SE541872C2 - Method and system for predicting the fuel consumption for a vehicle - Google Patents

Method and system for predicting the fuel consumption for a vehicle

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Publication number
SE541872C2
SE541872C2 SE1651247A SE1651247A SE541872C2 SE 541872 C2 SE541872 C2 SE 541872C2 SE 1651247 A SE1651247 A SE 1651247A SE 1651247 A SE1651247 A SE 1651247A SE 541872 C2 SE541872 C2 SE 541872C2
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Sweden
Prior art keywords
fuel consumption
vehicle
assignment
specified
predicting
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SE1651247A
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SE1651247A1 (en
Inventor
Rikard Laxhammar
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Scania Cv Ab
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Publication date
Application filed by Scania Cv Ab filed Critical Scania Cv Ab
Priority to SE1651247A priority Critical patent/SE541872C2/en
Priority to SE1750699A priority patent/SE541876C2/en
Priority to DE102017008605.2A priority patent/DE102017008605A1/en
Publication of SE1651247A1 publication Critical patent/SE1651247A1/en
Publication of SE541872C2 publication Critical patent/SE541872C2/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/10—Weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/406—Traffic density
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/20—Ambient conditions, e.g. wind or rain
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The present invention relates to a method for predicting the fuel consumption for a vehicle. The method comprises the steps of: collecting (S1) information about fuel consumption from driving circumstances of different vehicles on different occasions in time; in connection to the different occasions in time, collecting (S2) information about certain parameters relevant for fuel consumption; creating (S3) a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption. The method further comprises the step of applying (S4) said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.The present invention also relates to a system for predicting the fuel consumption for a vehicle. The present invention also relates to a vehicle. The present invention also relates to a computer program and a computer program product.

Description

METHOD AND SYSTEM FOR PREDICTING THE FUEL CONSUMPTION FOR A VEHICLE TECHNICAL FIELD The invention relates to a method for predicting the fuel consumption for a vehicle according to the preamble of claim 1. The invention also relates to a system for predicting the fuel consumption for a vehicle. The invention also relates to a vehicle. The invention in addition relates to a computer program and a computer program product.
BACKGROUND ART For vehicles such as vehicles in a vehicle fleet performing transport assignments it is important to be able to estimate the fuel consumption for a specific vehicle and specific assignment in order to improve reduction in fuel consumption by planning the assignments based upon the thus estimated fuel consumption. It is also important to know how reliable such an estimation of fuel consumption is.
Estimation of fuel consumption for a specific assignment may be performed based on the total distance of planned route and the average consumption for the specific vehicle. This, however does not provide an accurate estimation of the fuel consumption in that relevant parameters affecting fuel consumption such as weather conditions and road conditions are not taken into account.
US2015241310 discloses prediction of fuel consumption based on a statistic model and by using machine learning.
There is however a need for improving predicting the fuel consumption for a vehicle and the reliability of the prediction.
OBJECTS OF THE INVENTION An object of the present invention is to provide a method for predicting the fuel consumption for a vehicle which facilitates an accurate and reliable prediction.
Another object of the present invention is to provide a system for predicting the fuel consumption for a vehicle facilitates an accurate and reliable prediction.
A further object of the present invention is to provide a method for predicting the fuel consumption for a vehicle which in addition facilitates detecting unexpected fuel consumption after a certain specified driving assignment employing a specified vehicle.
A further object of the present invention is to provide a system for predicting the fuel consumption for a vehicle which in addition facilitates detecting unexpected fuel consumption after a certain specified driving assignment employing a specified vehicle.
SUMMARY OF THE INVENTION These and other objects, apparent from the following description, are achieved by a method, a system, a vehicle, a computer program and a computer program product, as set out in the appended independent claims. Preferred embodiments of the method and the system are defined in appended dependent claims.
Specifically an object of the invention is achieved by a method for predicting the fuel consumption for a vehicle. The method comprises the steps of: collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time; in connection to the different occasions in time, collecting information about certain parameters relevant for fuel consumption; creating a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption. The method further comprises the step of applying said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
The step of collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time comprises according to an embodiment determining fuel consumption from driving circumstances of different vehicles on different occasions in time. The fuel may comprise any type of fuel for propelling a vehicle such as diesel fuel, petrol fuel/gasoline fuel, ethanol fuel, hydrogen fuel, gas fuel such as compressed natural gas (CNG), liquid natural gas (LNG) or liquefied petroleum gas (LPG), or electric fuel, i.e. whether the vehicle is an electric vehicle requiring electric energy. For a hybrid vehicle energy consumption comprises fuel consumption for operating the engine, e.g. internal combustion engine, and electric energy consumption for operating an electric machine.
The step of determining fuel consumption may comprise any suitable detector/detector unit for detecting the fuel consumption.
The machine learning function comprises a machine learning algorithm.
By thus collecting information about certain parameters relevant for fuel consumption and creating a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption improved accuracy in the prediction of fuel consumption is obtained.
By thus applying said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, and predicting the fuel consumption range with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range a reliable prediction of the fuel consumption is facilitated. That is, hereby both a fuel consumption range and the probability that the predicted fuel consumption will be within the range is obtained which in turn improves reliability which is relevant information for planning the assignments based upon the thus estimated fuel consumption.
By applying said model detecting unexpected fuel consumption after a certain specified driving assignment employing a specified vehicle is in addition facilitated as explained below.
According to an embodiment of the method the step of predicting a fuel consumption range with a predetermined confidence level comprises the step of applying the so called conformal prediction method for machine learning. By thus applying the so called conformal prediction method for machine learning a unique and efficient way of obtaining the range and confidence level of the range is obtained.
According to an embodiment of the method said certain parameters relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
By thus processing one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon relevant for fuel consumption together with the collected information about fuel consumption model comprising a machine learning function the accuracy in determining the fuel consumption is improved.
Of course any other parameters relevant for fuel consumption may be considered.
The step of collecting information about total weight of the respective vehicles comprises the step of determining total weight of the respective vehicles in connection to the different occasions in time. The step of determining total weight of the respective vehicles in connection to the different occasions in time may comprise any suitable weight sensor and/or pressure sensor. The total weight of the respective vehicles comprises the weight of the vehicle and weight of load of the vehicle and possible change of load of the vehicle during the respective assignments of the different occasions.
The step of collecting information about weather conditions comprises the step of determining the weather conditions in connection to the different occasions in time. Weather conditions may be determined by means of weather sensors comprising any suitable rain sensor/precipitation sensor e.g. arranged in connection to the windshield, any suitable temperature sensor, any suitable wind sensor. Weather conditions may be determined by means of external weather data from any external provider/server with which any onboard or off-board database/ control unit/server unit or the like may establish contact. On-board refers to internally in a vehicle and off-board refers externally to the vehicle, i.e. any suitable external location.
Thus determined weather conditions may comprise rain and extent of rain, snow and extent of snow, current temperature, wind conditions, extent of wind and direction of wind, relative humidity, air pressure or the like.
The step of collecting information about road conditions comprises according to an embodiment the step of determining the road conditions in connection to the different occasions in time. Road conditions may be determined by means of any suitable detector means comprising any suitable detector unit for detecting the surface of the road along which the vehicle is travelling such as one or more camera units and/or one or more laser scanner units and/or one or more radar units. Road conditions may be determined by means of external data from any external provider/server with which any on-board or off-board database/ control unit/server unit or the like may establish contact.
The step of collecting information about the configuration of the road comprises according to an embodiment the step of determining configuration of the road in connection to the different occasions in time. The configuration of the road may comprise extension of the road comprising curvature and topology of the road. The configuration of the road may comprise speed limits along the road. The configuration of the road may be determined by means of any suitable detector means comprising any suitable detector unit for detecting the configuration of the road along which the vehicle is travelling comprising curvature, slopes, number of lanes, speed limits and the like, such as one or more camera units and/or one or more laser scanner units and/or one or more radar units. The configuration of the road may be determined by means of map data and position of the vehicle. The current position of the vehicle may be determined by means of a Global Navigation Satellite System, GNSS, e.g. a global positioning system, GPS, for continuously determining the position of the vehicle and thus whether the vehicle is moving.
Thus determined road conditions may comprise slippery road due to e.g. ice on the road, snow on the road, oil on the road, gravel on the road, water on the road, curves of the road or other road conditions that may affect driving along the road.
The step of collecting information about driver of the respective vehicles comprises according to an embodiment the step of determining driver of the respective vehicles in connection to the different occasions in time. The step of determining driver of the respective vehicles may comprise any suitable detector for detecting the operator/driver of the vehicle comprising e.g. one or more cameras for detecting and identifying the operator/driver and/or a card, ignition key or the like with which the operator/driver is identified in connection to operating the vehicle. The step of determining driver of the respective vehicles may comprise any suitable register in which the driver for the specific vehicle for the specific assignments.
The step of collecting information about traffic situation comprises according to an embodiment the step of determining the traffic situation in connection to the different occasions in time. The step of determining the traffic situation in connection to the different occasions in time may comprise any suitable detector for detecting the traffic situation. Traffic situation may be determined by means of external traffic data from any external provider/server with which any on-board or off-board database/control unit/server unit or the like may establish contact. On-board refers to internally in a vehicle and off-board refers externally to the vehicle, i.e. any suitable external location.
The step of collecting information about extent of driving in a platoon may comprise the step of for determining extent of driving in a platoon in connection to the different occasions in time. The step for determining extent of driving in a platoon in connection to the different occasions in time may comprise any suitable communication means for communicating with other vehicles within a vehicle-to-vehicle communication arrangement and/or for communicating with infrastructure within a vehicle-to-infrastructure communication arrangement within a platoon during platooning and/or to other vehicles and/or to infrastructure. Infrastructure may comprise any suitable communication means comprising any suitable unit such as a server unit or the like. It may also be determined whether it has been a leading vehicle or a trailing vehicle in said platoon during platooning.
Thus, according to an embodiment the different vehicles are determining, in connection to the different occasions in time, one or more of said parameters relevant for fuel consumption with on-board sensors and/or external means.
According to an embodiment of the method said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
According to an embodiment of the method said specified driving assignment comprises one or more of the following assignment parameters: total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
Of course any other assignment parameters may be considered. Type of fuel used by the specific vehicle is also determined.
The method comprises the step of estimating the assignment parameter total weight of the specified vehicle. The step of estimating total weight of the specified vehicle prior to the assignment comprises determining the unloaded weight of the vehicle and estimating the weight of the load and possible change of the load during the assignment. The total weight of the respective vehicles comprises the weight of the vehicle and weight of load of the vehicle and possible change of load of the vehicle during the respective assignments of the different occasions.
The method comprises the step of predicting the expected weather conditions during the specified assignment. Expected weather conditions may be determined by means of external weather data from any external provider/server with which any on-board or off-board database/ control unit/server unit or the like may establish contact. Thus determined weather conditions may comprise rain and extent of rain, snow and extent of snow, current temperature, wind conditions, extent of wind and direction of wind, relative humidity, air pressure or the like.
The method comprises the step of predicting the expected road conditions during the specified assignment. Road conditions may be determined by means of external data from any external provider/server with which any onboard or off-board database/ control unit/server unit or the like may establish contact. Thus determined road conditions may comprise slippery road due to e.g. ice on the road, snow on the road, oil on the road, gravel on the road, water on the road, curves of the road or other road conditions that may affect driving along the road.
The method comprises the step of determining the intended driver for the specified assignment. The step of determining intended driver for the specified assignment may comprise any suitable register in which the driver for the specific vehicle for the specific assignments is stored and accessible.
The method comprises the step of determining intended platooning during the assignment. The step of determining intended platooning during the assignment may comprise any suitable register in which the intended platooning for the specific vehicle for the specific assignments is stored and accessible.
The method comprises the step of predicting expected traffic situation during the specified assignment. The step of predicting expected traffic situation during the specified assignment may comprise information about the specific route and the time when the vehicle is expected to perform the assignment and hence when the vehicle will be on specific locations along the route, and data of what the normal traffic situation is expected to be during these points of time.
Said model is then applied for predicting a fuel consumption range for the specified driving assignment employing the specified vehicle with these assignment parameters. The fuel consumption range is predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
According to an embodiment the method comprise the steps of: determining the fuel consumption after a certain specified driving assignment employing a specified vehicle; applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle, comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
The step of determining the fuel consumption after a certain specified driving assignment employing a specified vehicle may comprise any suitable detector/detector unit for detecting the fuel consumption. The type of fuel used for the specific vehicle for the specific assignment is determined.
By thus applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle, the fuel consumption range thus being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range, the expected frequency of deviations may be controlled in advance wherein the frequency of false or unwanted detections may be limited. Thus, by comparing said determined fuel consumption with said predicted fuel consumption range unexpected fuel consumption associated with said assignment may be detected in an efficient way. Thus, detecting unexpected fuel consumption after a certain specified driving assignment employing a specified vehicle is facilitated by thus applying said model.
Thus, in addition to applying said model for predicting the fuel consumption for a specific assignment by for a specific vehicle and in addition to determining the actual fuel consumption during said specific assignment for the specific vehicle, the specific parameters during that particular assignment such as weather conditions, road conditions, configuration of the road, traffic situation, etc. are processed by said model in order to further be able to determine if a possible deviation was due to changed conditions/situations during said assignment or whether that deviation should not have occurred or whether a non-deviation actually should have been a deviation due to said conditions/situations.
Specifically an object of the invention is achieved by a system for predicting the fuel consumption for a vehicle. The system comprises means for collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time; means for collecting information about certain parameters relevant for fuel consumption in connection to the different occasions in time; a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption. The system further comprises means for applying said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, comprising means for predicting said fuel consumption range with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
According to an embodiment of the system the means for predicting a fuel consumption range with a predetermined confidence level comprises the so called conformal prediction model for machine learning.
According to an embodiment of the system said certain parameters relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
According to an embodiment of the system said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
According to an embodiment of the system said specified driving assignment comprises one or more of the following assignment parameters: total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
According to an embodiment the system comprises means for determining the fuel consumption after a certain specified driving assignment employing a specified vehicle; means for applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle; means for comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
The system for predicting the fuel consumption for a vehicle is adapted to perform the methods as set out herein.
The system according to the invention has the advantages according to the corresponding method as set out herein.
Specifically an object of the invention is achieved by a vehicle comprising a system as set out herein.
Specifically an object of the invention is achieved by a computer program for predicting the fuel consumption for a vehicle, said computer program comprising program code which, when run on an electronic control unit or another computer connected to the electronic control unit, causes the electronic control unit to perform methods as set out herein.
Specifically an object of the invention is achieved by a computer program product comprising a digital storage medium storing the computer program.
BRIEF DESCRIPTION OF THE DRAWINGS For a better understanding of the present invention reference is made to the following detailed description when read in conjunction with the accompanying drawings, wherein like reference characters refer to like parts throughout the several views, and in which: Fig. 1 schematically illustrates a side view of a vehicle according to the present invention; Fig. 2 schematically illustrates a block diagram of a system for predicting the fuel consumption for a vehicle according to an embodiment of the present invention; Fig. 3 schematically illustrates a block diagram of a method for predicting the fuel consumption for a vehicle according to an embodiment of the present invention; and Fig. 4 schematically illustrates a computer according to an embodiment of the present invention.
DETAILED DESCRIPTION Hereinafter the term “link” refers to a communication link which may be a physical connector, such as an optoelectronic communication wire, or a nonphysical connector such as a wireless connection, for example a radio or microwave link.
Hereinafter the term “conformal prediction” refers to the method/model in which past experience is used to determine precise levels of confidence in new predictions. Given an error probability e, together with a method that makes a point prediction of a label y, it produces a set of labels, typically containing the point prediction, which also contains y with probability 1- e.
Fig. 1 schematically illustrates a side view of a vehicle 1 according to the present invention. The exemplified vehicle 1 is a heavy vehicle in the shape of a truck. The vehicle according to the present invention could be any suitable vehicle such as a bus or a car. The vehicle according to the present invention could be an autonomous vehicle.
Fig. 2 schematically illustrates a system I for predicting the fuel consumption for a vehicle according to an embodiment of the present invention.
The system I comprises means 110 for collecting information about fuel consumption F1, F2, F3, .... Fn from driving circumstances of different vehicles on different occasions in time O1, O2, O3, ..., On.
The system I comprises means 120 for collecting information about certain parameters P1-Pn relevant for fuel consumption in connection to the different occasions in time O1, O2, O3, .... On.
The means 110 for collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time and the means 120 for collecting information about certain parameters relevant for fuel consumption in connection to the different occasions in time are according to an embodiment comprised in a means 100 for collecting and transmitting information. The means 100 may comprise any suitable electronic control unit, server unit or the like.
According to an embodiment of the system I said certain parameters relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
According to an embodiment of the system I said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
The system I comprises a model M comprising a machine learning function for processing said collected information about fuel consumption F1, F2, F3, .... Fn and said certain parameters P1-Pn relevant for fuel consumption. The machine learning function comprises a machine learning algorithm.
The model M is operably connected to the means 110 for collecting information about fuel consumption F1, F2, F3, .... Fn from driving circumstances of different vehicles on different occasions in time via a link 10. The model M is arranged to receive signals via said link 10 representing data for fuel consumption from driving circumstances of different vehicles on different occasions in time.
The model M is operably connected to the means 120 for collecting information about certain parameters P1-Pn relevant for fuel consumption in connection to the different occasions in time O1, O2, O3, .... On via a link 20. The model M is arranged to receive signals via said link 20 representing data about certain parameters P1-Pn relevant for fuel consumption in connection to the different occasions in time. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for total weight of the respective vehicles. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for specification of the respective vehicles. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for weather conditions. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for road conditions. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for configuration of the road. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for driver of the respective vehicles. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for traffic situation. The model M is according to an embodiment arranged to receive a signal via said link 20 representing data for extent of driving in a platoon.
The machine learning function of said model M for processing said collected information about fuel consumption F1, F2, F3, .... Fn and said certain parameters P1-Pn relevant for fuel consumption will thus process the information and learn what the fuel consumption will be for specific vehicles depending on said parameters relevant for fuel consumption.
The system further comprises means 130 for applying said model M for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle.
The means 130 for applying said model M for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle may be used for one or more specified vehicles and corresponding specified assignments.
The means 130 for applying said model M for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle comprises means 132 for collecting assignment parameters AP1, AP2, AP3, ..., ??? relevant for fuel consumption for the specific assignment for the specified vehicle. The assignment parameters are collected prior to the assignment and thus comprise predicted/expected parameters.
According to an embodiment of the system said specified driving assignment comprises one or more of the following assignment parameters: total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
The model M is operably connected to the means 132 for collecting assignment parameters AP1, AP2, AP3, .... APn relevant for fuel consumption for the specific assignment for the specified vehicle via a link 32. The model M is arranged to receive signals via said link 32 representing data about assignment parameters AP1, AP2, AP3, .... APn relevant for fuel consumption for the specific assignment for the specified vehicle. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for total weight of the specified vehicle. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for expected weather conditions during the specified assignment. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for expected road conditions during the specified assignment. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for configuration of the road for the assignment. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for intended driver for the specified assignment. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for expected traffic situation during the specified assignment. The model M is according to an embodiment arranged to receive a signal via said link 32 representing data for intended platooning during the assignment.
The model M comprises means M1 for predicting said fuel consumption range with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
According to an embodiment of the system I the means M1 for predicting a fuel consumption range with a predetermined confidence level comprises the so called conformal prediction model PC for machine learning.
The means M1 for predicting a fuel consumption range with a predetermined confidence level is operably connected to the means 130 for applying said model M for predicting a fuel consumption range for a specified driving assignment level via a link 30a. The means M1 is arranged to send a signal via said link 30a representing data for predicted fuel consumption range with a predetermined confidence level.
According to an embodiment the system I comprises means 140 for determining the fuel consumption after a certain specified driving assignment employing a specified vehicle. The means 140 for determining fuel consumption may comprise any suitable detector/detector unit for detecting the fuel consumption.
The means 130 for applying said model M for predicting a fuel consumption range for a specified driving assignment is arranged to be utilized for said specified driving assignment employing said specified vehicle.
According to an embodiment the system I comprises means 150 for comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
The means 150 for comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment is operably connected to said means 140 for determining the fuel consumption after a certain specified driving assignment employing a specified vehicle via a link 40. The means 150 is via said link 40 arranged to receive a signal representing data for determined fuel consumption for said specified vehicle.
The model M for predicting a fuel consumption range for a specified driving assignment is operably connected to the means 150 for comparing said determined fuel consumption with said predicted fuel consumption range via a link 30b. The means M1 is arranged to send a signal via said link 30b representing data for predicted fuel consumption range with a predetermined confidence level.
By comparing said determined fuel consumption with said predicted fuel consumption range an unexpected fuel consumption associated with said assignment may be detected in an efficient way.
Fig. 3 schematically illustrates a block diagram of a method for predicting the fuel consumption for a vehicle according to an embodiment of the present invention.
According to the embodiment the method for predicting the fuel consumption for a vehicle comprises a step S1. In this step information about fuel consumption from driving circumstances of different vehicles on different occasions in time is collected.
According to the embodiment the method for predicting the fuel consumption for a vehicle comprises a step S2. In this step information about certain parameters relevant for fuel consumption is collected in connection to the different occasions in time.
According to the embodiment the method for predicting the fuel consumption for a vehicle comprises a step S3. In this step a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption is created.
According to the embodiment the method for predicting the fuel consumption for a vehicle comprises a step S4. In this step said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle is applied, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
The step of collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time comprises according to an embodiment determining fuel consumption from driving circumstances of different vehicles on different occasions in time. The fuel may comprise any type of fuel for propelling a vehicle such as diesel fuel, petrol fuel/gasoline fuel, ethanol fuel, hydrogen fuel, gas fuel such as compressed natural gas (CNG), liquid natural gas (LNG) or liquefied petroleum gas (LPG), or electric fuel, i.e. whether the vehicle is an electric vehicle requiring electric energy. For a hybrid vehicle energy consumption comprises fuel consumption for operating the engine, e.g. internal combustion engine, and electric energy consumption for operating an electric machine.
The step of determining fuel consumption may comprise any suitable detector/detector unit for detecting the fuel consumption.
The machine learning function comprises a machine learning algorithm.
According to an embodiment of the method the step of predicting a fuel consumption range with a predetermined confidence level comprises the step of applying the so called conformal prediction method for machine learning.
By thus applying the so called conformal prediction method for machine learning a unique, statistically valid and efficient way of obtaining the range and confidence level of the range is obtained.
According to an embodiment of the method said certain parameters relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
Thus, according to an embodiment the different vehicles are determining in connection to the different occasions in time one or more of said parameters relevant for fuel consumption with on-board sensors and/or external means.
According to an embodiment of the method said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
According to an embodiment of the method said specified driving assignment comprises one or more of the following assignment parameters: total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
Of course any other assignment parameters may be considered. Type of fuel used by the specific vehicle is also determined.
Said model is then applied for predicting a fuel consumption range for the specified driving assignment employing the specified vehicle with these assignment parameters. The fuel consumption range is predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
According to an embodiment the method comprises the steps of: determining the fuel consumption after a certain specified driving assignment employing a specified vehicle; applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle, comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
The step of determining the fuel consumption after a certain specified driving assignment employing a specified vehicle may comprise any suitable detector/detector unit for detecting the fuel consumption. The type of fuel used for the specific vehicle for the specific assignment is determined.
With reference to figure 4, a diagram of an apparatus 500 is shown. Apparatus 500 comprises a non-volatile memory 520, a data processing device 510 and a read/write memory 550. Non-volatile memory 520 has a first memory portion 530 wherein a computer program, such as an operating system, is stored for controlling the function of apparatus 500. Further, apparatus 500 comprises a bus controller, a serial communication port, l/O-means, an A/D-converter, a time date entry and transmission unit, an event counter and an interrupt controller (not shown). Non-volatile memory 520 also has a second memory portion 540.
A computer program P is provided comprising routines for predicting the fuel consumption for a vehicle. The program P comprises routines for collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time. The program P comprises routines for collecting information about certain parameters relevant for fuel consumption in connection to the different occasions in time. The program P comprises routines for creating a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption. The program P comprises routines for applying said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range. The computer program P may be stored in an executable manner or in a compressed condition in a separate memory 560 and/or in read/write memory 550.
When it is stated that data processing device 510 performs a certain function it should be understood that data processing device 510 performs a certain part of the program which is stored in separate memory 560, or a certain part of the program which is stored in read/write memory 550.
Data processing device 510 may communicate with a data communications port 599 by means of a data bus 516. Non-volatile memory 520 is adapted for communication with data processing device 510 via a data bus 513. Separate memory 560 is adapted for communication with data processing device 510 via a data bus 511. Read/write memory 550 is adapted for communication with data processing device 510 via a data bus 515. To the data communications port 599 e.g. the links connected to the control units 100 may be connected.
When data is received on data port 599 it is temporarily stored in second memory portion 540. When the received input data has been temporarily stored, data processing device 510 is set up to perform execution of code in a manner described above. The signals received on data port 599 can be used by apparatus 500 for collecting information about fuel consumption from driving circumstances of different vehicles on different occasions in time. The signals received on data port 599 can be used by apparatus 500 for collecting information about certain parameters relevant for fuel consumption in connection to the different occasions in time. The signals received on data port 599 can be used by apparatus 500 for creating a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption. The signals received on data port 599 can be used by apparatus 500 for applying said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range.
Parts of the methods described herein can be performed by apparatus 500 by means of data processing device 510 running the program stored in separate memory 560 or read/write memory 550. When apparatus 500 runs the program, parts of the methods described herein are executed.
The foregoing description of the preferred embodiments of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations will be apparent to practitioners skilled in the art. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to understand the invention for various embodiments and with the various modifications as are suited to the particular use contemplated.

Claims (13)

1. A method for predicting the fuel consumption for a vehicle (V), the method comprising the steps of: collecting (S1) information about fuel consumption (F1, F2, F3, ..., Fn) from driving circumstances of different vehicles on different occasions in time ( O1, O2, O3, .... On); in connection to the different occasions in time, collecting (S2) information about certain parameters (P1-Pn) relevant for fuel consumption; creating (S3) a model comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption, characterized by the step of applying (S4) said model for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, said predicted fuel consumption range being predicted with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range, wherein the step of predicting a fuel consumption range with a predetermined confidence level comprises the step of applying the so called conformal prediction method for machine learning.
2. A method according to claim 1, wherein said certain parameters (P1-Pn) relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
3. A method according to claim 1 or 2, wherein said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
4. A method according to any of claims 1-3, wherein said specified driving assignment comprises one or more of the following assignment parameters (AP1, AP2, AP3, ..., APn): total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
5. A method according to any of claims 1-4, comprising the steps of: determining the fuel consumption after a certain specified driving assignment employing a specified vehicle; applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle, comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
6. A system for predicting the fuel consumption for a vehicle, the system comprising means (110) for collecting information about fuel consumption (F1, F2, F3, ..., Fn) from driving circumstances of different vehicles on different occasions in time (O1, O2, O3, .... On); means (120) for collecting information about certain parameters (P1-Pn) relevant for fuel consumption in connection to the different occasions in time; a model (M) comprising a machine learning function for processing said collected information about fuel consumption and said certain parameters relevant for fuel consumption, characterized by means (130) for applying said model (M) for predicting a fuel consumption range for a specified driving assignment employing a specified vehicle, comprising means (M1) for predicting said fuel consumption range with a predetermined confidence level corresponding to the frequency at which the predetermined fuel consumption will fall within said predicted range, wherein the means (M1) for predicting a fuel consumption range with a predetermined confidence level comprises the so called conformal prediction model (CP) for machine learning.
7. A system according to claim 6, wherein said certain parameters (P1-Pn) relevant for fuel consumption collected in connection to the different occasions in time comprises one or more of: total weight of the respective vehicles, specification of the respective vehicles, weather conditions, road conditions, configuration of the road, driver of the respective vehicles, traffic situation, and extent of driving in a platoon.
8. A system according to claim 6 or 7, wherein said specified vehicle comprises one or more of the following vehicle parameters: vehicle type, vehicle age, vehicle weight, engine power, emission classification, gearbox system, clutch system, rear axle ratio and number of vehicle axles.
9. A system according to any of claims 6-8, wherein said specified driving assignment comprises one or more of the following assignment parameters (AP1, AP2, AP3, ..., APn): total weight of the specified vehicle, expected weather conditions during the specified assignment, expected road conditions during the specified assignment, configuration of the road for the assignment, intended driver for the specified assignment, expected traffic situation during the specified assignment, and intended platooning during the assignment.
10. A system according to any of claims 6-9, comprising means (140) for determining the fuel consumption after a certain specified driving assignment employing a specified vehicle (V); means for applying said model for predicting a fuel consumption range for said specified driving assignment employing said specified vehicle; means for comparing said determined fuel consumption with said predicted fuel consumption range for detecting possible unexpected fuel consumption associated with said assignment.
11. A vehicle (1) comprising a system (I) according to any of claims 6-10.
12. A computer program (P) for predicting the fuel consumption for a vehicle, said computer program (P) comprising program code which, when run on an electronic control unit (100) or another computer (500) connected to the electronic control unit (100), causes the electronic control unit to perform the steps according to claim 1-5.
13. A computer program product comprising a digital storage medium storing the computer program according to claim 12.
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