EP3485402A1 - Procédé d'analyse d'un véhicule automobile sur la base d'une simulation - Google Patents

Procédé d'analyse d'un véhicule automobile sur la base d'une simulation

Info

Publication number
EP3485402A1
EP3485402A1 EP17737818.9A EP17737818A EP3485402A1 EP 3485402 A1 EP3485402 A1 EP 3485402A1 EP 17737818 A EP17737818 A EP 17737818A EP 3485402 A1 EP3485402 A1 EP 3485402A1
Authority
EP
European Patent Office
Prior art keywords
function
parameter
motor vehicle
simulated
values
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
EP17737818.9A
Other languages
German (de)
English (en)
Inventor
Mario OSWALD
Peter Schoeggl
Erik Bogner
Robert Schuh
Moritz Stockmeier
Volker Mueller
Mario TEITZER
Thomas GERSTORFER
Hans-Michael Koegeler
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.)
AVL List GmbH
Original Assignee
AVL List GmbH
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 AVL List GmbH filed Critical AVL List GmbH
Publication of EP3485402A1 publication Critical patent/EP3485402A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/188—Controlling power parameters of the driveline, e.g. determining the required power
    • B60W30/1882—Controlling power parameters of the driveline, e.g. determining the required power characterised by the working point of the engine, e.g. by using engine output chart
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0018—Method for the design of a control system
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B60W2050/0039—Mathematical models of vehicle sub-units of the propulsion unit
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/22—Yield analysis or yield optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]

Definitions

  • the invention relates to a method for simulation-based analysis and / or optimization of a motor vehicle, wherein a driving operation of the motor vehicle is simulated on the basis of a model, a driving state parameter is determined, which with respect to one or more values of at least one simulated size and / or at least a variable is defined and is suitable to characterize at least one driving condition, in particular a driving condition, of the motor vehicle.
  • Document EP 0 846 945 B1 discloses a method for analyzing the driving behavior of motor vehicles with the following steps:
  • Document EP 1 623 284 A1 discloses a method for optimizing vehicles and engines for driving such vehicles by the following steps:
  • a first aspect of the invention relates to a method for simulation-based analysis and / or optimization of a motor vehicle, preferably comprising the following steps i o:
  • a driving condition parameter that is defined with respect to one or more values of at least one simulated variable and / or at least one variable and that is capable of characterizing at least one driving condition, in particular a driving condition, of the motor vehicle;
  • a second aspect of the invention relates to a system for simulation-based analysis and / or optimization of a motor vehicle, which is set up to carry out a method according to the first aspect of the invention and / or preferably comprises the following means:
  • a model with at least one manipulated variable for obtaining values at least one simulated variable which is suitable for characterizing overall vehicle behavior, in particular drivability, of the motor vehicle the model having at least one submodel, and wherein the at least one submodel is based on a function and preferably the operation of at least one component, in particular an internal combustion engine, of the motor vehicle characterized; and means configured to determine a driving-state parameter which is defined by values of at least one simulated variable and / or at least one manipulated variable and capable of characterizing at least one driving operating state, in particular a driving state, of the motor vehicle;
  • Means arranged to output the values of the at least one simulated variable, suitable for characterizing the overall vehicle behavior, in conjunction with the respective associated driving operating state parameter.
  • the means are associated with a first and a second module, which are connected via a first data interface.
  • a third aspect of the invention relates to a method for simulation-based analysis and / or optimization of a motor vehicle, comprising the following working steps:
  • a fourth aspect of the invention relates to a system for simulation-based analysis and / or optimization of a motor vehicle, which is set up to carry out a method according to the invention and / or comprises:
  • Means arranged to output the values of the at least one simulated variable.
  • the means are associated with a first and a second module, which are connected via a first data interface.
  • a variable in the sense of the invention is a variable of a simulation, which in particular has at least one actuating or input variable and at least one simulated "measured variable.”
  • a variable is a physical variable.
  • a condition in the sense of the invention is one or more constellations of values of several sizes and / or a progression of values of one or more variables.
  • a model in the sense of the invention is a, in particular simplified, image of reality.
  • An inventive model may also be referred to as a replacement model for the operation of a device.
  • this may in turn have submodels and / or submodels that model individual components of the device.
  • such a model can have a map-based model and / or a function-based model, in particular as partial models.
  • a map is stored, which assigns values of an input variable to values of an output variable.
  • a function is stored with function parameters or coefficients and variables to which values of input variables assign values of output variables.
  • a function parameter or parameters of a function within the meaning of the invention is a so-called shape variable, which is constant for the case just considered, but can be varied for the next case.
  • a function parameter is a single value or a single function and can in particular represent a coefficient.
  • Output within the meaning of the invention is a provision for further processing, in particular in the method or in another method, or a reproduction by a user interface.
  • a total vehicle behavior in the sense of the invention is at least one property of the vehicle in motion or driving, in particular selected from the following group: function of the assistance systems, security feeling, ride comfort, agility, driveability, emission, efficiency, NVH comfort, revving of the Motor vehicle / in relation to the motor vehicle, in particular in a holistic context.
  • Driveability in the sense of the invention is a behavior of a motor vehicle in transient operating states, which is caused by an action of a driver or driver assistance system.
  • a driving condition in the sense of the invention characterizes the operation of a motor vehicle at a time or over a period of time and may be a driving condition in a simple form.
  • a driving condition is an overall operating condition of the vehicle which characterizes the driving condition as well as the operating condition of the aggregates and accessories used for the propulsion of the vehicle.
  • An operating state within the meaning of the invention is any possibility of operating a device.
  • an internal combustion engine operating condition preferably means both an operation of the internal combustion engine in a stationary state, i. For example, idling operation or constant speed and constant load vehicle operation, as well as steady state, i.e., transient, operation.
  • an acceleration of the internal combustion engine preferably both a snapshot of a constellation of values of the variables and, alternatively, a time profile of values of the variables, for example the accelerator pedal position, or alternatively this is also represented by an initial and end point of the values of variables, for example by velocity values a predetermined opening degree of the throttle valve defined.
  • a value in the sense of the invention is a number, a constellation of numbers or an expression.
  • Operating behavior in the sense of the invention is a sequence of operating states.
  • a driving state in the sense of the invention characterizes a dynamics of a motor vehicle in motion or while driving.
  • driving conditions are preferably starting process of an internal combustion engine when driving (v Kra ft poverty> 0), acceleration, tip-in, tip-out, slow down, changing gears, sliding at a constant speed, sailing, idling when driving (v Kr aft poverty> 0) engine stop when driving (v Kr aft poverty> 0).
  • a driving condition can also be subdivided finer in under-driving conditions. In the extreme case, each combination of values of the variables is assigned an underrun condition.
  • these are preferably stationary and transient or transient states of the driving operation, which designate the transition from a first stationary driving state to a second stationary driving state.
  • An efficiency within the meaning of the invention is a measure of the energy required to achieve a defined benefit.
  • a process is efficient when a particular benefit is achieved with minimal energy input.
  • an efficiency is at least a component of the efficiency.
  • An emission behavior within the meaning of the invention is a course of the emissions over a predefined period of time or a profile of the emissions over a predetermined distance, time and distance being coupled in particular via a speed profile.
  • a module according to the invention is a building block or component of the system according to the invention. Individual modules can be technically implemented as hardware and / or software and are connected via interfaces.
  • a means in the sense of the invention may be designed as hardware and / or software, in particular one, preferably with a memory and / or bus relating.
  • the CPU may be selected to execute instructions implemented as a program stored in a memory system, to capture input signals from a data bus, and / or to output signals to a data bus.
  • a storage system may comprise one or more, in particular different, storage media, in particular optical, magnetic, fixed and / or other non-volatile media.
  • the program may be such that it is capable of embodying or executing the methods described herein so that the CPU may perform the steps of such methods and, in particular, control and / or monitor a reciprocating engine.
  • a motor vehicle in the sense of the invention is a vehicle operated by a drive.
  • a motor vehicle is in particular a land vehicle, watercraft or aircraft. Preferably, this is a passenger car, truck, bus or motorcycle.
  • the invention is based in particular on the approach of making the overall vehicle behavior, in particular the drivability of the motor vehicle, analyzable and optimizable in a pure simulation.
  • the driving operation of the motor vehicle is in this case simulated by a model, so that neither measurements are carried out on a real motor vehicle nor on a test bench in order to obtain values of variables to be observed.
  • the model which depicts the motor vehicle is either a completely function-based model or, at least for that component which is to be designed in one process run, has a function-based submodel which images this component.
  • other components of the vehicle may also be included as a map-based submodel in the model to perform the simulation.
  • the values of the simulated variable are preferably set in relation to a driving operating state, in particular a driving state, of the motor vehicle. Therefore, according to the invention, a driving mode parameter is determined on the basis of the variables simulated by means of the model. In this way, statements about the overall vehicle behavior can be additionally improved.
  • Values of the simulated variables and, if appropriate, of the respective driving mode parameter are then preferably output, for example in order to be displayed to a user via a user interface or provided by a modeling algorithm which changes the model, in particular the function-based components of the model. in particular, can further optimize.
  • models of individual components or of an entire motor vehicle can be optimized with respect to certain criteria under specification of boundary conditions. Based on the model obtained, the individual components or the entire vehicle can then be designed.
  • the same values or value constellations of variables can be differently evaluated as a function of the respective driving operating state parameter.
  • the Fahr istssstands parameter is defined by at least one predetermined condition with respect to the at least one control variable and / or at least one simulated size.
  • the checking of a condition makes it possible to predefine driving conditions or driving conditions and to output a driving condition parameter only when such a predefined driving condition or driving condition is actually present.
  • the method is preferably continued only when a predefined driving condition or driving condition exists, in particular, only in this case, an evaluation parameter is calculated. As a result, the occupied storage capacity and computing capacity of a data processing device can be saved.
  • the step of simulating occurs for points of variation of a test plan, in particular a statistical test plan, which predefine the values of the input variables or manipulated variables of the model.
  • a statistical design which can be generated by known mathematical methods, allows a substantial reduction of the number of variation points, which are necessary for a simulation of the driving operation of the motor vehicle, in particular with respect to a so-called raster measurement.
  • the system according to the invention comprises: Means arranged to determine at least one evaluation parameter indicating the overall vehicle behavior of the motor vehicle on the basis of an assignment rule, in particular a function, in dependence on the at least one output simulated variable and the driving condition parameter and wherein the means for outputting further set are to output the at least one rating parameter.
  • the ZuOrd Vietnamese, Vietnamese, etc. may be hereby preferably based on generally accepted contexts or on dependencies, which were set up on the basis of one or preferably a plurality of reference vehicles.
  • the ZuOrd Vietnamesesvorschrift can take into account subjective feelings of a vehicle occupant, resulting from different constellations of simulated size and Fahr istssstands parameters.
  • the simulated variables are differently assessed depending on the driving operating state parameter, ie in particular the present driving state, in order to determine the evaluation parameter which indicates the overall vehicle behavior of the vehicle.
  • the driving operating state parameter ie in particular the present driving state
  • the evaluation parameter which indicates the overall vehicle behavior of the vehicle.
  • the conversion of the simulated variable as a function of the driving state parameter into a, preferably objective, evaluation parameter is relatively simple. This is the case, for example, for the emission and efficiency criteria.
  • a training phase for the second module of the system is provided for this purpose, which is at least substantially similar to the system training described in the aforementioned EP 0 846 945 B1.
  • a vehicle occupant in particular a test driver, is subjected to a test operation in the real vehicle, whereby no predefined driving cycle has to be maintained per se.
  • the drive cycle performed in the experiment essentially corresponds to a normal driving operation.
  • Drive-related and vehicle-related data are preferably recorded as time series during the driving operation.
  • Drive-related data are in particular engine speed, engine torque, requested power, in an internal combustion engine, in particular throttle or accelerator pedal position, intake manifold vacuum, coolant temperature, ignition timing, injection quantity, lambda value, exhaust gas recirculation rate and exhaust gas temperature.
  • Vehicle-related data are, in particular, vehicle speed, vehicle longitudinal acceleration, vehicle lateral acceleration, rolling, rolling, pitching.
  • different driving states in particular via the output of a driving state parameter, become known on the basis of previously defined conditions.
  • tip-in can be defined as a driving condition in which a sudden opening of the throttle valve occurs from a low-speed and a low-load condition
  • Conditions are defined for each driving condition to be differentiated, in this case for the measured quantities whose occurrence is based on the existence of the driving condition defined by these conditions.
  • the conditions are identical to those conditions which later determine the driving operating state parameter or the driving operating states in the method according to the invention.
  • a rating parameter is preferably defined based on one or more measured quantities.
  • test persons are preferably asked about the overall vehicle behavior of the vehicle.
  • the score parameter is then preferably set or correlated to best reflect the score by the subject or persons.
  • statements of several test subjects about the overall driving behavior of the motor vehicle are preferably evaluated by statistical means.
  • a ZuOrdnungsvorschrift obtained in this way can preferably be used to determine the evaluation parameter.
  • the system according to the invention comprises:
  • Means arranged for matching the values of at least one simulated variable for the respective driving condition parameter or the value of the at least one evaluation parameter with a predetermined setpoint range, in particular with target values for a design of the motor vehicle;
  • This advantageous embodiment of the method according to the invention makes it possible to optimize the function used for simulating or the submodel of a component of the motor vehicle with respect to the criteria relevant to the overall vehicle behavior, in particular automatically.
  • the values of the simulated variables are compared with a nominal value range, which was determined for example on the basis of a reference vehicle, taking into account the respectively present value of the driving-state parameter.
  • an evaluation parameter in which an objectified evaluation is already included can be compared with a setpoint range for this evaluation parameter.
  • the respective desired value range here represents target values for the design of the at least one component of the motor vehicle.
  • the function used for simulating or the submodel of the component of the motor vehicle is changed, in particular by modifying function parameters or coefficients of the function of the at least one submodel used for simulating.
  • optimization algorithms are used, as they are known in the art.
  • the values of the at least one simulated variable as a function of the driving operating state parameter and / or the values of the evaluation parameter and the function parameters or coefficients of the function used for the simulation enter as variables.
  • the dependence of the variables is then mapped by polynomial models and optionally extended by various types of neural network algorithms, in particular by interrelationships between individual polynomial models.
  • these model algorithms indicate not only local models but global models.
  • the global model algorithms then describe the behavior of the component to be designed over the entire operating range as a function of the function parameters of the simulated function.
  • a (further) experimental design is created which specifies variation points with respect to the function parameters of the function or functions used for simulation.
  • the model algorithm used for the optimization can enter into other or further boundary conditions than the criterion of the overall vehicle behavior, for example statutory safety specifications, for example minimum distances in road traffic or statutory emission specifications.
  • the function of the partial model of the vehicle component used for simulation is output, in particular an indication is given that the overall vehicle behavior of the vehicle Motor vehicle with the function used for simulating the predetermined target values met.
  • the component of the motor vehicle represented by the function-based submodel may be designed in such a way that the operation of the component reflects the function used for simulation.
  • the simulation-based methodology according to the invention makes it possible to predict design-relevant overall vehicle properties sufficiently accurately in the concept and design phase of a new motor vehicle in which no vehicle test vehicles to be tested are available at a very early stage.
  • an optimization algorithm in particular in connection with a design target metric, ie an evaluation parameter, a hardware specification for design-relevant motor vehicle and engine components are generated.
  • the simulation-based method according to the invention can be implemented in that map-based models, which are normally used to simulate a motor vehicle or its components, are at least partially replaced by function-based models.
  • the engine torque currently applied to the crankshaft which in a map-based model is normally specified in each simulation time step as a function of load or accelerator pedal position and engine speed, is represented according to the invention by a corresponding function-based model.
  • the stationary and transient torque characteristics of an internal combustion engine in particular a supercharged internal combustion engine, can be mapped via mathematical functions with few functional parameters.
  • transient driving conditions of the motor vehicle such as full load acceleration, low-end torque, tip-in (positive load change), tip-out (negative load change), acceleration, etc., simulate sufficiently accurate.
  • the modification takes place on the basis of an optimization algorithm and the at least one function parameter of the function used for simulating in the optimization algorithm is used as the manipulated variable of the component or of the motor vehicle, in particular the single actuator. Size or sizes, treated.
  • the inventive system comprises means adapted to generate a further test plan having variation points with respect to the at least one function parameter of the function to be simulated, in particular based on an optimization algorithm, wherein the step simulating on the basis of the further experimental plan.
  • the method according to the invention relate in particular to an embodiment in which the component is a drive device, in particular an internal combustion engine. Accordingly, the method according to the invention furthermore has the following advantageous embodiments:
  • the submodel is a torque model of the drive device, in particular an internal combustion engine, of the motor vehicle, wherein the submodel has at least one of the following submodels:
  • Partial load model based on a partial load function
  • a torque gradient model based on a torque gradient function
  • Saugmomentmodell which is based on a Saugmoment function.
  • the torque model has at least the full-load model and the full-load function describes a full-load characteristic curve through three sub-functions:
  • the full-load characteristic in particular the full-load characteristic curve of an internal combustion engine, is not completely differentiable, but has kinks, so that it can preferably be represented completely completely by splitting it into three different functional areas. This is inventively achieved by the areas of low speed, average speed and maximum power, wherein at low speed and average speed, the full load function is approximated by the torque, while preferably at maximum power, the function is approximated via the power curve.
  • the functions for each subarea may preferably be defined by only two function parameters.
  • the part-load function is calculated on the basis of the full-load function and a pedal characteristic function, which specifies a relationship between the variable torque and the variable "pedal or throttle position.”
  • This function can also only be defined via two function parameters.
  • the pedal characteristic function has a first function parameter and a second function parameter, both of which are rotational speed-dependent and wherein the first function parameter indicates a factor and the second function parameter specifies an offset. This results in a particularly simple structure of the pedal characteristic function, which simplifies the use in an optimization algorithm.
  • the torque gradient function has a linear and a cubic component, wherein a function parameter indicates the direction of the linear and the cubic component.
  • the torque gradient function can thus also be described with only three function parameters.
  • a small number of function parameters also means a small number of variables to be changed in a polynomial model of an optimization algorithm. In this way, the number of variation points of a test plan can be kept as low as possible even by the modeling.
  • the at least one submodule indicates in each case the stationary and / or the transient operation of the at least one component.
  • the at least one component is an internal combustion engine, a charging system, a steering system, a drive train, a chassis system, a transmission system or a driver assistance system.
  • one or more, in particular all, steps of the method are completely or partially automated, in particular by the system or its means. Further advantageous embodiments, in particular with regard to the third and fourth aspects of the invention, are given below:
  • the method further comprises the following operation:
  • a driving state parameter which is defined with respect to one or more values of at least one simulated variable and / or at least one manipulated variable and is suitable for characterizing at least one driving operating state, in particular a driving state, of the motor vehicle; wherein the values of the at least one simulated variable are output in connection with the respectively associated driving operating state parameter.
  • the submodel which is based on a function, has at least one function parameter, by means of which the simulated driving operation of the motor vehicle can be changed.
  • the Fahr istssstands- parameter is defined by at least one predetermined condition with respect to the at least one control variable and / or at least one simulated size.
  • the step of simulating occurs for points of variation of a test plan, in particular a statistical test plan.
  • the method further comprises the following steps:
  • the method further comprises the following steps:
  • the method further comprises the following operation:
  • the method further comprises the following steps:
  • the modification takes place on the basis of an optimization algorithm and the at least one function parameter of the function used for simulating in the optimization algorithm is used as a control variable of the component or of the motor vehicle, in particular single control variable or sizes, treated.
  • the method further comprises the following operation:
  • the method further comprises the following operation:
  • the at least one partial model characterizes a device of the vehicle, further comprising the following working steps:
  • the at least one submodel is a torque model of a drive device, in particular an internal combustion engine, of the motor vehicle, and the submodel has at least one of the following submodels:
  • Torque gradient model based on a torque gradient function
  • the torque model has at least the full-load model and the full-load function describes a full-load characteristic through three sub-functions:
  • the partial load function is calculated on the basis of the full load function and a pedal characteristic function which a relationship between a variable "torque" and a variable "pedal or throttle position".
  • the pedal characteristic function has a first function parameter and a second function parameter, which are both speed-dependent, and wherein the first function parameter indicates a factor and the second function parameter specifies an offset.
  • the Drehmomentgradienten- has a linear and a cubic portion, wherein a function parameter indicating the weighting of the linear and the cubic portion.
  • the driving-state parameter and / or the evaluation parameter are determined as a function of a vehicle parameter, preferably mass and / or engine characteristic of the motor vehicle, in particular maximum power, maximum torque and / or maximum rotational speed.
  • the model as a further partial model on a vehicle model, which is adapted to at least partially characterize a driving behavior of the motor vehicle.
  • the method further comprises the following operation:
  • the second module further comprises: Means configured to determine a driving-state parameter which is defined with regard to one or more values of at least one simulated variable and / or at least one manipulated variable and is suitable for characterizing at least one driving operating state, in particular a driving state, of the motor vehicle, wherein the values of the at least one simulated variable are output in connection with the respectively associated driving operating state parameter.
  • the submodel which is based on a function, has at least one function parameter, by means of which the simulated driving operation of the motor vehicle can be changed.
  • the first data interface is set up to provide vehicle parameters, values of the at least one manipulated variable and / or the at least one simulated variable from the first module to the second module and values of a function parameter and variation points of to provide the second module to the first module.
  • the second module further comprises:
  • Means arranged to determine at least one evaluation parameter indicating the overall vehicle behavior of the motor vehicle on the basis of an assignment rule, in particular a function, as a function of the at least one output simulated variable and / or the driving mode parameter and wherein the means for outputting are further configured to output the at least one evaluation parameter.
  • the second module further comprises: - Means for providing a first specifications with respect to the driving operation of the motor vehicle, which at least one setpoint range of at least one simulated size, in particular for the respective Fahr istssstands- parameter, or the value of the at least one evaluation parameter, which target values for criteria for interpretation of the motor vehicle, preferably a total vehicle behavior, further preferably a drivability, correspond.
  • the second module further comprises:
  • the second module further comprises:
  • Means adapted to alter at least one function parameter of the function of the at least one submodel used for the simulation on the basis of the alignment, if the values of the at least one output simulated variable are outside the setpoint range, preferably the operation step again the driving is simulated; and Means for outputting a value of the at least one function parameter if the values of the at least one output simulated quantity are within the setpoint range.
  • system further comprises a third module, wherein the third module is connected to the second module via a second data interface and to the first module via a third data interface and comprises:
  • the second data interface is set up to provide values of the evaluation parameter from the second module to the third module and the third data interface is set up to provide values of the function parameter and variation points from the third module to the first module.
  • the second module or the third module further comprises:
  • Means arranged for generating a test plan, which has variation points with regard to the at least one function parameter of the function to be used for simulating, in particular on the basis an optimization algorithm, wherein a step of simulating is based on the further design plan.
  • Figure 1 shows a first embodiment of the system according to the invention
  • Figure 2 shows a second embodiment of the system according to the invention
  • FIGS. 3 to 6 show a subdivision of a full load characteristic curve according to the invention
  • FIG. 7 shows a full load characteristic curve with an applied proximity function
  • Figure 8 is a torque gradient approximation function
  • Figs. 9 and 10 show a pedal characteristic approximation function of a partial load model
  • Figure 11 is a full load characteristic with applied partial load approximation function
  • FIG. 12 shows a flow chart of an exemplary embodiment of the invention
  • a first exemplary embodiment of the system 10 according to the invention for simulation-based analysis and / or optimization of a motor vehicle and an associated method 100 according to the invention are explained below with reference to FIGS. 1 and 12.
  • the system 10 preferably has three modules 1 1, 12, 13, which are each connected via data interfaces for data transmission.
  • data is transferred from the first module 1 1 to the second module 12 via a first data interface 14, from the second module 12 to the third module 13 via a second data interface 15 and the third module 13 in turn to the first module 1 1 via a third data interface 16.
  • a model M is deposited, with which the driving operation of a motor vehicle 1 can be simulated.
  • This model M can, as indicated by the arrow, read or written via an interface from the outside into the module 1 1.
  • the model M can also be read out of the module 1 1 again.
  • a predetermined driving cycle is stored in the first module 11, which represents a sequence of driving operating states for the motor vehicle 1.
  • This driving cycle is preferably created on the basis of the experience of test engineers and includes load points, which experience has shown that are required for calibrating a motor vehicle 1, in the present exemplary embodiment of the drive or the internal combustion engine of the motor vehicle 1.
  • the model M has a partial model.
  • This submodel is function-based, that is, it is described by a function having function parameters, in particular coefficients, and variables, in particular manipulated variables, such as the speed and the gas pedal position.
  • the function reproduces an assignment rule which continuously assigns a value of one or more simulated variables to a constellation of values of manipulated variables.
  • further submodels of the model M describe the function of further components of the vehicle. If these further components are not likewise to be analyzed or optimized by the method 100 according to the invention, then the further submodels may preferably be map-based, ie the assignment rule inherent in these submodels is not stored as a function but as a map; which discretely assigns a value of one or more simulated quantities to a constellation of values of manipulated variables in each simulation time step.
  • the model M as a further submodel can have a vehicle model which is set up to simulate a driving behavior of the motor vehicle 1.
  • vehicle parameters which enter into such a vehicle model are, in particular, the weight and / or the dimension or the center of gravity of the vehicle.
  • a vehicle model is created in a manner known per se in order to be able to simulate a driving operation.
  • the first module 12 preferably simulates a two or more mass oscillator to reflect the mass of the motor vehicle 1, the rigidity of the drive train and the transmission behavior of the tires. Further preferably, the damping can also be simulated on the basis of the vehicle model.
  • the attenuation values depend in particular on the operating state of the vehicle.
  • such a vehicle model may have further submodels, such as a tire model, a suspension model (spring / damper), a chassis geometry model, a resistance model, a steering model, a clutch model, a transmission model and / or a model of elasticity (multi-mass system or multi-mass oscillator).
  • a tire model such as a tire model, a suspension model (spring / damper), a chassis geometry model, a resistance model, a steering model, a clutch model, a transmission model and / or a model of elasticity (multi-mass system or multi-mass oscillator).
  • values of simulated quantities of the model M are generated by executing the predetermined driving cycle with the vehicle 1 characterized by the model M.
  • the initial values of the function parameters or coefficients of the function to be used for simulating are preferably selected on the basis of the experience of a test engineer. Further preferably, these may also initially be set by the third module 13, as described below with respect to later iterations, preferably in the form of variation points of an experimental design.
  • Such a simulation of the driving operation of the motor vehicle 1 can be carried out in particular with the Applicant's system AVL VSM TM.
  • At least one of the simulated variables is suitable here, in particular in interaction with other simulated variables, in order to characterize an overall vehicle behavior of the vehicle 1 or to carry out an evaluation of the overall vehicle behavior on the basis of this variable.
  • the overall vehicle behavior includes at least driveability.
  • the overall vehicle behavior, in particular the drivability serves as a criterion for the design of the vehicle 1
  • the simulated variables are preferably forwarded to the second module 12.
  • the second module 12 is preferably able to check the values of the simulated variables for the presence of a predetermined condition S102.
  • a condition is in particular a constellation of the values of several simulated variables and / or the course of values of one or more variables. If such a condition is met, the second module 12 determines a driving mode and determines a driving condition parameter for this purpose.
  • the driving condition parameter is defined to one or more values of at least one simulated variable or at least one manipulated variable, but does not represent a separate value, but is essentially an association of values of at least one simulated quantity characterizing the overall vehicle behavior of the vehicle. to the values of at least one simulated quantity and / or at least one manipulated variable, which characterize the driving operating state.
  • variables of the model M can be used to define the driving condition parameter.
  • An example of this is the gas pedal or throttle valve position, the value of which can be used to conclude a driving operating state.
  • the driving condition parameter here is preferably a numerical value or a constellation of numerical values or also defined by a term which is assigned to the value or the values.
  • a database may be provided in the second module 12, on the basis of which the current driving operating state can be determined on the basis of the driving operating state parameter by adjusting values of the simulated and / or manipulated variables.
  • the data exchange between the first module 1 1 and the second module 12 takes place here preferably via the first interface 14, which may be software and / or hardware.
  • the values of the at least one simulated variable, which characterizes the overall vehicle behavior are output in conjunction with the respective driving operating state parameter.
  • the values are preferably output directly to a third module 13, which in particular serves for applying an optimization algorithm to the results obtained during simulation.
  • the values are output to an evaluation algorithm within the second module 12, with which the simulated variables determined for the motor vehicle 1, which express the overall vehicle behavior, preferably objectified, can be evaluated.
  • a ZuOrdnungsvor arrangement between at least one simulated size, by which the overall vehicle behavior is characterized, and the Fahr istssstands parameter to the evaluation parameter is used, in which the evaluation of one or a plurality of vehicles by human test riders, in particular with respect to reference vehicles , comes in.
  • the establishment of such a ZuOrd Vietnamesesvorschrift for ordering a rating parameter as a function of a simulated size, by which the overall vehicle behavior is characterized will be explained with reference to an embodiment in relation to the driving condition in the following.
  • it is a tip-in in second gear, so an acceleration process with increasing throttle opening.
  • the throttle position, the engine speed and the longitudinal acceleration are first measured in a time-dependent manner for a driving operating state tip-in.
  • the subject's subjective sensations are captured, for example, by the subject inputting their subjective sensation through a user interface assessment.
  • an evaluation of the rotational speed n and the longitudinal acceleration is performed.
  • a Fast Fourier Transformation (FFT) of the rotational speed n and the longitudinal acceleration is calculated.
  • a maximum value of bucking oscillation in the frequency range between 2 and 8 hertz and the frequency at which the maximum value occurs are preferably calculated according to the following equation:
  • st represents the imaginary part
  • a (t) the time course of the acceleration.
  • c1, c2 and c3 are parameters, a osc the maximum value of the bucking oscillation in the range of 2 to 8 hertz and Dr the calculated evaluation parameter, in the present Case a so-called driveability index for the criterion driveability.
  • the parameters c1, c2 and c3 can preferably be found automatically in a self-learning system. Preferably iteration loops are used for this, in which the parameters are changed until the deviation between the authorized value Dr and the subjective assessment of the subject Dr subjectively becomes a minimum. This is done according to the following equations:
  • the expressions p ,, q, and n represent variation step sizes .
  • the variation of c1, c2 and c3 is carried out until the difference between the calculated evaluation parameter Dr and the subjective evaluation parameter Drsubj is smaller than a predefined limit value is.
  • the subjective judgment in the vehicle can be completely simulated from the amplitudes a osc of the bucking vibration.
  • the found parameters c1, c2, c3 form the subjective assessment.
  • the illustrated embodiment for setting the ZuOrdnungsvorschrift for the evaluation parameter is just one of many ways to create this ZuOrdungsvorschrift.
  • the iteration can also be performed with other methods known from mathematics or statistics.
  • the ZuOrdnungsvorschrift also be a comparison of a simulated size with a setpoint range.
  • the setpoint range in this case corresponds to target values for a criterion. For example, a certain fuel consumption over a target value range could be specified as the target value.
  • the fuel consumption which is simulated on the basis of the model M, can then be compared with the setpoint range and thus determined whether an overshoot or undershoot is present and makes a change of the model M or the function parameters contained therein necessary.
  • the optimization algorithm is preferably provided in the second module 12, which then directly from the simulated size, in this case the simulated consumption, to the target value range as the target value, a new design plan or a set of new variation points of the functional Created parameters.
  • the evaluation parameter in this case is the consumption.
  • the simulation is then again carried out in the first module 1 1 using the new functional parameters S101.
  • Such a ratio of a simulated fuel consumption as a simulated quantity and the associated target value range as target values could also be converted into a rating which is represented by a numerical value, in particular a note, or a term ("too low", "too high", "
  • This evaluation parameter which is preferably calculated by the second module 12, is then preferably output to the third module 13 via the second interface 15, which in this case is deposited in the third module 13
  • Optimization algorithm then calculates, in particular on the basis of a test plan, variation points of the function parameter in order to optimize the consumption.
  • the second module 12 vehicle parameters are provided with respect to the simulated by the first module 1 1 motor vehicle 1. These are preferably the mass and the engine characteristic, in particular maximum power, maximum torque, rotational speed at maximum power, rotational speed at maximum torque and maximum rotational speed of the simulated motor vehicle 1. Further preferably, these data are transmitted from the first module 1 1 via the first data interface 14 to the second module 12.
  • the rating parameter may not only contain a single rating, but may consist of multiple ratings.
  • an overall rating for full load acceleration is, for example, a maximum expected torque, a 90% torque threshold, a 90% torque range, a torque fill, a spin, an expected one Acceleration and a reference acceleration.
  • the second module 12 can be given a multiplicity of criteria by means of which the evaluation or the determination of the evaluation parameter S104 for the overall vehicle behavior of the motor vehicle 1 is to be carried out. Examples of such criteria are, for example, drivability, agility, ride comfort, emission, efficiency, NVH comfort, driving pleasure, sense of security and function of the driver assistance systems. A particularly accurate assessment of overall vehicle behavior can be achieved by evaluating these criteria in a holistic context.
  • a value of the evaluation parameter determined by means of the assignment rule is then output from the second module 12 via a second data interface 15 to the third module 13, as an alternative to at least one simulated variable in connection with the driving operating state parameter S105.
  • the evaluation parameter can also be output via a user interface.
  • an optimization algorithm for improving the evaluation of the overall vehicle behavior is preferably carried out.
  • function parameters or coefficients of the function of the at least one submodel of the model M enter as variables. These variables are varied according to the optimization algorithm in order to optimize the evaluation parameter or the one or more evaluation criteria S107.
  • the criteria of the evaluation can preferably be weighted differently here.
  • boundary conditions enter into the optimization algorithm. These may be, for example, properties that were not taken into account in the evaluation in the second module 12. Such boundary conditions may be, for example, a desired torque or a desired performance or even boundary conditions that do not characterize the overall vehicle behavior of the motor vehicle 1, but which are, for example, safety-relevant or prescribed by law by law.
  • the Overall vehicle behavior has already reached a desired rating S106.
  • values of the at least one simulated variable by means of which the overall vehicle behavior can be characterized are compared for the respective, associated driving operating state parameters with a desired value range, in particular with target values for a design of the motor vehicle 1.
  • the evaluation parameter determined by the second module 12 can also be compared with a desired value range.
  • the evaluation algorithm is only executed if a setpoint range has not yet been reached. If, on the other hand, the setpoint range is reached, the last used value of the at least one function parameter of the function of the submodel used for the simulation is output S109, as will be explained below.
  • the function parameters of the functions of the submodel used for simulating are treated in the optimization algorithm as manipulated variables of the component of the motor vehicle 1 or the function of the submodel of this component, in particular as the only manipulated variables.
  • the function parameters are provided to the third module 13 by the first module 1 1 via the third data interface 16 or are defined by a user before performing the method 100 according to the invention as a variable when setting up the optimization algorithm.
  • the third module 13 preferably prepares a test plan based on the optimization algorithm S108, which contains further variation points in a variation space, which is dependent on the at least one function parameter of the function used for simulating Submodel is spanned. Then, in the manner of an iterative optimization, the driving mode of the motor vehicle 1 is again carried out on the basis of the modified partial model, ie with changed functional parameters or coefficients S101 '.
  • a design is set up in particular using statistical methods and corresponds to a design of experiment. Variational points of such an experimental plan are, for example, "design points" shown in FIG.
  • the value of the at least one function parameter or coefficient of the function of the submodel is outputted S109.
  • the value or values may be output via a user interface, more preferably the value or values are used in the function used for simulation.
  • the function obtained in this way indicates that mode of operation of the component of the motor vehicle 1 which would have to have this in order to achieve a specific evaluation of the overall vehicle behavior of the motor vehicle 1.
  • a specification for the at least one component of the motor vehicle 1 or of the entire motor vehicle 1 can now be prepared S1 10a.
  • the designs and / or the control or the regulation of the component of the motor vehicle 1 can be adapted S1 10b.
  • the respective component is preferably constructed, designed and controlled in such a way that the one real operation reflects the output function or the function parameters or coefficients.
  • the at least one component of the motor vehicle 1 or the entire motor vehicle 1 is not only changed, but completely optimized for the optimized sub-model or its function.
  • a torque model as illustrated in FIGS. 3 to 11, can be optimized with respect to various criteria with respect to the driving operation of a motor vehicle 1.
  • the desired target values of the criteria such as an expected acceleration and a reference acceleration or a torque or Drehfittechnik
  • This allows a completely new development approach, in which statements can already be made in the early stages of the development phase for the development engineers, how individual components of a motor vehicle 1 must be designed with defined vehicle parameters to later criteria in relation to the driving operation of the motor vehicle 1 fulfill.
  • a first specifications can be set, which sets target values, in particular a setpoint range with respect to a criterion, for example, the aforementioned driving dynamics parameter or at least one evaluation parameter, which represents the driving dynamics.
  • a second specification book for a component can be determined by means of the method according to the invention, which enables at least a coarse laying of the component of a motor vehicle 1.
  • FIG. 2 shows a second embodiment of the system 10 according to the invention.
  • the system 10 shown in FIG. 2 has only a first module 11 and a second module 12. Accordingly, there is also only the first data interface 14, with which data between the first module 1 1 and the second module 12 can be replaced.
  • no evaluation parameter is calculated. Rather, in the step of balancing S106, the values of the at least one simulated variable by which the overall vehicle behavior can be characterized are directly determined for the respective vehicle Fahr sunnyssstands parameter compared with a setpoint range for this driving condition parameter.
  • the function parameter (s) of the functions used for the simulation are changed using any boundary conditions, without carrying out an evaluation on the basis of a preferably objectivized, ZuOrdungsvorschrift.
  • FIGS. 3 to 11 The creation of a function-based engine model or torque model will be explained with reference to FIGS. 3 to 11, which can be used as a submodel for mapping the operating mode of an internal combustion engine in the method 100 according to the invention.
  • a function-based submodel M1 maps the torque characteristic of a supercharged internal combustion engine 1 in the stationary and transient operating range.
  • Further submodels of the function-based submodel for the engine are a part-load model M3, see FIG. 1, as well as a torque gradient model M2 and preferably also an intake torque model and optionally further submodels.
  • map-based models are commonly used to illustrate the operation of an engine.
  • a characteristic map is used, with which the torque currently applied to the crankshaft is determined in each time step of a simulation as a function of load or throttle position and engine speed.
  • maps are used for the intake torque and the torque gradient due to the supercharger pressure buildup of the turbocharger depending on the input parameters accelerator pedal position, speed and load at the time of load change .
  • engine model can be basically sufficient evaluation relevant transient driving conditions such as full load acceleration, low-end torque, rotary joy, positive load change (tip-in), acceleration and pulling power of a motor vehicle 1 sufficiently accurate.
  • map-based engine model of automated optimization accessible by means of an optimization algorithm
  • individual submodels of the map-based engine model or even all submodels are replaced by function-based submodels.
  • the number of variables to be varied that is to say the function parameters or coefficients of the submodels, can be substantially reduced for an efficient optimization process, and the function parameters or coefficients of the individual submodels can be changed independently of one another, in particular in the context of variable optimization ,
  • the full load characteristic of an internal combustion engine within a speed band of the internal combustion engine is considered in three separate segments.
  • the three speed segments are indicated in FIG. 3 with three different hatchings.
  • the segment up to about 2000 rpm can be used as a full load segment at low RPM, the segment up to about 5200 RPM as a full load segment at medium RPM and the segment up about 7000 revolutions per minute are called full load segment at maximum power.
  • This classification was chosen in this case in the manner to correspond to the usual behavior of modern supercharged internal combustion engines, in particular gasoline engines, as well as possible. Preferably, however, other divisions are possible, which are better suited for mapping the operation of other engines.
  • Torque increase in this segment Depending on the values of these function parameters M 100 o and k M i, the position and slope of the approximation function changes to the full load characteristic, as shown in FIG. 4.
  • the torque in this segment can be described by the following function VF2:
  • M 2 (n) M MAX + - Q ⁇ ⁇ '
  • n M speed at maximum torque
  • the approximation function to the speed characteristic curve in the second segment is represented by the function parameters M max , n M and k M2 in FIG. 5.
  • the maximum power segment does not describe the torque curve, but the performance curve is a function with function parameters.
  • the performance curve can be described in this segment by the following function VF3:
  • n P speed at maximum power
  • model parts of the individual segments or the approximation functions on which the model parts are based are combined to form an overall approximation function for mapping the entire full-load curve.
  • the dependency is defined according to the invention as an increase in torque per unit of time and is a function of the respective present speed.
  • the torque gradient according to the invention is therefore preferably described as a function of the rotational speed by the following functions:
  • auxiliary variables x and y serve only to simplify the calculation.
  • the function parameter "prog" of the torque gradient function can be adjusted within a range of 0 to 1 and, in the above functions, influences to what proportions the torque gradient function is composed of a linear component and a cubic component.
  • FIG. 8 An approximation function according to the invention for the torque gradient is shown in FIG.
  • the value of the torque gradient is represented for each speed with a pedal jump from 0 to 100%.
  • the function parameters gradi, grad 5 and prog are shown in Fig. 8.
  • the arrows also show how a change in this function parameter affects the course of the torque gradient function.
  • the partial load range of a supercharged internal combustion engine depends in particular on the pedal characteristic of the internal combustion engine. This defines the relationship between an accelerator pedal position and a requested torque.
  • a function-based part-load model is preferably determined based on a function-based pedal characteristic.
  • a function-based pedal characteristic is a percentage value that can be used to scale the full load curve according to the pedal position.
  • the function-based pedal characteristic is described according to the invention by the following functions:
  • the calculated result according to this function can also assume values below 0% or above 100%. Therefore, the result must be limited to a valid value range.
  • a real pedal characteristic also has a speed dependency.
  • the two function parameters of the pedal characteristic function "linear" and "shift” are described as speed-dependent functions.
  • the values of the function parameters are defined at three different speeds and the course of the pedal characteristic function is then interpolated over a quadratic polynomial.
  • the arrows in FIG. 9 indicate how the approximate function of the pedal characteristic curve shifts when the "shift" and "linear" function parameters are changed in each case.
  • One Direct response to an accelerator pedal position (straight dashed line) is perceived by a driver as rather athletic, a more progressive curve low torque request in the lower accelerator pedal position and high torque request in the upper accelerator pedal position area is considered rather comfortable.
  • FIG. 11 shows a partial load model M3 according to the invention, which was calculated from the function-based full-load characteristic model according to the invention and the function-based pedal characteristic model according to the invention.
  • the illustrated submodels and their model parts for full-load models M1, torque gradient models M2 and part-load models M3 can be correspondingly modified also transferred to non-supercharged internal combustion engines.
  • Other submodels can be created based on other drive types, such as electric motors, and for other components of the vehicle, such as the steering or the transmission, function-based, so that they can be optimized with the inventive method 100.

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Abstract

L'invention concerne un procédé destiné à l'analyse ou l'optimisation, basée sur une simulation, d'un véhicule automobile. Ledit procédé comprend de préférence les étapes suivantes consistant à : - simuler (S101) une marche du véhicule automobile (1) sur la base d'un modèle (M) ayant au moins une grandeur de réglage pour acquérir des valeurs d'au moins une grandeur simulée qui est appropriée pour caractériser un comportant global, en particulier une maniabilité, du véhicule automobile (1), le modèle comprenant au moins un sous-modèle, en particulier un modèle de couple, et le ou les sous-modèles sont basés sur une fonction et caractérisent de préférence le fonctionnement d'au moins un composant, en particulier d'un moteur à combustion interne, du véhicule automobile (1) ; et - délivrer (S103) les valeurs de la ou des grandeurs simulées.
EP17737818.9A 2016-07-13 2017-07-12 Procédé d'analyse d'un véhicule automobile sur la base d'une simulation Pending EP3485402A1 (fr)

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ATA50628/2016A AT518850B1 (de) 2016-07-13 2016-07-13 Verfahren zur simulationsbasierten Analyse eines Kraftfahrzeugs
PCT/EP2017/067596 WO2018011292A1 (fr) 2016-07-13 2017-07-12 Procédé d'analyse d'un véhicule automobile sur la base d'une simulation

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110674582A (zh) * 2019-09-29 2020-01-10 中仿智能科技（上海）股份有限公司 一种模拟飞行器燃油系统的仿真模型及其方法
CN112241580A (zh) * 2019-09-12 2021-01-19 北京新能源汽车技术创新中心有限公司 动力系统参数确定方法、装置、计算机设备和存储介质

Families Citing this family (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10559217B2 (en) * 2016-08-05 2020-02-11 Intel Corporation Methods and apparatus to develop in-vehicle experiences in simulated environments
US11009427B2 (en) * 2017-10-13 2021-05-18 Paccar Inc Real-time correction of vehicle load curve for dynamometer testing, and associated systems and methods
DE102017218811A1 (de) * 2017-10-20 2019-04-25 Robert Bosch Gmbh Verfahren und Vorrichtung zum Betreiben eines Aktorregelungssystems, Computerprogramm und maschinenlesbares Speichermedium
AT520185B1 (de) * 2017-12-04 2019-02-15 Avl List Gmbh Prüfstand und Verfahren zur Durchführung eines Prüfversuchs
DE102019211951A1 (de) * 2019-08-08 2021-02-11 Robert Bosch Gmbh Verfahren zum Überprüfen eines Fahrzeugdynamikmodells
CN110501941B (zh) * 2019-08-16 2021-08-24 东南大学 一种电动汽车整车控制模型获取方法
DE102019218530B4 (de) * 2019-11-29 2021-07-22 Zf Friedrichshafen Ag Verfahren zur Bestimmung einer Position eines Kraftfahrzeuges
CN113022709A (zh) * 2019-12-24 2021-06-25 上海汽车集团股份有限公司 车身顶棚结构的开发方法以及车身顶棚结构
CN113449405B (zh) * 2020-03-25 2024-05-28 陕西重型汽车有限公司 重型商用车动力总成设计及匹配方法
US20210302183A1 (en) * 2020-03-31 2021-09-30 Fuelsave Consultoria, S.A. Vehicle efficiency prediction and control
CN111783228B (zh) * 2020-07-02 2023-10-31 重庆大学 一种面向节能的纯电动物流车三档变速系统参数匹配优化方法
CN113962138B (zh) * 2020-07-21 2023-11-03 腾讯科技（深圳）有限公司 移动平台的参数值确定方法、装置、设备及存储介质
CN111993847B (zh) * 2020-08-04 2022-10-04 中汽研汽车检验中心（天津）有限公司 基于整车路噪性能提升的轮胎参数优化方法
US20220041176A1 (en) * 2020-08-10 2022-02-10 Toyota Research Institute, Inc. Vehicle dynamics emulation
CN112214833B (zh) * 2020-09-03 2022-05-03 东风商用车有限公司 一种方向盘怠速抖动分析方法及系统
CN112380795B (zh) * 2020-12-09 2024-08-30 中国航空工业集团公司北京长城计量测试技术研究所 一种基于预置模型库的试车台流场数值仿真系统
CN113111003B (zh) * 2021-01-11 2022-05-03 北京赛目科技有限公司 一种场景无关的无人驾驶仿真测试评估方法及装置
CN112758078B (zh) * 2021-01-27 2022-03-15 吉利汽车研究院（宁波）有限公司 车辆发动机负荷点调整量的寻优方法及寻优系统
CN115071718A (zh) * 2021-03-10 2022-09-20 丰田自动车株式会社 驾驶诊断装置以及驾驶诊断方法
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CN113239458B (zh) * 2021-04-26 2022-11-01 中汽研汽车检验中心（天津）有限公司 一种基于虚拟路面的整车路噪仿真对标方法
CN113468664B (zh) * 2021-06-30 2022-06-07 重庆长安汽车股份有限公司 一种车辆声学包装系统的布置方法
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EP4180889B1 (fr) * 2021-11-15 2024-07-17 Volvo Truck Corporation Procédé de surveillance de l'état de santé d'un système de châssis d'un véhicule
US11525728B1 (en) * 2021-11-16 2022-12-13 Geotab Inc. Systems and methods for determining an estimated weight of a vehicle
US12056797B2 (en) * 2021-12-20 2024-08-06 Gm Cruise Holdings Llc Synthetic scene generation using spline representations of entity trajectories
CN114609992B (zh) * 2022-02-09 2024-02-27 三一汽车起重机械有限公司 底盘测试系统及方法
DE102022210320A1 (de) 2022-09-29 2024-04-04 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren und Vorrichtung zur Verschleißanalyse einer Komponente eines Kraftfahrzeugs
US12036998B2 (en) 2022-10-04 2024-07-16 Geotab Inc. Systems and methods for determining an estimated weight of a vehicle
WO2024170027A1 (fr) * 2023-02-14 2024-08-22 Continental Automotive Technologies GmbH Préréglage de paramètres basé sur une simulation pour des fonctions de conduite dynamique
CN116341136A (zh) * 2023-03-21 2023-06-27 中国农业大学 一种发动机曲轴优化设计方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002081253A2 (fr) * 2001-04-06 2002-10-17 Ricardo Consulting Engineers Limited Procede et systeme d'evaluation de motricite

Family Cites Families (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2397296A1 (fr) 1977-07-11 1979-02-09 Kleber Colombes Enveloppe de pneumatique
JP3301183B2 (ja) * 1993-11-24 2002-07-15 日産自動車株式会社 車両の前後輪間駆動力配分制御装置
ES2176660T3 (es) 1996-12-03 2002-12-01 Avl List Gmbh Procedimiento y dispositivo para el analisis del comportamiento en conduccion de vehiculos a motor.
JP2003186917A (ja) * 2001-12-18 2003-07-04 Mitsubishi Heavy Ind Ltd 車両バーチャル性能評価装置
AT6235U3 (de) * 2003-02-28 2004-01-26 Avl List Gmbh Verfahren zur steuerung eines prüfstandes
AT500978B8 (de) 2003-05-13 2007-02-15 Avl List Gmbh Verfahren zur optimierung von fahrzeugen
DE102004054608B4 (de) * 2004-09-21 2006-06-29 Repower Systems Ag Verfahren zur Regelung einer Windenergieanlage und Windenergieanlage mit einem Rotor
EP1922234B1 (fr) * 2005-09-01 2011-01-05 AVL List GmbH Procede pour faire fonctionner un moteur a combustion interne
DE102007044042B4 (de) * 2007-09-14 2009-12-31 Iav Gmbh Ingenieurgesellschaft Auto Und Verkehr Verfahren und Vorrichtung zur Simulation der Fahreigenschaften eines zu entwickelnden Antriebskonzeptes eines Kraftfahrzeuges
AT505105B1 (de) * 2008-07-24 2009-10-15 Avl List Gmbh Verfahren zur beurteilung der fahrbarkeit von fahrzeugen
JP5257508B2 (ja) * 2009-03-13 2013-08-07 トヨタ自動車株式会社 駆動源の制御装置および制御方法
DE102009013291A1 (de) * 2009-03-14 2010-09-16 Audi Ag Verfahren zur Erstellung eines Regelverfahrens für ein die Fahrdynamik eines Fahrzeugs beeinflussende aktive Fahrzeugkomponente
JP5006947B2 (ja) * 2010-01-14 2012-08-22 本田技研工業株式会社 プラントの制御装置
AT11220U3 (de) * 2010-02-04 2010-12-15 Avl List Gmbh Verfahren zum testen eines fahrzeuges oder eines teilsystems davon
JP5392165B2 (ja) * 2010-03-31 2014-01-22 マツダ株式会社 ガソリンエンジンの制御装置
JP5569100B2 (ja) * 2010-03-31 2014-08-13 マツダ株式会社 エンジンの制御装置
FR2958583B1 (fr) * 2010-04-09 2012-04-20 Renault Sas Systeme et procede de limitation de couple moteur d'un vehicule a quatre roues motrices
DE102010034850B4 (de) * 2010-05-19 2015-01-22 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Straßensimulationsprüfstand
CN103186101B (zh) * 2011-12-27 2016-04-20 中联重科股份有限公司 整车控制器的硬件在环仿真测试系统
DE102013003040B4 (de) * 2013-02-22 2015-11-12 Audi Ag Kraftfahrzeug mit nachträglich per Anwendungsprogramm veränderbarem Fahrverhalten sowie Verfahren hierzu
CN103217299A (zh) * 2013-03-28 2013-07-24 浙江吉利汽车研究院有限公司杭州分公司 一种基于驾驶模拟器的车辆底盘性能主观评价系统
DE102013021004A1 (de) * 2013-12-13 2015-06-18 Man Truck & Bus Ag Systeme und Verfahren zum Entwickeln und Testen von hybriden Energiespeichern
CN103823929A (zh) * 2014-02-18 2014-05-28 北京理工大学 一种基于驾驶员模型的车辆转向系统性能测试方法
DE102014109577A1 (de) * 2014-07-09 2016-01-14 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Aufladeeinrichtung für eine Verbrennungskraftmaschine, Verbrennungskraftmaschine sowie Verfahren zum Betreiben einer Verbrennungskraftmaschine
CN104102781A (zh) 2014-07-18 2014-10-15 上汽通用五菱汽车股份有限公司 一种基于多目标优化的发动机性能优化方法
DE102014214140A1 (de) * 2014-07-21 2016-01-21 Zf Friedrichshafen Ag Verfahren zur vorausschauenden Steuerung einer Geschwindigkeitsregelanlage eines Kraftfahrzeuges
DE102014223004A1 (de) * 2014-11-11 2016-05-12 Robert Bosch Gmbh Verfahren und Vorrichtung zum Feststellen, ob in einem Kraftfahrzeug ein Fehlerzustand vorliegt oder nicht
CN108137050B (zh) * 2015-09-30 2021-08-10 索尼公司 驾驶控制装置、驾驶控制方法
CN105302981A (zh) * 2015-11-10 2016-02-03 中国北方车辆研究所 基于自动变速箱控制策略开发的matlab仿真平台架构

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002081253A2 (fr) * 2001-04-06 2002-10-17 Ricardo Consulting Engineers Limited Procede et systeme d'evaluation de motricite

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ADRIAN NE?LER: "Optimierungsstrategien in der modellbasierten Dieselmotorenentwicklung", 5 February 2015 (2015-02-05), pages 1 - 165, XP055396784, Retrieved from the Internet <URL:https://depositonce.tu-berlin.de/bitstream/11303/4841/1/Nessler_Adrian.pdf> [retrieved on 20170808] *
See also references of WO2018011292A1 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112241580A (zh) * 2019-09-12 2021-01-19 北京新能源汽车技术创新中心有限公司 动力系统参数确定方法、装置、计算机设备和存储介质
CN112241580B (zh) * 2019-09-12 2024-06-07 北京国家新能源汽车技术创新中心有限公司 动力系统参数确定方法、装置、计算机设备和存储介质
CN110674582A (zh) * 2019-09-29 2020-01-10 中仿智能科技（上海）股份有限公司 一种模拟飞行器燃油系统的仿真模型及其方法
CN110674582B (zh) * 2019-09-29 2023-08-25 中仿智能科技（上海）股份有限公司 一种模拟飞行器燃油系统的仿真模型及其方法

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