC title
Automatic parameter input, automatic initialising or calibrating means · CPC title
Predicting future conditions · CPC title
Control strategies specially adapted for achieving a particular effect · CPC title
Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.