FACTOR COST TIME SERIES TO OPTIMIZE DRIVERS AND VEHICLES: METHOD AND APPARATUS

• US 20140107912A1
• Filed: 02/28/2013
• Published: 04/17/2014
• Est. Priority Date: 10/17/2012
• Status: Abandoned Application

• Current Assignee: Certusoft, Inc.
• Original Assignee: Certusoft, Inc.
• Inventors: Yucel, Sermet, Magnuson, Jon M., Martin, Samuel E., Moran, Melinda G., Paterlini, Maria G., Headrick, William H.
• Application Number:
  US13/779,923
• Publication Number:
  US 20140107912A1
• Time in Patent Office: Days
• US Class Current: 701/123
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/085 using electronic data carriers

Abstract

A method and system for analyzing and improving driver and vehicle performance are described. Detailed vehicle data, including high frequency time series data, which was collected during a trip, is obtained, as well as external data regarding trip route and environment. Using the data and a model of the physics of the vehicle, driver and vehicle time series may be obtained for the trip. These time series may allocate fuel consumption to various factor costs relating to the driver (e.g., rate of acceleration, choice of gear) and to the vehicle (e.g., choice of engine, aerodynamic improvements). From trip simulations run with virtual drivers, an optimal (relative to some criterion) virtual driver (i.e., control choices) can be obtained. Simulations with the optimal driver can find an optimal vehicle from a set of virtual vehicles. Losses due to driver behavior and to vehicle configuration can be computed by comparisons, and alternatives suggested.

Claims

1. A method, comprising:

a) from tangible storage or through a physical interface, obtaining data that includes

(i) settings of controls of a vehicle at a sequence of points along a road route, and

(ii) estimates of force and/or torque transfers between internal components of the vehicle at the sequence of points along the route;

b) using the data and a model of processes that govern physics of motion of the vehicle, allocating, at the sequence of points, costs of operating the vehicle to a plurality of factor costs, wherein a factor cost can be

(i) a driver factor cost, which corresponds to a category of control setting choices made by the driver along the route, or

(ii) a vehicle factor cost, which corresponds to an aspect of configuration of the vehicle.

2. The method of claim 1, further comprising:

c) estimating at least one of the force and/or torque transfers using the model of processes that govern physics of motion of the vehicle.

3. The method of claim 1, wherein the estimates, of force and/or torque transfers includes an estimate that pertains to a transmission and an estimate that pertains to an engine of the vehicle.

4. The method of claim 1, wherein a given factor cost is estimated at a plurality of points along the route.

5. The method of claim 1, wherein a cost of operating the vehicle at or in a neighborhood a point along the route is allocated among a plurality of factor costs that were each estimated at or in a neighborhood the point.

6. The method of claim 1, wherein a total cost of operating the vehicle over the route is allocated among a plurality of factor costs that were each estimated at a plurality of points along the route.

7. The method of claim 1, further comprising:

c) executing a solution method that seeks a sequence of control settings at points along the route to optimize some criterion relating to cost of operating the vehicle; and

d) using settings of controls indicated by the solution method in part (i) of step a.

8. The method of claim 7, where the solution method is a genetic algorithm or swarm optimization.

9. The method of claim 8, where the solution method includes smoothing speed of the vehicle at points along the route.

10. The method of claim 8, where the solution method utilizes an optimization space that bounds speed of a vehicle at points along the route.

11. The method of claim 1, wherein a factor cost is based, at least in part, upon fuel consumption.

12. The method of claim 1, wherein a factor cost is based, at least in part, upon trip duration.

13. The method of claim 1, wherein a factor cost is based, at least in part, upon wear on the vehicle.

14. A method, comprising:

a) in a simulation executed on a digital processing system, selecting control settings, which represent choices made by a driver of a vehicle, at route points during a road trip;

b) from tangible storage, accessing

(i) a model of the physical processes governing motion of the vehicle during the trip, wherein the model incorporates data obtained by monitoring components of power trains of similarly configured vehicles during actual road trips; and

(ii) data characterizing the power train of the vehicle;

c) using the model and the data, estimating transfers, which relate to vehicle propulsion, between internal components of the vehicle at route points; and

d) based on the estimated transfers,

(i) estimating progress of the vehicle under control of the driver, and

(ii) at route points, allocating costs of operating the vehicle to a plurality of factor costs, wherein a factor cost can be

(i) a driver factor cost, which corresponds to a category of control setting choices made by the driver along the route, or

(ii) a vehicle factor cost, which corresponds to an aspect of configuration of the vehicle.

15. The method of claim 14, further comprising:

e) applying steps a through c to a first virtual driver, the first virtual driver corresponding to a first set of control settings;

f) applying steps a through c to a second virtual driver, the second virtual driver corresponding to a second set of control settings;

g) comparing the driver factor costs of the first virtual driver with the driver factor costs of the second virtual driver.

16. The method of claim 14, further comprising:

e) applying steps a through c to each virtual driver in a first candidate solution set that includes a plurality of virtual drivers, each virtual driver corresponding to a respective set of control settings;

f) based on the results of step d, updating the first candidate solution set to create a second candidate solution set, wherein the second candidate solution set contains a virtual driver that replaces a counterpart in the first candidate solution set; and

g) replacing the first candidate solution set with the second candidate solution set, and repeating steps e and f.

17. The method of claim 16, wherein the replacement driver exhibits a lower driver factor cost than its counterpart.

18. The method of claim 17, further comprising:

h) repeating steps e through g until an optimal virtual driver is converged upon.

19. The method of claim 14, further comprising:

e) selecting an optimal virtual driver using comparisons of respective driver factor costs for a plurality of virtual drivers.

20. The method of claim 19, further comprising:

f) comparing driver factor costs corresponding to a human driver with factor costs corresponding to the optimal virtual driver.

21. The method of claim 20, further comprising:

g) based on the comparison, transmitting through a hardware interface a suggestion for a technique to reduce driver factor costs for the human driver.

22. The method of claim 14, further comprising:

e) using control settings of a given driver and characteristics of a first vehicle, applying steps a through d;

f) using control settings of the given driver and characteristics of a second vehicle, applying steps a through d;

g) comparing the vehicle factor costs of the first vehicle with the vehicle factor costs of the second vehicle.

23. The method of claim 22, wherein the given driver is a virtual driver selected by an optimization process.

24. The method of claim 22, further comprising:

h) based on the comparison, transmitting through a hardware interface a suggestion for a modification to the first vehicle.

25. The method of claim 22, further comprising:

h) based on the comparison, transmitting through a hardware interface a suggestion that the second vehicle be used to drive the route instead of the first vehicle.

26. A system, comprising:

a) tangible digital storage, including

(i) time series data, received from a monitoring system onboard a vehicle, the data including

(A) settings of vehicle controls, as selected by a driver over a route,

(B) status of a plurality of power train components,

(C) rate of fuel consumption,

(D) speed of the vehicle, and

(E) location of the vehicle

(ii) logic that models physical processes of the vehicle;

b) a processing system, including an electronic digital processor, that uses the logic and the data to estimate time series of a set of forces and/or torques acting on a plurality of components internal to the vehicle.

27. The system of claim 26, further comprising:

c) an interface that includes a hardware component, through which the system receives the time series data.

28. The system of claim 26, further comprising:

c) an interface that includes a hardware component, through which the system receives environmental and/or route information.

29. The system of claim 26, further comprising:

c) an interface that includes a hardware component, through which the system transmits information about comparisons of performance factor costs for vehicles and/or drivers.

30. The system of claim 26, further comprising:

c) a database containing attributes of a plurality of vehicle models and/or individuals vehicles; and

d) a database containing road properties along a plurality of routes.