Adaptive control of automotive HVAC system using crowd-sourcing data

• US 10,272,737 B2
• Filed: 11/16/2017
• Issued: 04/30/2019
• Est. Priority Date: 01/14/2015
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Stanek, Joseph, Lockwood, John A., Davison, Mark, Palic, Jeffrey A., Scott, Lisa
• Primary Examiner(s): Marc-Coleman, Marthe Y
• Application Number:
  US15/814,777
• Publication Number:
  US 20180086178A1
• Time in Patent Office: 530 Days
• Field of Search: 701 36, 165202
• CPC Class Codes: B60H 1/00657 Remote control devices
• 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 1/00771 the input being a vehicle position or surrounding, e.g. GPS-based position or tunnel
• CPC Class Codes: B60H 1/00778 the input being a stationary vehicle position, e.g. parking or stopping
• CPC Class Codes: B60H 1/00785 by the detection of humidity or frost
• CPC Class Codes: B60H 1/00849 for selectively commanding the induction of outside or inside air B60H1/00842 takes precedence
• 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: F02N 11/0807 Remote means
• CPC Class Codes: F02N 11/12 Starting of engines by means of mobile, e.g. portable, starting sets
• CPC Class Codes: F02N 2300/306 with external senders or receivers, e.g. receiving signals from traffic lights, other vehicles or base stations
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

A motor vehicle comprises an HVAC system including a climate control circuit coupled to onboard sensors, a human-machine interface, and climate actuators. The actuators are responsive to respective command parameters generated by the control circuit in response to the sensors and the human-machine interface. A wireless communication system transmits vehicle HVAC data to and receives crowd data from a remote server. The control circuit initiates a request for crowd data via the communication system to the remote server, wherein the request includes peer parameters for identifying a vehicle environment. The control circuit receives a response via the communication system from the remote server. The response comprises crowd data and at least one weight indicating a confidence level associated with the crowd data. The control circuit generates at least one command parameter using a set of fuzzy rules responsive to the crowd data and the weight from the response.

Claims

1. A method of adaptive climate control system operation in a vehicle, comprising the steps of:

sensing climate conditions and control settings in the vehicle;

adjusting command parameters of the climate control system according to the sensed climate conditions and control settings;

sending a cloud request to a remote server including peer environmental parameters associated with the vehicle;

receiving a response from the remote server;

parsing the response into crowd data and weights that indicate a confidence level of the crowd data; and

applying the crowd data and weights to a fuzzy rule set to further adjust the command parameters, wherein the fuzzy rule set is further responsive to the sensed climate conditions.

2. The method of claim 1 further comprising the step of periodically sending the sensed climate conditions, control settings, and peer parameters to the remote server for inclusion in a database.

3. The method of claim 1 wherein sending the cloud request is triggered by a preconditioning event in the vehicle.

4. The method of claim 1 wherein the peer parameters include geographic coordinates.

5. The method of claim 1 wherein the peer environmental parameters include a shelter identification.

6. The method of claim 1 wherein the peer environmental parameters include occupancy data comprising a number and seating positions of occupants in the vehicle.

7. The method of claim 1 wherein the occupancy data further comprises a personal identification of an occupant of the vehicle.

8. The method of claim 1 wherein the peer environmental parameters include identification of a vehicle type.

9. The method of claim 1 wherein the request identifies an actuator function to which the requested crowd data relates.

10. The method of claim 9 further comprising the step of detecting an ambient temperature, wherein the actuator function identified in the request is comprised of a defrost function when the detected ambient temperature is below a predetermined temperature.

11. The method of claim 10 wherein the crowd data for the defrost function includes a defrost setting of an air handling unit, operation of heated glass surfaces, and operation of window washers and wipers.

12. Apparatus comprising:

a data collection server receiving real-time climate data and peer parameters from a vehicle fleet;

a database storing climate data sorted according to peer environmental parameters;

a request agent receiving user requests having corresponding peer environmental parameters;

a data selector retrieving relevant crowd data according to the request and weighting crowd data according to similarity of peer environmental parameters;

wherein the request agent transmits the crowd data and weights to the user.

13. The apparatus of claim 12 wherein the climate data includes climate conditions sensed by respective vehicles in the fleet and operation states of climate control systems activated within the respective vehicles, and wherein the peer environmental parameters include location data, occupancy data, and vehicle type for the respective vehicles.

14. The apparatus of claim 12 wherein weighting of the relevant crowd data is further responsive to a statistical sample size.

15. The apparatus of claim 12 wherein the data collection server further receives regional weather data from a source independent of the vehicle fleet for inclusion with the crowd data.