EP4341882A1 - Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne - Google Patents

Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne

Info

Publication number
EP4341882A1
EP4341882A1 EP22729152.3A EP22729152A EP4341882A1 EP 4341882 A1 EP4341882 A1 EP 4341882A1 EP 22729152 A EP22729152 A EP 22729152A EP 4341882 A1 EP4341882 A1 EP 4341882A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
person
vehicle configuration
profile
driving profile
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22729152.3A
Other languages
German (de)
English (en)
Inventor
Jens Kastens
Heidi Rathgeb
Alexander Graf
Nicolas Blum
Andrea AGOSTINELLI
Dominic SCHMIDT
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ZF Friedrichshafen AG
Original Assignee
ZF Friedrichshafen AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ZF Friedrichshafen AG filed Critical ZF Friedrichshafen AG
Publication of EP4341882A1 publication Critical patent/EP4341882A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Definitions

  • the invention relates to a method, a computer program, a computer-readable medium and a system for determining a vehicle configuration that is adapted to the driving behavior of a person.
  • One aspect of the invention relates to a method for determining a vehicle configuration that is adapted to the driving behavior of a person or a driver.
  • the computer-implemented method can be executed automatically by a computer system.
  • the method can be used to recommend a suitable drive train for the vehicle as a function of the driving profile.
  • the vehicle may be a road vehicle such as a car, truck, or motorcycle.
  • the method comprises: determining a driving profile of the person, the driving profile being a speed profile and/or comprises an acceleration profile of a vehicle controlled by the person.
  • the driving profile is determined using a vehicle other than the vehicle whose configuration is to be adapted or optimized.
  • the driving profile includes data of a vehicle that is controlled by the person or the driver and that can be recorded and/or determined while driving. This data can include speed values and/or acceleration values. It is also possible that further information, such as a speed of the engine of the vehicle, a gear position, an accelerator pedal position, etc., is included in the driving profile.
  • the data can be determined via sensors in the vehicle and/or calculated from measured values of these sensors. All this data is recorded for the driving profile over time.
  • the method further comprises: determining category values for the driving profile for a plurality of vehicle configurations, the category values comprising at least one of an energy consumption and a CO2 emission of the respective vehicle configuration and wherein the energy consumption or the CO2 -ejection can be calculated from the velocity profile and/or acceleration profile.
  • a simulation, a machine learning algorithm, or a combination thereof can be used to calculate the category scores.
  • a category value evaluates a vehicle configuration in relation to the driving profile in terms of a specific category, such as fuel consumption, energy consumption, C02 emissions, etc.
  • the driving profile is used to determine one or more category values for a plurality of vehicle configurations.
  • a corresponding algorithm is used for this purpose, in which a vehicle configuration and the driving profile are entered and which outputs one or more category values.
  • a vehicle configuration determines the structure of the vehicle to be configured, ie which drive is to be used, how is the transmission structured, etc. Wind resistance and/or a body shape can also be contained in the vehicle configuration. It is possible that person is the plural of vehicle configurations, for example via a configurator application operated by the person.
  • the method further comprises: determining an optimal vehicle configuration by determining vehicle configuration values for each vehicle configuration by weighting the category values for each vehicle configuration and choosing an optimal vehicle configuration value.
  • An optimal vehicle configuration is determined from the one or more category values.
  • the category values are weighted to determine a vehicle configuration value.
  • the vehicle configuration value can be used to evaluate a vehicle configuration in relation to the driving profile. It is possible that the person set the weight using the configurator application mentioned above.
  • the optimal vehicle configuration is determined from the vehicle configuration values. This can be the vehicle configuration with the highest or lowest vehicle configuration value.
  • the driving profile is determined using a person's mobile device, which records movement data with internal sensors and the driving profile is generated from the movement data.
  • the mobile device can be a smartphone or tablet.
  • An internal sensor can be a GPS sensor and/or an acceleration sensor.
  • the data determined by the internal sensors of the mobile device can be stored in the mobile device and/or can be transmitted to a server via a data communication network for mobile devices, which evaluates the data and carries out further steps of the method.
  • the mobile device is carried in a vehicle controlled by the person. With this vehicle, the movement profile or at least a part of routes of the driving profile is determined. In particular, the speed values and/or acceleration values of the driving profile can be recorded with a mobile device in this vehicle.
  • the mobile device recognizes whether it is based on the movement data in a controlled vehicle and whether the driving profile is generated solely from the movement data recorded in the vehicle. From acceleration values and/or speed values it can be determined whether the person is walking or is in a vehicle. This means that it can be automatically recognized whether values for the driving profile are to be recorded.
  • the driving profile includes a plurality of routes.
  • a route can be a specific route taken by the person from a starting point to a destination.
  • a route can be traversed several times and/or regularly.
  • the person can use the mobile device to select which of the routes to use to determine the category values. Only certain routes, such as the way to the person's place of work, can be included in the driving profile.
  • a new route can be started each time the vehicle is stationary for a longer period of time and/or the mobile device detects that it is leaving the vehicle or being brought into the vehicle.
  • the mobile device can be designed to record data communication with the vehicle and in this way recognize that it should start recording data for the driving profile and/or a route.
  • the vehicle configuration includes at least one of the following information: a vehicle string, an average fuel consumption, a mass of a selected vehicle, and/or a drag coefficient of a selected vehicle.
  • a vehicle configuration can have a powertrain type (such as electric, petrol, diesel, gas, etc.), an engine type (e.g. wise with different maximum powers), a transmission type (automatic/manual), etc.
  • the vehicle configuration can include a body type from which the drag coefficient can be derived.
  • the vehicle configuration can include information that indicates a fuel or energy consumption of the vehicle to be configured depending on the driving profile.
  • the weights for the category values are at least partly chosen by the person.
  • the person can use the mobile device to choose which category is more important to them than another category, such as C02 emissions, energy consumption, wear and tear, etc. With this choice, the weight for the important category can then be increased.
  • the driving profile includes positions of a route traveled by the person. Not only the speed and/or the acceleration, but also the location of the vehicle can be stored in the driving profile. It can thus be determined from map data whether certain regularly traveled routes are advantageous for a vehicle configuration or not.
  • the driving profile can indicate, for example, that the person usually drives in a built-up area or drives in a mountainous area.
  • gas stations and/or charging stations can also be determined along routes.
  • a category value is determined that takes into account charging stations for electrically operated vehicles or gas stations along the route.
  • the category value for a vehicle configuration with an electric drive can depend on how many charging stations for the drive are available along the routes of the driving profile.
  • the category values for the driving profile are determined by simulating a vehicle driving along the driving profile with the vehicle configuration.
  • an energy Energy consumption and/or C02 emissions of the respective vehicle configuration can be calculated.
  • the driving profile can be broken down into sections and/or routes. Energy consumption and/or CO2 emissions can then be calculated for each section and/or route of the driving profile. Energy consumption and/or CO2 emissions can also be determined for individual routes.
  • the category values can thus be determined by a simulation with which the route that the person has traveled with the vehicle is virtually driven with a vehicle that has the desired vehicle configuration.
  • the driving behavior of the person who is stored in the driving profile can be included.
  • each category value is determined by a machine learning algorithm into which the driving profile is input. It may be that the entire calculation of a category value from the driving profile is performed by a machine learning algorithm.
  • the output of the machine learning algorithm such as an artificial neural network, is one or more category values.
  • the energy consumption or the CO 2 emission is determined as a function of a speed and/or an acceleration using a machine learning algorithm. It may be that the machine learning algorithm only determines an energy consumption and/or a CO2 emission for the respective vehicle configuration for a section and/or a route of the simulated route that the vehicle covers with a vehicle configuration in the simulation.
  • the vehicle configuration values are also determined by the machine learning algorithm, ie the vehicle configuration values are output values of the machine learning algorithm and/or that a category value is already the vehicle configuration value of the vehicle configuration entered into the machine learning algorithm.
  • the machine learning algorithm has been trained with a variety of driving profiles, speeds and/or accelerations and a variety of vehicle configurations for which the category values, energy consumption and/or CO 2 emissions are known. Driving profiles that have already been provided with category values can be stored in a database, for example on the server.
  • the machine learning algorithm something like an artificial neural network, is trained with data from this database in order to then calculate suitable category values, energy consumption and/or CO2 emissions from an unknown driving profile and vehicle configuration.
  • an associated machine learning algorithm is trained for each vehicle configuration.
  • each vehicle configuration can have its own machine learning algorithm.
  • the vehicle configuration can also be entered into the machine learning algorithm.
  • category values, energy consumption and/or CO2 emissions can also be generated for vehicle configurations for which no training data was available.
  • the machine learning algorithm it is also possible for the machine learning algorithm to evaluate the vehicle configurations in relation to the driving profile, i.e. a value is output for each vehicle configuration that evaluates the vehicle configuration as a whole.
  • the method further comprises: determining a vehicle based on the vehicle configuration.
  • the determined vehicle configuration can be inserted into an order for a vehicle, based on which a vehicle is then manufactured that has the vehicle configuration and in particular the corresponding drive train.
  • a computer-readable medium can be a hard disk, a USB storage device, a RAM, a ROM, an EPROM or a FLASH memory.
  • a computer-readable medium can also be a data communication network, such as the Internet, that enables downloading of program code.
  • the computer system may include the mobile device and optionally a server to which the mobile device is connected.
  • a further aspect of the invention relates to a system for determining a vehicle configuration adapted to the driving behavior of a person, which is set up to carry out the method as described herein. It is to be understood that features of the system can also be features of the method or the computer program and the computer-readable medium and vice versa. The system can be the computer system described above. It is also possible that the vehicle with which the driving profile is recorded is part of the system.
  • FIG. 1 schematically shows a system according to an embodiment of the invention.
  • FIG. 2 shows a flow chart for a method according to an embodiment of the invention.
  • FIG. 1 shows a vehicle 10 together with a system 12 comprising a mobile device 14 and a server 16 for determining a vehicle configuration which is adapted to a person's driving behavior.
  • the mobile device 14, such as a mobile phone or tablet computer, is carried in the vehicle 10, which is controlled by the person. ed. While driving, the mobile device 14 records movement data of the vehicle with internal sensors 18, such as a GPS sensor or an acceleration sensor, and sends this data to a server 16, which compiles this data into a driving profile for the person.
  • the mobile device 14 and the server 16 are connected via a mobile data communication network for data exchange.
  • the person can also create multiple vehicle configurations that are sent to the server 16.
  • the server 16 uses the driving profile to determine which vehicle configuration is optimal for the person's driving profile and thus for their driving behavior with regard to one or more categories, such as energy consumption or CO2 emissions.
  • the mobile device 14 can also partially carry out the method steps which are described above and below in connection with the server 16 .
  • FIG. 2 shows a diagram with which a method for determining a vehicle configuration adapted to the driving behavior of the person is illustrated.
  • the method can be performed by a software application on the mobile device 14 in interaction with other software components on the server 16.
  • the software application on the mobile device can be obtained from the person using a digital distribution platform for application software from the operating system manufacturer of the mobile device 14 .
  • the person can also create a user account and use it to register in the software application.
  • step S10 a driving profile 20 of the person is determined with the mobile device 14 .
  • the driving profile 20 includes a speed profile and/or an acceleration profile of the vehicle 10 controlled by the person, which is recorded with the mobile device 14 carried in the vehicle 10 controlled by the person.
  • the driving profile 20 is determined with the person's mobile device 14, which records movement data with internal sensors 18 and wherein the driving profile 20 consists of the Movement data is generated.
  • the movement data can include speeds, accelerations and/or positions.
  • the software application on the mobile device can record position data during a journey and then transmit this to the server 16 for evaluation.
  • a data history can be stored and pre-processed there.
  • the driving profile 20 can include a history of the movement data of trips made by the person.
  • the trips can be recorded using the software application that is installed on the mobile device 14 via the integrated GNSS (Global Navigation Satellite System) receiver of the mobile device 14 .
  • GNSS Global Navigation Satellite System
  • vehicle data can also be integrated to improve the estimates.
  • the mobile device can receive this data via an interface to the vehicle 10 .
  • the mobile device 14 can automatically recognize whether it is located in the controlled vehicle 10 based on the movement data.
  • the driving profile 20 can then only be generated from the movement data recorded in the vehicle 10 . So that the person does not have to start and stop recording the journey themselves every time they drive, the software application can automatically detect when a journey begins and ends and start and stop the recording process. To do this, it is detected whether movement is possible without a vehicle or not in order to start recording, and after a certain period of time without movement, recording is stopped and the journey is completed. This allows the software application to run in the background and requires little attention from the person.
  • the individual trips can also be stored as routes 22 in the driving profile 20 .
  • the person can use the mobile device 14 to select which of the routes 22 will be used for the further process or stored in a database on the server 16 .
  • the recorded position data for example of an individual trip determined as above, can then be combined into a route 22 .
  • These automatically recorded routes 22 can then be presented to the person on the mobile device 14 in an overview. It's possible that the Person must release the routes 22 for further analysis. Routes 22 can also be deleted.
  • step S12 several possible vehicle configurations 24 are determined. This can be done, for example, by the person configuring one or more vehicles with the software application and the server 16 determining possible vehicle configurations 24 for this purpose. However, it is also possible for the following steps to be carried out for all possible vehicle configurations 24 that are stored in the server 16 .
  • the vehicle configuration 24 is a data structure that can contain information about the following characteristics of a configured vehicle: a drive type, a vehicle train, an average fuel consumption, a mass of the selected vehicle, a Cd value of the vehicle, etc. In particular, information about at what speed and/or what acceleration the vehicle consumes how much energy or fuel can be contained in the vehicle configuration 24 .
  • the method should enable the person to find the optimal drive train concept for his driving profile 20 .
  • the properties of a (hybrid) drive train for example ICE (Internal Combustion Engine), FIEV (Hybrid Electric Vehicle), PFIEV (Plug-in Hybrid Electric Vehicle), BEV (Battery Electric Vehicle), hydrogen drive, natural gas drive , can be configured individually, although concepts that currently do not exist can also be configured, such as a hypothetical hybrid drive made of natural gas and hydrogen.
  • vehicle parameters can also be taken into account and/or stored in the vehicle configuration 24 .
  • vehicle configurations 24 are weighted with regard to certain categories. These weights 26 can be at least partially selected for the categories or the associated category values 28 by the person using the software application. For example, the person can whether an electric drive is very important to her and/or whether she also wants to drive in areas that are poorly accessible for electric vehicles. The weights 26 can then be determined from this information.
  • steps S10 and S12 can also be carried out in reverse order.
  • step S14 the server 16 determines category values 28 for the driving profile 20 for a plurality of vehicle configurations 24.
  • Category values 28 are determined at least for energy consumption and/or CO2 emissions for the respective vehicle configuration 24, which were determined in step S12.
  • the energy consumption or the CO2 emissions are calculated from the speed profile and/or acceleration profile that were determined in step S10.
  • Category values 28 can also be determined for other categories, such as cost savings, CO2 savings, charging/refueling comfort, range, etc.
  • a category value 28 evaluates the respective category, for example from 0 to 1, with 0 being “low” or can mean “minimal” and 1 "high” or "maximum”.
  • the category value 28 for a category relating to the driving profile 20 is determined by simulating driving along the driving profile 20 by a vehicle with the vehicle configuration 24 .
  • the respective energy and/or fuel consumption of the vehicle can be calculated from the parameters for the vehicle configuration 24 and the speed and/or acceleration along the trips and/or routes 22 stored in the driving profile 20 .
  • the parameters can be entered into a model of the vehicle, which calculates the respective energy and/or fuel consumption.
  • the energy and/or fuel consumption for a trip and/or route 22 or for the entire driving profile 20 is calculated by adding up these values.
  • the fuel/energy consumption and/or the C02 emissions can be estimated based on the speed and/or acceleration profile during the journey.
  • Data-driven models (such as machine learning) based on historical vehicle data can be used for the assessment.
  • the model can also take into account vehicle parameters such as the specified WLTP (World harmonized Light-duty Vehicles Test Procedure) consumption or the mass and the drag coefficient.
  • the consumption values can also be used to calculate potential cost savings.
  • additional costs/savings through tax benefits costs for vehicle maintenance (e.g. electric vehicles do not require engine oil and have significantly lower brake wear due to the possible recuperation) and also the depreciation can be taken into account.
  • the environmental compatibility can be evaluated via the C02 emissions.
  • the person's individual usage profile which results from the driving profile 20, is not compared with predefined usage profiles; instead, the person's driving behavior is calculated using simulation with the individual framework conditions and possible drive train configurations.
  • a category value 28 can be determined by a machine learning algorithm into which the driving profile 20 is input. It is also possible for the energy consumption or the CO2 emission to be determined as a function of a speed and/or an acceleration using a machine learning algorithm.
  • the model used to determine the energy consumption or CO2 emissions during the simulation can be a machine learning algorithm. It is possible that an associated machine learning algorithm is trained for each vehicle configuration 24 or that the vehicle configuration 24 is additionally entered into the machine learning algorithm.
  • the position data and the cumulative consumption data can also be used to assess the comfort when recharging or refueling. Depending on the energy source, these times differ considerably. Generally can be rated how often the person has to recharge the energy storage device(s) (petrol, diesel, battery, natural gas, hydrogen) in a given period of time.
  • the corresponding category value 28 can be used as a secondary evaluation criterion or weighted less. First-rate or higher weighting can be used to evaluate how high the proportion of journeys or routes 22 is that cannot be driven with a single tank filling/loading and thus cause additional waiting time for the person for refilling. These evaluation criteria can also be weighted with regard to the existing charging infrastructure by comparing the position data from the driving profile 20 with existing map material.
  • a category value 28 can be determined which takes into account charging stations for electrically operated vehicles or filling stations along the route 22 .
  • the availability of charging options can be taken into account. This means that if there are no charging stations in the vicinity of Route 22, then an electric vehicle is less recommended than if the charging infrastructure is better developed. In this way, regional differences can also be included in the calculation.
  • step S16 an optimal vehicle configuration 24 is determined from the category values 28 .
  • a vehicle configuration value 30 is determined by weighting the category values 28 from step S14.
  • An optimal vehicle configuration value 30, for example the largest vehicle configuration value 30, is selected from the vehicle configuration values 30, which evaluate the vehicle configuration 24 overall.
  • a vehicle configuration value 30 may be calculated by multiplying the category values 28 by the associated weights 26 and then summing.
  • the ratings of the categories, ie the category values 28, can then be user-defined weighted and combined into a unitless total value, ie the vehicle configuration value 30, which can be used for decision-making.
  • the goal may be to recommend the powertrain or vehicle that maximizes the person's utility.
  • the software application can then retrieve the results, such as the category values 28 and/or the vehicle configuration value 30, from the server 16 and visualize them on the mobile device 14 for the person.
  • the person can use their mobile device 14 to record their journeys manually or automatically using the GPS function of the mobile device 14 .
  • the data is uploaded to a central database in the server 16 and analyzed there. If a representative database can be assumed, a powertrain configuration is determined from the analyzes carried out. Certain criteria (e.g. profitability, environmental impact) can be weighted differently in the determination.
  • the recommendation is then transmitted again to the mobile device 14 and displayed to the person.
  • the software application on the mobile device 14 or the system 12 can also be used for other options.
  • the person's driving profile 20 can be compared to the average of driving profiles of other people.
  • the created driving profile 20 is compared with other driving profiles 20 in the database. In this way, the person can be given specific feedback on their driving behavior. Anomalies can be shown and suggestions for improving defined criteria can be displayed.
  • the system 12 can still be used sensibly even after a vehicle has been purchased. It can serve as proof for car insurance. This means that the person receives a rate reduction from the insurance company if they record a defined portion of their journeys with the software application. The system 12 can then also provide feedback and recommendations on driving behavior.
  • the software application can also determine the current mileage or the kilometers driven (ie the mileage) of the vehicle without being connected to the vehicle. All you have to do is enter an initial mileage at the beginning or read it out through an interface to the vehicle.
  • the software application can determine the mileage by continuously recording the distances driven.

Landscapes

  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé de détermination d'une configuration de véhicule (24) qui est adaptée au comportement de conduite d'une personne, comprenant les étapes consistant à : déterminer un profil de conduite (20) de la personne, ledit profil de conduite (20) comprenant un profil de vitesse et/ou un profil d'accélération d'un véhicule (10) qui est contrôlé par la personne ; déterminer des valeurs de catégorie (28) pour le profil de conduite (20) pour une pluralité de configurations de véhicule (24), les valeurs de catégorie (28) comprenant au moins l'une des valeurs choisies parmi l'exigence d'énergie et l'émission de CO2 pour chaque configuration de véhicule (24), et l'exigence d'énergie ou l'émission de CO2 étant calculée à partir du profil de vitesse et/ou du profil d'accélération ; et déterminer une configuration de véhicule optimale (24) en déterminant des valeurs de configuration de véhicule (30) pour chaque configuration de véhicule (24) en pondérant les valeurs de catégorie (28) pour chaque configuration de véhicule (24) et en sélectionnant la valeur de configuration de véhicule (30) optimale.
EP22729152.3A 2021-05-21 2022-05-13 Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne Pending EP4341882A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021205236.3A DE102021205236A1 (de) 2021-05-21 2021-05-21 Bestimmen einer an das Fahrverhalten einer Person angepassten Fahrzeugkonfiguration
PCT/EP2022/063028 WO2022243190A1 (fr) 2021-05-21 2022-05-13 Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne

Publications (1)

Publication Number Publication Date
EP4341882A1 true EP4341882A1 (fr) 2024-03-27

Family

ID=82016427

Family Applications (1)

Application Number Title Priority Date Filing Date
EP22729152.3A Pending EP4341882A1 (fr) 2021-05-21 2022-05-13 Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne

Country Status (4)

Country Link
EP (1) EP4341882A1 (fr)
CN (1) CN117355857A (fr)
DE (1) DE102021205236A1 (fr)
WO (1) WO2022243190A1 (fr)

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102009019398A1 (de) 2009-04-29 2010-11-11 Audi Ag Verfahren zum Unterstützen eines Kunden beim Festlegen von Ausstattungsmerkmalen eines Kraftfahrzeugs und Datenverarbeitungseinrichtung
PL2480871T3 (pl) 2009-09-25 2019-10-31 Geotab Inc System, sposób i program komputerowy do symulowania wykorzystania energii pojazdu
US8862346B2 (en) * 2012-03-20 2014-10-14 Eaton Corporation System and method for simulating the performance of a virtual vehicle
SE536699C2 (sv) 2012-10-17 2014-06-03 Scania Cv Ab Systematiskt val av fordonsspecifikation
US20150073933A1 (en) * 2013-09-11 2015-03-12 Ford Global Technologies, Llc Vehicle powertrain selector
DE102016006541B4 (de) 2016-05-27 2019-02-07 Audi Ag Verfahren zum Ermitteln einer Empfehlung für ein anforderungsgerechtes Alternativfahrzeug und zum Übermitteln der Empfehlung an einen Fahrer eines aktuellen Fahrzeugs, Vorrichtung zum Durchführen des Verfahrens und Fahrzeug mit einer solchen Vorrichtung
US20210133808A1 (en) 2016-10-28 2021-05-06 State Farm Mutual Automobile Insurance Company Vehicle identification using driver profiles
US20180349975A1 (en) * 2017-05-30 2018-12-06 Sung Hyun Lee System and method of facilitating consumer-assisted vehicle sales platform
US10937082B2 (en) 2018-12-20 2021-03-02 Toyota Motor North America, Inc. Vehicle recommendation system using sensors

Also Published As

Publication number Publication date
CN117355857A (zh) 2024-01-05
WO2022243190A1 (fr) 2022-11-24
DE102021205236A1 (de) 2022-11-24

Similar Documents

Publication Publication Date Title
EP3137870B1 (fr) Système et méthode pour l'analyse d'efficacité énergétique d'un véhicule
CN104200267B (zh) 一种车辆驾驶经济性评价系统及评价方法
DE102019114590A1 (de) Kraftfahrzeuge mit elektroantrieb, systeme und steuerlogik für vorausschauende ladeplanung und antriebsstrangsteuerung
DE102017119453A1 (de) Energieoptimierte Fahrzeugroutenauswahl
WO2015166069A2 (fr) Système pour évaluer et/ou optimiser le comportement en fonctionnement d'un véhicule
DE102015113701A1 (de) Strecke-bis-Leerzustand-Berechnung auf Routenbasis für ein Fahrzeug
CN106163862A (zh) 具有能源约束的行程规划
DE102018218204A1 (de) Bestimmen eines mindestladezustandes für ein energiespeichermittel eines fahrzeugs
DE112012000447T5 (de) System und Verfahren eines Kraftstoffquantitätsmanagements eines Fahrzeugs
DE102010003760A1 (de) Verfahren sowie Einrichtung für ein Reichweitenmanagement eines Fahrzeugs mit elektromotorischem Antrieb
DE102020119861A1 (de) Verfahren und Assistenzeinrichtung zum iterativ optimierten Betreiben eines Kraftfahrzeugs und Kraftfahrzeug
DE102013002240B3 (de) Verfahren und Vorrichtung zur Reichweitenkennzeichnung für ein Fahrzeug
CN109849817A (zh) 一种共享电动汽车用户免充电、快速换车系统及方法
CN115587678A (zh) 商用车智能地图出行方法和系统
EP3639246A1 (fr) Procédé d'optimisation de la conduite d'un véhicule automobile sur une voie de circulation
DE102018219621A1 (de) Vorrichtung und verfahren zum bestimmen von fahrzeugleistungsfaktoren
DE102016206802A1 (de) Verfahren, Vorrichtung und mobiles Anwendergerät zur Anpassung einer Energieversorgung eines Antriebssystems eines Fahrzeugs
WO2018202875A1 (fr) Unité d'affichage pour un véhicule hybride
EP4341882A1 (fr) Détermination d'une configuration de véhicule qui est adaptée au comportement de conduite d'une personne
DE102017213209A1 (de) Berücksichtigung der Luftgüte im Fahrzeug
DE102014002998A1 (de) Verfahren zur Ermittlung der Reichweite eines Kraftfahrzeugs
DE102020107537A1 (de) Verfahren und System zum Betreiben eines Kraftfahrzeugs
EP3956636B1 (fr) Procédé et dispositif de calcul d'une consommation d'énergie d'un véhicule automobile sur un itinéraire
DE102011055669A1 (de) Verfahren zur Prognose einer Reichweite
EP3999808A1 (fr) Procédé permettant de guider un véhicule à moteur

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20231110

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR