# US 8,543,321 B2 · Method for forecasting the evolution of the magnitude of a data for a vehicle journey > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,543,321 B2 - Document ID: US8543321B2 - Status: Granted - Kind: B2 - Application number: 13/496,228 - Filed: 2009-09-25 - Published: 2012-07-12 - Granted: 2013-09-24 - Earliest claimed: 2009-09-25 - Assignees: Renault Trucks - Applicants: Romain Montelimard - Inventors: Romain Montelimard - Web page: https://patentgrep.com/patent/US8543321B2 ## CPC - B60W 40/06: Road conditions - B60W 40/09: Driving style or behaviour - B60W 40/10: related to vehicle motion - B60W 40/13: Load or weight - B60W 50/0097: {Predicting future conditions} - G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents} - G01C 21/3469: {Fuel consumption; Energy use; Emission aspects} - B60W 2050/0029: {Mathematical model of the driver} - B60W 2050/0031: {Mathematical model of the vehicle} - B60W 2050/004: {of the clutch} - B60W 2556/10: Historical data - B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data - Y02T 10/40: Engine management systems - Y02T 10/84: Data processing systems or methods, management, administration - 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 - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION ## Abstract The magnitude (Y) of a data associated to a journey of an automotive vehicle is expressed by a function (f) of at least one input parameter (x). This method includes at least the steps of: a) defining a first model (ft=0) of the function; b) running the vehicle on a reference trip, the input parameter (x, m, p) and the magnitude (Y) being measured (YM, xM) during or at the end of the reference trip; c) computing a value (YC) of the magnitude by using the first model (ft=0) of the function (f) and the value of the parameter (xM) measured at step b); d) comparing the values (YM, YC) of the magnitude at said time; and e) adjusting the function (ft=1) in a way corresponding to the reduction of the difference between the measured value (YM) and the computed value (YC). ## Claims ### Claim 1 A method for forecasting the evolution of the magnitude of a data associated to a journey of an automotive vehicle via a mathematical model and for selecting the best miming conditions for a vehicle, in an onboard computer computation system, where the magnitude is expressed by a function of at least one input parameter, comprising: defining a first model of the function used for computing the magnitude on the basis of the input parameter; running the vehicle on a reference trip, for a given time or a given distance, the input parameter and the magnitude being measured (YM, xM) to provide a measured value of the magnitude at least at one time during or at the end of the reference trip; computing a computed value of the magnitude by using the first model of the function and the value of the parameter measured at the running step; comparing the values of the magnitude at the at least one time measured at the running step and computed at the computation step; if the comparison of the measured value and the computed value reveals a difference greater than a predetermined difference value between the measured value and the computed value, adjusting the function in a way corresponding to a reduction of the difference between the measured value and the computed value; and selecting by onboard computation means, based on the forecasting of the magnitude of the data associated to a given journey, a road to be followed. ### Claim 2 (depends on 1) Method according to claim 1, comprising performing the running step, the computation step the comparison step, and the adjustment step successively, a plurality of times, the function adjusted in the adjustment step of a preceding performance of the running step, the computation step, the comparison step, and the adjustment step being used to compute the value of the magnitude on a next performance of the running step, the computation step, the comparison step, and the adjustment step. ### Claim 3 (depends on 1) Method according to claim 1, wherein the reference trip is a part of the journey to be followed by the vehicle. ### Claim 4 (depends on 3) Method according to claim 3, wherein the reference trip is an initial part of the journey. ### Claim 5 (depends on 1) Method according to claim 1, wherein the computation step, the comparison step and the adjustment step occur in real time. ### Claim 6 (depends on 1) Method according to claim 1, wherein in the defining step, the first model is based on an initial data set for the function. ### Claim 7 (depends on 1) Method according to claim 1, wherein in the defining step, the first model is based on the last data set used for the function in a previous journey of the vehicle. ### Claim 8 (depends on 1) Method according to claim 1, wherein the magnitude (Y) of the data is expressed as a polynomial function of one parameter in the form Y = ∑ i = 0 N ⁢ a i ⁢ x i where x is the input parameter, N is an integer larger than 1 and ai is a real number for i integer between 0 and N, and wherein the first model includes a set of N+1 real numbers ([a0, . . . aN]t=0) corresponding, to values of ai for i integer between 0 and N. ### Claim 9 (depends on 8) Method according to claim 8, wherein in the comparison step, adjustment of the function occurs by adjusting the respective values ([a0, . . . aN]t=0) of real numbers ai for i integer between 0 and N. ### Claim 10 (depends on 1) Method according to claim 1, wherein the magnitude (Y) of the data is expressed as a polynomial function of several parameters in the form, Y = Ax + B ⁢ ⅆ x ⅆ t + Cm + Dp + E where x is a distance travelled during a reference trip, a journey or a part of a journey, m is the mass of the vehicle, p is the tire pressure, and A, B, C, D and E are real numbers and wherein the first model includes a set of real numbers (A0, B0, C0, D0 and E0) corresponding to A, B, C, D and E. ### Claim 11 (depends on 10) Method according to claim 10, wherein in the comparison step, adjustment of the function occurs by adjusting the respective values (A1, B1, C1, D1 and E1) of numbers A, B, C, D and E. ### Claim 12 (depends on 1) Method according to claim 1, wherein the magnitude of the data is expressed as a function of a parameter ( ⅆ x ⅆ t ) which depends on the driver's behaviour and wherein the first model includes a set of numbers. ### Claim 13 (depends on 12) Method according to claim 12, wherein in the comparison step, adjustment of the function occurs by selecting a set of numbers which depends on the driver's behaviour determined on the reference trip. ### Claim 14 (depends on 1) Method according to claim 1, wherein the data comprises fuel consumption.