Estimating and predicting fuel usage with smartphone

Patent metadata

Field	Value
Publication number	US-2016123752-A1
Application number	US-201414529100-A
Country	US
Kind code	A1
Filing date	Oct 30, 2014
Priority date	Oct 30, 2014
Publication date	May 5, 2016
Grant date	—

Abstract

Examples are disclosed herein that relate to estimating and predicting vehicular fuel use. One example estimates fuel usage by a vehicle during a trip by obtaining sensor measurements from one or more sensors of a mobile computing device during the trip, determining a plurality of trip features from the sensor measurements, each trip feature representing an aspect of one or more of energy produced and energy consumed during the trip, obtaining vehicle-specific parameters of the vehicle, and determining an estimated fuel usage from the vehicle-specific parameters and the plurality of trip features for output by the computing device.

First claim

1 . On a computing device, a method for predicting fuel usage for a vehicle, the method comprising:
obtaining map information for a trip having a route; obtaining road characteristic information for the route from the map information, the road characteristic information defining road features for the route; obtaining a plurality of trip features from the map information and road characteristic information, the plurality of trip features comprising effects of the road features on a vehicle traveling the route; obtaining vehicle-specific parameters for the vehicle; determining a predicted fuel usage for the trip from the vehicle-specific parameters and the plurality of trip features; and outputting the predicted fuel usage.

2 . The method of claim 1 , wherein obtaining road characteristic information comprises obtaining the road features for each road segment of a plurality of road segments for the route and obtaining the plurality of trip features for each road segment.

3 . The method of claim 1 , further comprising, for each of the plurality of trip features, updating a model relating the road features to each trip feature.

4 . The method of claim 1 , wherein the road features comprise one or more of a road length, a road speed limit, a road grade, a road rolling resistance, a road change in potential energy, a road aerodynamic drag, rush hour velocity multipliers, and a number of stops.

5 . The method of claim 1 , wherein the plurality of trip features comprises one or more of an energy from burning fuel, an energy generated by an engine of the vehicle, a change in kinetic energy, a change in potential energy, an aerodynamic drag, a rolling resistance, and a standby energy.

6 . The method of claim 1 , wherein the vehicle-specific parameters comprise one or more of a vehicle mass, a frontal effective area, and an efficiency of an engine of the vehicle.

7 . The method of claim 1 , further comprising obtaining vehicle-specific parameters from vehicle specifications.

8 . A machine-readable storage device comprising instructions executable by a mobile computing device to estimate fuel usage by a vehicle during a trip by:
obtaining sensor measurements from one or more sensors of the mobile computing device during the trip, determining a plurality of trip features from the sensor measurements, each trip feature representing an aspect of one or more of energy produced and energy consumed during the trip; obtaining vehicle-specific parameters of the vehicle; determining an estimated fuel usage from the vehicle-specific parameters and the plurality of trip features; outputting the estimated fuel usage; and presenting feedback relating driving behaviors of a driver of the trip to the estimated fuel usage for the trip.

9 . The device of claim 8 , wherein the instructions executable to determine the plurality of trip features are executable to
segment the sensor measurements over time into epochs, wherein a duration of each epoch is sufficient to include a plurality of sensor measurements from each sensor; sum over the sensor measurements from each sensor for each epoch, wherein the sum is performed based upon a fixed rate change between consecutive sensor measurements for each sensor, and determine the plurality of trip features for each epoch based upon the sum.

10 . The device of claim 9 , wherein the instructions executable to determine the plurality of trip features are executable to omit epochs having sensor measurements for less than a threshold fraction of the epoch and to omit epochs having a time difference between two of the consecutive sensor measurements larger than a threshold time.

11 . The device of claim 8 , wherein the plurality of trip features comprises one or more of an energy from burning fuel, an energy generated by an engine of the vehicle, a change in kinetic energy, a change in potential energy, an aerodynamic drag, a rolling resistance, and a standby energy.

12 . The device of claim 8 , wherein the sensor measurements comprise one or more of a vehicular speed, a location of the vehicle, a slope of a road segment of the trip, a fuel injection rate, an engine revolutions-per-minute speed, and a torque.

13 . The device of claim 8 , wherein the vehicle-specific parameters comprise one or more of a vehicle mass, a frontal effective area, and an efficiency of an engine of the vehicle.

14 . The device of claim 8 , wherein the instructions executable to obtain the vehicle-specific parameters are executable to learn the vehicle-specific parameters from a relationship between the plurality of trip features and the estimated fuel usage.

15 . The device of claim 8 , wherein the instructions executable to present feedback are executable to present comparative feedback comprising the feedback for the driver compared to additional feedback for a second driver, the additional feedback comprising relation of driving behavior of the second driver to a second estimated fuel usage for the second driver.

16 . A mobile computing device, comprising:
a logic subsystem; a data-holding subsystem comprising instructions executable by the logic subsystem to estimate fuel usage by a vehicle during a trip by
obtaining sensor measurements from one or more sensors of the mobile computing device during the trip,
determining a plurality of trip features from the sensor measurements, each trip feature representing an aspect of one or more of energy produced and energy consumed during the trip;
obtaining vehicle-specific parameters of the vehicle;
determining an estimated fuel usage from the vehicle-specific parameters and the plurality of trip features; and
receiving from an on-board diagnostics device information related to a current energy generated by an engine of the vehicle;
determining an estimated fuel usage from the vehicle-specific parameters and the plurality of trip features;
outputting the estimated fuel usage; and
presenting feedback relating driving behaviors of a driver of the trip to the estimated fuel usage for the trip.

17 . The mobile computing device of claim 16 , further comprising instructions executable by the logic subsystem to obtain the sensor measurements from the on-board diagnostics device and to update a function of the mobile computing device to learn the on-board diagnostics device information based upon the sensor measurements obtained from the on-board diagnostics device.

18 . The mobile computing device of claim 16 , wherein the on-board diagnostics device information comprises one or more of a fuel injection rate, a vehicular speed, an engine revolutions-per-minute speed, and a torque.

19 . The mobile computing device of claim 16 , wherein the plurality of trip features comprises one or more of an energy from burning fuel, an energy generated by an engine of the vehicle, a change in kinetic energy, a change in potential energy, an aerodynamic drag, a rolling resistance, and a standby energy.

20 . The mobile computing device of claim 16 , wherein the instructions executable to present feedback are executable to present comparative feedback comprising the feedback for the driver compared to additional feedback for a second driver, the additional feedback comprising relation of driving behavior of the second driver to a second estimated fuel usage for the second driver.

Assignees

• Microsoft Corp

Inventors

• Kandula Srikanth
• Shoaib Mohammed
• Bahl Paramvir
• Patro Ashish

Classifications

• G01C21/3469 Primary

  Fuel consumption; Energy use; Emission aspects · CPC title