System and Method for Estimating and Predicting Vehicle Trip Energy Consumption

Abstract

A system for estimating or predicting energy consumption for a trip of a personal vehicle includes a user facing portion and a back-end portion. The user facing portion includes a display and a user interface hosting a real-time application configured to receive travel information and present a received energy distribution via the display. The back-end portion includes a back-end database and an energy processor configured to access the back-end database. The energy processor includes a demand model module configured to produce a set of possible velocity histories and a set of possible ambient temperatures. A vehicle model module is configured to receive the velocity histories and ambient temperatures to provide the energy distribution or a probabilistic prediction of future energy consumption to the user facing portion.

What is claimed is: 
     
         1 . A computer based method for estimating and/or predicting energy consumption for a personal vehicle, comprising the steps of:
 receiving low-resolution trip data regarding a plurality of prior vehicle trips;   accessing driving behavior data from a driving behavior database;   matching the trip data to the driving behavior data to produce a set of possible full or partial velocity histories;   accessing weather data from a historical weather database;   matching the trip data to the weather data to produce a set of possible ambient temperatures;   accessing vehicle data from a vehicle efficiency database; and   generating an energy distribution or prediction from the vehicle data, the set of possible velocity histories, and the set of possible ambient temperatures.   
     
     
         2 . The method of  claim 1 , further comprising the step of displaying the energy distribution with a display device. 
     
     
         3 . The method of  claim 1 , further comprising the step of derounding the trip data. 
     
     
         4 . The method of  claim 1 , further comprising the steps of:
 generating a tractive energy model from the vehicle data and the set of possible velocity histories;   generating a drive efficiency model from the set of possible velocity histories; and   generating an auxiliary energy model from the set of possible ambient temperatures.   
     
     
         5 . The method of  claim 1 , further comprising the step of populating the driving behavior database with GPS trajectory data, such that it can be accessed given travel information regarding a trip and demographic information of a vehicle driver. 
     
     
         6 . The method of  claim 1 , further comprising the step of populating the historical weather database with weather data, such that it can be accessed given travel information regarding a trip, and demographic information of a vehicle driver. 
     
     
         7 . The method of  claim 1 , further comprising the step of populating the vehicle efficiency database with vehicle data, such that it can be accessed given a selected vehicle type, and demographic information of a vehicle driver. 
     
     
         8 . The method of  claim 1 , further comprising the step of receiving a selected vehicle type and demographic information of a vehicle driver from a driver via a user questionnaire. 
     
     
         9 . The method of  claim 1 , further comprising the step of matching a driver to a vehicle based on user inputs and contextual information. 
     
     
         10 . The method of  claim 1 , further comprising the step suggesting a route based on the energy distribution calculated by the energy processor.