Method and system for predicting energy consumption of a vehicle through application of a statistical model utilizing sensor and database data

Patent metadata

Field	Value
Publication number	US-2018113173-A1
Application number	US-201715793347-A
Country	US
Kind code	A1
Filing date	Oct 25, 2017
Priority date	Feb 27, 2014
Publication date	Apr 26, 2018
Grant date	—

Abstract

A method for predicting energy consumption of a vehicle using a statistical model. The method includes (i) predicting a set of future input vectors for the vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously generated at the defined time intervals at a plurality of previous points in time, (ii) predicting a change in the energy level of the vehicle at the future points in time using a processor and the statistical model, and (iii) providing results corresponding to the predicted change in the energy level to an output unit of the vehicle. The change in the energy level comprises a function of corresponding future input vectors and an associated weighting vector. The change in the energy level is predicted based on a regression analysis of the energy level associated with each of the reference input vectors.

First claim

1 . A method for predicting energy consumption of a vehicle using a statistical model, said method comprising:
predicting a set of future input vectors for said vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously generated at the defined time intervals at a plurality of previous points in time, wherein an energy level of said vehicle is associated with each reference input vector of said plurality of reference input vectors at each previous point in time of said plurality of previous points in time; predicting a change in said energy level of said vehicle at said future points in time using a processor and said statistical model, wherein (i) the change in said energy level comprises a function of corresponding future input vectors and an associated weighting vector, said weighting vector having been derived using said plurality of reference input vectors and associated energy levels at each point in time of said plurality of previous points in time, and representing an overall effect of each input vector on energy consumption of said vehicle, and (ii) said change in said energy level is predicted based on a regression analysis of said energy level associated with each of said reference input vectors; and providing results corresponding to the predicted change in said energy level to an output unit of said vehicle.

2 . The method according to claim 1 , wherein each reference input vector comprises a plurality of sensor data and a plurality of database data.

3 . The method according to claim 2 , wherein said plurality of sensor data is captured for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile at each point in time of said plurality of points in time.

4 . The method according to claim 2 , wherein said plurality of sensor data is obtained from a plurality of sensors associated with said vehicle.

5 . The method according to claim 4 , wherein said plurality of sensors correspond to at least one of a tire pressure sensor, a regenerative braking sensor, a battery capacity sensor, a battery charge sensor, a solar radiation sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, a motor temperature sensor, a lubrication level sensor, a wind resistance sensor, a proximity sensor, a weight sensor, an identity sensor, and a set of environmental sensors.

6 . The method according to claim 2 , wherein said plurality of database data is obtained for a plurality of vehicles and comprises data related to at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile.

7 . The method according to claim 6 , wherein said plurality of database data is obtained from a database storing previously recorded data for at least one of said vehicle location environment, said vehicle equipment profile, and said driver behavior profile corresponding to said plurality of vehicles.

8 . The method according to claim 2 , wherein said plurality of sensor data correspond to at least one of location data, time data, day data, solar radiation data, temperature data, humidity data, barometric pressure data, wind speed data, wind direction data, fuel level data, driving pattern data, and driver identity data associated with said vehicle and an environment around said vehicle.

9 . The method according to claim 2 , wherein said plurality of database data corresponds to at least one of weather data, route data, traffic data, and driving pattern data of a plurality of drivers.

10 . The method according to claim 1 , wherein said statistical model comprises at least one of a linear function, a quadratic function, a periodic function, and a rule based function of at least one of an energy level of the vehicle at each point in time, each vehicle input vector, and each database input vector for each defined time interval.

11 . A system for predicting energy consumption of a vehicle using a statistical model, said system comprising:
an acquisition module that obtains a plurality of input vectors at defined time intervals at a plurality of points in time; a processor that predicts (i) a set of future input vectors for said vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously obtained at the defined time intervals at a plurality of previous points in time, wherein an energy level of said vehicle is associated with each reference input vector of said plurality of reference input vectors at each previous point in time of said plurality of previous points in time, and (ii) a change in said energy level of said vehicle at said future points in time using said statistical model, wherein the change in said energy level comprises a function of corresponding future input vectors and an associated weighting vector, said weighting vector is derived using said plurality of reference input vectors and associated energy levels at each point in time of said plurality of previous points in time, said weighting vector represents an overall effect of each input vector on energy consumption of said vehicle, and said change in said energy level is predicted based on a regression analysis of said energy level associated with each of said reference input vectors; and an output unit that displays results corresponding to the predicted change in said energy level of said vehicle.

12 . The system according to claim 11 , wherein said acquisition module acquires a plurality of sensor data for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile at each point in time of said plurality of points in time, wherein said plurality of sensor data is obtained from a plurality of sensors coupled to said vehicle, wherein said plurality of database data is obtained for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile for a plurality of vehicles, and wherein said plurality of database data is obtained from a database storing previously recorded data for at least one of said vehicle location environment, said vehicle equipment profile, and said driver behavior profile corresponding to said plurality of vehicles.

13 . The system according to claim 12 , wherein said acquisition module acquires said plurality of sensor data from a plurality of sensors that correspond to at least one of a tire pressure sensor, a regenerative braking sensor, a battery capacity sensor, a battery charge sensor, a solar radiation sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, a motor temperature sensor, a lubrication level sensor, a wind resistance sensor, a proximity sensor, a weight sensor, an identity sensor, and a set of environmental sensors.

14 . The system according to claim 11 , wherein said acquisition module acquires said plurality of database data corresponding to at least one of weather data, route data, traffic data, and driving pattern data of a plurality of drivers.

15 . The system according to claim 11 , wherein said processor utilizes said statistical model comprising at least one of a linear function, a quadratic function, a periodic function, and a rule based function of at least one of a stored energy of said vehicle at each point in time, each vehicle input vector, and each database input vector for each defined time interval.

16 . A non-transitory program storage device readable by a computer, and comprising a program

Assignees

• Invently Automotive Inc

Inventors

• Brown Stephen J

Classifications

• G06F17/18 Primary

  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 title

• G01R31/3651

  Physics · mapped topic

• G01L5/13

  for measuring the tractive or propulsive power of vehicles · CPC title

• G01R31/3648 Primary

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• G01C21/3469

  Fuel consumption; Energy use; Emission aspects · CPC title