AUTOMATIC CONTROL OF HEATING AND COOLING OF A VEHICLE SEATING ASSEMBLY PURSUANT TO PREDICTIVE MODELING THAT RECALIBRATES BASED ON OCCUPANT MANUAL CONTROL

• US 20190176568A1
• Filed: 12/08/2017
• Published: 06/13/2019
• Est. Priority Date: 12/08/2017
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Ostrowski, David, Manwell, Elizabeth Anne, Herbert, William G., John, Mohan
• Granted Patent: US 10,518,602 B2
• Time in Patent Office: Days
• CPC Class Codes: B60H 1/00285 for vehicle seats vehicle seats with heating or ventilation means independent from the HVAC system of the vehicle B60N2/56
• CPC Class Codes: B60H 1/0073 Control systems or circuits characterised by particular algorithms or computational models, e.g. fuzzy logic or dynamic models
• CPC Class Codes: B60H 1/00742 by detection of the vehicle occupants' presence; by detection of conditions relating to the body of occupants, e.g. using radiant heat detectors
• CPC Class Codes: B60H 1/00764 the input being a vehicle driving condition, e.g. speed B60H1/00828, B60H1/00864 take precedence
• CPC Class Codes: B60H 1/00807 the input being a specific way of measuring or calculating an air or coolant temperature
• CPC Class Codes: B60H 1/00878 the components being temperature regulating devices
• CPC Class Codes: B60H 1/00964 Control systems or circuits characterised by including features for automatic and non-automatic control, e.g. for changing from automatic to manual control
• CPC Class Codes: B60H 1/00985 Control systems or circuits characterised by display or indicating devices, e.g. voice simulators characterised by the input of sound using a voice synthesizer B60H1/00757; B60H1/00978 takes precedence
• CPC Class Codes: B60H 2001/224 automatic operation, e.g. control circuits or methods
• CPC Class Codes: B60H 2001/2246 obtaining information from a variable, e.g. by means of a sensor
• CPC Class Codes: B60H 2001/3255 related to temperature
• CPC Class Codes: B60K 2360/11 Instrument graphical user interfaces or menu aspects
• CPC Class Codes: B60K 35/10 Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor
• CPC Class Codes: B60N 2/56 Heating or ventilating devices
• CPC Class Codes: B60N 2/5678 characterised by electrical systems
• CPC Class Codes: B60R 21/0154 in combination with seat heating
• CPC Class Codes: B60Y 2400/302 Temperature sensors
• CPC Class Codes: G06F 17/18 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 Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 5/01 : Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/025 : Extracting rules from data
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks

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.

Claims

1. A method of controlling a temperature altering element within a seating assembly of a vehicle comprising:

presenting a vehicle comprising;

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 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 initially automatically activate the temperature altering element; and

automatically activating the temperature altering element.

2. The method of claim 1,

wherein, the Pre-established Predictive Activation Model was formed pursuant to a classification and regression tree analysis of input data related to the Certain Identifiable Conditions collected from other operators of other vehicles.

3. The method of claim 2,

wherein, the Pre-established Predictive Activation Model establishes rules that are a function of at least the following Certain Identifiable Conditions:

ambient temperature;

temperature set point for an interior of the vehicle;

time of day;

whether the first occupant has requested the vehicle to heat the interior with a blower at a certain blower speed;

temperature of the interior of the vehicle; and

temperature differential between the ambient temperature and the in-vehicle temperature.

4. The method of claim 2,

wherein, the Pre-established Predictive Activation Model establishes rules that are a function of at least the following Certain Identifiable Conditions:

whether windshield wipers have been activated;

whether air conditioning has been activated;

temperature set point for an interior of the vehicle;

ambient temperature;

level at which a climate control system in the vehicle is blowing air;

engine speed;

vehicle speed; and

in-vehicle temperature.

5. The method of claim 2,

wherein, the Pre-established Predictive Activation Model establishes rules that are a function of at least the following Certain Identifiable Conditions:

in-vehicle temperature;

ambient temperature;

level at which a climate control system in the vehicle is blowing air;

whether a rear window defrost has been activated;

vehicle speed;

whether air conditioning has been activated;

engine speed; and

whether windshield wipers have been activated.

6. The method of claim 3,

wherein, when the ambient temperature is greater than a certain temperature, the controller automatically activates the temperature altering element pursuant to the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Conditions excluding the ambient temperature;

and

wherein, when the ambient temperature is less than the certain temperature, the controller automatically activates the temperature altering element pursuant to the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Conditions excluding the ambient temperature.

7. The method of claim 4,

wherein, when windshield wipers have been activated, the controller does not automatically activate the temperature altering element pursuant to the Pre-established Predictive Activation Model;

and

wherein, when windshield wipers have not been activated, the controller automatically activates the temperature altering element pursuant to the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Conditions excluding whether windshield wipers have been activated.

8. The method of claim 5,

wherein, when in-vehicle temperature is less than a certain temperature, the controller does not automatically activate the temperature altering element to impart cooling pursuant to the Pre-established Predictive Activation Model;

and

wherein, when in-vehicle temperature is greater than the certain temperature, the controller automatically activates the temperature altering element to impart cooling pursuant to the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Condition excluding in-vehicle temperature.

9. The method of claim 2,

wherein, the Pre-established Predictive Activation Model establishes rules for activation of the temperature altering element to provide cooling and the rules are a function of data related to at least the following Certain Identifiable Conditions:

ambient temperature;

in-vehicle temperature;

whether a rear window defrost has been activated; and

a temperature set point for an interior of the vehicle;

wherein, when the ambient temperature is less than a certain temperature and the in-vehicle temperature is greater than another certain temperature, the controller automatically activates the temperature altering element to impart cooling pursuant to the rules of the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Conditions including vehicle speed;

wherein, when the ambient temperature is greater than the certain temperature, the controller automatically activates the temperature altering element to impart cooling pursuant to the rules of the Pre-established Predictive Activation Model as a function of data relating to at least one other of the Certain Identifiable Conditions.

10. The method of claim 2 further comprising:

automatically deactivating the temperature altering element pursuant to the Pre-established Predictive Activation Model, after initially automatically activating the temperature altering element pursuant to the Pre-established Predictive Activation Model, if the collected data relating to the Certain Identifiable Conditions collected after initially automatically activating the temperature altering element satisfy the rules of the Pre-established Predictive Activation Model for deactivation of the temperature altering element.

11. The method of claim 10 further comprising:

automatically reactivating the temperature altering element pursuant to the Pre-established Predictive Activation Model, after automatically deactivating the temperature altering element pursuant to the Pre-established Predictive Activation Model, if the collected data relating to the Certain Identifiable Conditions collected after deactivating the temperature altering element again satisfy the rules for activation pursuant to the Pre-established Predictive Activation Model.

12. The method of claim 11 further comprising:

the first occupant of the seating assembly manually deactivating the temperature altering element via the user interface.

13. The method of claim 12 further comprising:

upon the first occupant manually deactivating the temperature altering element via the user interface, recalibrating the Pre-established Predictive Activation Model into a new predictive activation model accounting for the collected data relative to the Certain Identifiable Conditions existing when the occupant manually deactivated the temperature altering element and establishing new rules for activation and/or deactivation of the temperature altering element.

14. The method of claim 13 further comprising:

the first occupant manually activating the temperature altering element via the user interface.

15. The method of claim 14 further comprising:

upon the first occupant manually activating the temperature altering element via the user interface, recalibrating the new predictive activation model into a newer predictive activation model accounting for the collected data relative to the Certain Identifiable Conditions existing when the occupant manually activated the temperature altering element and establishing new rules for activation and/or deactivation of the temperature altering element.

16. The method of claim 15,

the temperature altering element being adjustable to several different levels of temperature altering;

the controller further including a Pre-established Predictive Level Model establishing rules governing which level of the several different levels of temperature altering the controller will initially automatically set for the temperature altering element, the rules of the Pre-established Predictive Level Model being a function of one or more of the Certain Identifiable Conditions; and

the user interface further configured to allow the first occupant to select manually the level of the several different levels of temperature altering;

the method further comprising;

determining, by comparing the collected data to the rules of Pre-established Predictive Level Model, which level of the several different levels of temperature altering the controller will initially automatically set for the temperature altering element; and

initially automatically setting the temperature altering element to the determined level.

17. The method of claim 16,

wherein, the Pre-established Predictive Level Model is formed pursuant to a multilayer perceptron classifier analysis of input data relating to the Certain Identifiable Conditions collected from other vehicles.

18. The method of claim 17 further comprising:

the first occupant of the seating assembly manually changing the level of temperature altering for the temperature altering element via the user interface.

19. The method of claim 18 further comprising:

upon the first occupant manually changing the level of temperature altering for the temperature altering element via the user interface, recalibrating the Pre-established Predictive Level Model into a new predictive level model accounting for the collected data relative to the Certain Identifiable Conditions existing when the occupant manually changed the level of temperature altering and establishing new rules governing the level of temperature altering for the temperature altering element when the temperature altering element is automatically activated;

automatically deactivating the temperature altering element;

automatically reactivating the temperature altering element;

determining, by comparing the collected data to the rules of the new predictive level model, which level of the several different levels of temperature altering the controller will initially automatically set for the temperature altering element; and

automatically setting the temperature altering element to the determined level.

20. The method of claim 19 further comprising:

removing the first occupant from the seating assembly;

occupying the seating assembly with a second occupant;

recognizing that the second occupant is different than the occupant;

collecting data relative to the identifiable conditions while the second occupant is occupying the seating assembly;

determining, by comparing the only the data collected while the second occupant is occupying the seating assembly, and not the data collected with the occupant was occupying the seating assembly, to the rules of the Pre-established Predictive Activation Model, whether data collected satisfy the rules of the Pre-established Predictive Activation Model so as to initially automatically activate the temperature altering element; and

initially automatically activating the temperature altering element while the second occupant is occupying the seating assembly.