Method and system for vehicle stop/start control

{
  "publication_number": "US2020191069A1",
  "country": "US",
  "kind": "A1",
  "title": "Method and system for vehicle stop/start control",
  "abstract": "Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption map that provide input to nonlinear model predictive controller.",
  "claims": [
    "1. An engine operating method, comprising: adjusting an estimated vehicle coasting duration via a controller based on responses of a peer group of human drivers; and automatically stopping an internal combustion engine via the controller responsive to the estimated vehicle coasting duration. 2. The method of claim 1, further comprising adjusting the estimated vehicle coasting duration responsive to severity of environmental conditions including at least one of ambient air density, humidity, rain, dust, hail, snow, and insects. 3. The method of claim 1, further comprising adjusting the estimated vehicle coasting duration responsive to a capacity of a distance to object sensing system to detect an object at a predetermined distance. 4. The method of claim 1, further comprising additionally adjusting the estimated vehicle coasting duration based on responses of a sub-peer group of human drivers. 5. The method of claim 1, where the peer group of drivers includes a plurality of human drivers and further comprising: consolidating the responses of the peer group of human drivers within a central server. 6. The method of claim 1, further comprising additionally adjusting the estimated vehicle coasting duration based on responses of a sub-peer group of human drivers. 7. The method of claim 6, further comprising adjusting a machine learning model responsive to a capacity of a distance to object sensing system to detect an object at a predetermined distance and adjusting the estimated vehicle coasting duration via the machine learning model. 8. An engine operating method, comprising: assigning a machine learning model to a peer group of drivers; adjusting an estimated vehicle coasting duration based on responses of the peer group of human drivers; and automatically stopping an internal combustion engine responsive to the estimated vehicle coasting duration. 9. The method of claim 8, further comprising overriding automatically stopping the internal combustion engine responsive to the estimated vehicle coasting duration. 10. The method of claim 9, further comprising automatically stopping the internal combustion engine responsive to pedal input provided via a driver after overriding automatically stopping the internal combustion engine responsive to the estimated vehicle coasting duration. 11. The method of claim 8, further comprising additionally adjusting the estimated vehicle coasting duration responsive to a change in capacity to detect an object via a distance to object sensing system due to environmental conditions. 12. The method of claim 11, where the estimated vehicle coasting duration is decreased in response to a decrease in the capacity to detect the object. 13. The method of claim 11, where the estimated vehicle coasting duration is increased in response to a decrease in the capacity to detect the object. 14. The method of claim 8, where automatically stopping the internal combustion engine includes ceasing to rotate the internal combustion engine while a vehicle in which the internal combustion engine resides is coasting. 15. An engine control system, comprising: an internal combustion engine; a distance to object sensing system that transmits a signal and receives a reflected version of the signal; and a controller including executable instructions stored in non-transitory memory to estimate a vehicle coasting duration and stop the internal combustion engine based on the vehicle coasting duration, the vehicle coasting duration estimated based on vehicle coasting durations of a predetermined peer group of human drivers, the vehicle coasting duration a function of responses of members of the predetermined peer groups during conditions where performance of the distance to object sensing system is degraded due to environmental conditions. 16. The vehicle system of claim 15, where the environmental conditions include at least one of ambient air density, humidity, rain, dust, hail, snow, and insects. 17. The vehicle system of claim 15, where the predetermined peer group of human drivers are human drivers of a specific vehicle make and model. 18. The vehicle system of claim 15, where the predetermined peer group of human drivers are human drivers of a specific age group. 19. The vehicle system of claim 15, where the predetermined peer group of human drivers in a specific geographical region. 20. The vehicle system of claim 15, further comprising additional instructions to adjust a beginning time or location for the vehicle coasting duration responsive to a change in the distance to object sensing system's capacity to detect an object at a predetermined distance."
  ],
  "cpc": [
    "F02D 29/02",
    "B60W 10/06",
    "B60W 2050/0043",
    "B60W 2540/00",
    "B60W 2555/20",
    "B60W 2556/10",
    "B60W 2710/06",
    "B60W 30/18054",
    "B60W 30/18072",
    "B60W 40/10",
    "B60W 50/0098",
    "F02D 2041/1412",
    "F02D 2200/60",
    "F02D 2200/70",
    "F02D 2200/701",
    "F02D 41/2451",
    "F02N 11/0822",
    "F02N 11/0837",
    "F02N 2200/105",
    "F02N 2200/12",
    "F02N 2200/122",
    "F02N 2200/125",
    "F02N 2300/2008",
    "Y02T 10/40",
    "Y02T 10/60",
    "Y02T 10/84"
  ],
  "assignees": [
    "FORD GLOBAL TECH LLC"
  ],
  "filing_date": "2018-12-17",
  "publication_date": "2020-06-18",
  "priority_date": "2018-12-17",
  "application_number": "US-201816222653-A",
  "family_id": "70858906"
}