Automatic control of heating and cooling of a vehicle seating assembly pursuant to predictive modeling that recalibrates based on occupant manual control

Patent metadata

Field	Value
Publication number	US-11634005-B2
Application number	US-202117307586-A
Country	US
Kind code	B2
Filing date	May 4, 2021
Priority date	Dec 8, 2017
Publication date	Apr 25, 2023
Grant date	Apr 25, 2023

Abstract

A method of controlling a temperature altering element within a seating assembly of a vehicle comprising: presenting a vehicle including a seating assembly including a temperature altering element, a controller in communication with the temperature altering element, the controller including a Pre-established Predictive Activation Model setting forth rules governing the activation of the temperature altering element as a function of data relating to Certain Identifiable Conditions, and a user interface configured to allow the temperature altering element to be manually activated or deactivated; occupying the seating assembly with a first occupant; collecting data relating to the Certain Identifiable Conditions while the first occupant is occupying the seating assembly; determining, by comparing the collected data to the rules of the Pre-established Predictive Activation Model, whether the collected data satisfies the rules of the Pre-established Predictive Activation Model so as to activate the temperature altering element; and activating the temperature altering element.

First claim

What is claimed is:

1. A method of controlling a temperature altering element within a seating assembly of a vehicle comprising:
collecting data relating to Certain Identifiable Conditions while an occupant is occupying the seating assembly of the vehicle, the seating assembly comprising the temperature altering element configured to impart heating or cooling to the occupant of the seating assembly within which the temperature altering element is disposed, wherein the vehicle further comprises: a controller in communication with the temperature altering element, the controller comprising a plurality of Pre-established Predictive Activation Models, each setting forth different rules governing activation of the temperature altering element as a function of data relating to the Certain Identifiable Conditions;
selecting one of the plurality of Pre-established Predictive Activation Models as a function of the data relating to the Certain Identifiable Conditions collected during the collecting step; and
automatically activating the temperature altering element when the data collected during the collecting step satisfies the rules of the Pre-established Predictive Activation Model that was selected during the selecting step;
wherein, selecting one of the plurality of Pre-established Predictive Activation Models as a function of the data relating to the Certain Identifiable Conditions collected during the collecting step comprises selecting one of the plurality of Pre-established Predictive Activation Models as a function of data relating to one or more of: (a) an average trip length; (b) a standard deviation of trip length; (c) an average number of trips per unit of time; (d) a number of trips shorter than a predetermined distance; (e) a distance driven on a highway; (f) a distance driven not on a highway; and (g) a ratio of (e) to (f).

2. The method of claim 1 , wherein
selecting one of the plurality of Pre-established Predictive Activation Models as a function of the data relating to the Certain Identifiable Conditions collected during the collecting step comprises selecting one of the plurality of Pre-established Predictive Activation Models as a function of data relating to the ratio of distance driven on a highway to the distance driven not on a highway.

3. The method of claim 1 , wherein
selecting one of the plurality of Pre-established Predictive Activation Models as a function of the data relating to the Certain Identifiable Conditions collected during the collecting step comprises selecting one of the plurality of Pre-established Predictive Activation Models as a function of data relating to one or more of: (a) torque; (b) vehicle speed; (c) engine revolutions per minute; (d) fuel economy; and (e) how often the vehicle moved without accelerating or decelerating through braking.

4. The method of claim 1 further comprising:
before selecting one of the plurality of Pre-established Predictive Activation Models as a function of the data relating to the Certain Identifiable Conditions collected during the collecting step, automatically activating the temperature altering element when the data relating to the Certain Identifiable Conditions collected during the collecting step satisfies the rules of a predetermined default Pre-established Predictive Activation Model for activation of the temperature altering element.

5. The method of claim 1 , wherein
each of the plurality of Pre-established Predictive Activation Models was generated from a different segmented portion of data from a larger data set generated from other vehicles.

6. The method of claim 5 , wherein
each of the plurality of Pre-established Predictive Activation Models was generated pursuant to a classification and regression tree analysis of the different segmented portion of data.

7. The method of claim 1 further comprising:
segmenting data from a larger data set generated from other vehicles into different segmented portions of the data; and
generating the plurality of Pre-established Predictive Activation Models, each different Pre-established Predictive Activation Model generated from one of the different segmented portions of the data.

8. The method of claim 7 , wherein
segmenting data from the larger data set generated from other vehicles comprises utilizing a k-means cluster algorithm to segment the data from the larger data set.

9. The method of claim 7 , wherein
each of the plurality of Pre-established Predictive Activation Models was generated as a function of data related to the Certain Identifiable Conditions existing when operators of the other vehicles manually activated a temperature altering element of a seating assembly upon which the operators were sitting via a user interface.

10. The method of claim 1 , wherein
the temperature altering element is configured to impart heating to the occupant of the seating assembly.

11. The method of claim 1 , wherein
the temperature altering element is configured to impart cooling to the occupant of the seating assembly.

12. The method of claim 1 , wherein
during the collecting step, it is determined that the occupant is primarily a highway driver; and
the Pre-established Predictive Activation Model chosen during the selecting step is a consequence of the occupant being determined to be primarily a highway driver.

13. The method of claim 1 , wherein
during the collecting step, it is determined that the occupant is primarily a city driver; and
the Pre-established Predictive Activation Model chosen during the selecting step is a consequence of the occupant being determined to be primarily a city driver.

14. A vehicle comprising:
a seating assembly;
a temperature altering element within the seating assembly, the temperature altering element being configured to impart heating or cooling to an occupant of the seating assembly within which the temperature altering element is disposed; and
a controller in communication with the temperature altering element and one or more data sources that generate data, the controller comprising a plurality of Pre-established Predictive Activation Models, each setting forth different rules governing activation of the temperature altering element as a function of data that the one or more data sources generate;
wherein, the controller determines which of the plurality of Pre-established Predictive Activation Models to utilize to govern activation of the temperature altering element also as a function of data that the one or more data sources generate; and
wherein, the controller determines which of the plurality of Pre-established Predictive Activation Models to utilize to govern activation of the temperature altering element as a function of data relating to one or more of: (a) an average trip length; (b) standard deviation of trip length; (c) an average number of trips per unit of time; (d) a number of trips shorter than a predetermined distance; (e) a distance driven on a highway; (f) a distance driven not on a highway; and (g) a ratio of (e) to (f).

15. The vehicle of claim 14 , wherein
the controller determines which of the plurality of Pre-established Predictive Activation Models to utilize to govern activation of the temperature altering element additionally as a function of data relating to one or more of: (a) torque; (b) vehicle speed; (c) engine revolutions per minute; (d) fuel economy; and (e) how often the vehicle moved without accelerating or decelerating through braking.

16. The vehicle of claim 14 , wherein
the plurality of Pre-established Predictive Activation Models was generated as a function of data ob

Assignees

• Ford Global Tech Llc

Inventors

• Ostrowski David
• Manwell Elizabeth Anne
• Herbert William G
• John Mohan

Classifications

• B60H1/00878

  the components being temperature regulating devices · CPC title

• B60H1/00742

  by detection of the vehicle occupants' presence; by detection of conditions relating to the body of occupants, e.g. using radiant heat detectors · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06N20/20

  Ensemble learning · CPC title

• B60N2/56

  Heating or ventilating devices · CPC title