EP3137358A1

EP3137358A1 - System and method for analyzing the energy efficiency of a motor vehicle, in particular of an apparatus of the motor vehicle

Info

Publication number: EP3137358A1
Application number: EP15721185.5A
Authority: EP
Inventors: Helmut List; Peter Schoeggl; Guenter Karl Fraidl; Erik Bogner; Mario OSWALD; Paul Kapus
Original assignee: AVL List GmbH
Current assignee: AVL List GmbH
Priority date: 2014-04-30
Filing date: 2015-04-30
Publication date: 2017-03-08
Also published as: US20170050644A1; JP7053147B2; CN106660563A; KR102353705B1; KR20160148670A; JP2017522213A; DE102014006321A1; WO2015166064A1; CN106660563B; US10035515B2

Abstract

The invention relates to a system (1) for analyzing an energy efficiency of a motor vehicle (2), having: a first device (4a, 4b), in particular a sensor, configured to detect a first data set of at least one first parameter, which is suitable to characterize energy consumed by at least one apparatus A, in particular a steering system or drive device (3); a second device (5a, 5b, 5c, 5d), in particular a sensor, configured to detect a second data set of at least one second parameter, which is suitable to characterize an operating state of the at least one apparatus A; a third device (6), configured to detect a third data set of at least one third parameter, which is suitable to characterize at least one driving state of the vehicle (2); a first comparison device (7), in particular part of a data processing device, configured to compare the values of the second data set with predefined parameter ranges which correspond to at least one operating state, and to compare the values of the third data set with predefined parameter ranges which correspond to at least one driving state; an allocation device (8), in particular part of the data-processing device, configured to allocate the values of the first data set and the values of the second data set to the respectively present at least one driving state; and a processing device (9), in particular part of the data-processing device, configured to determine at least one first characteristic value which characterizes at least the energy efficiency of the at least one apparatus A on the basis of the first data set and the second data set as a function of the at least one driving state.

Description

System and method for analyzing the energy efficiency of a

Motor vehicle, in particular a device of the motor vehicle

description

The invention relates to a system and a method for assessing and / or optimizing energy efficiency of a motor vehicle. The energy efficiency of motor vehicles is becoming increasingly important, both as a purchase argument for customers in the context of rising energy costs and for the legislator in the context of the compulsion to reduce the environmental impact of vehicles within the framework of climate protection goals. On the customer side, the topic of the total cost of vehicle ownership is also gaining importance. With regard to the purely technical aspects, CO 2 legislation is certainly the most important technology driver. The future CO 2 and / or consumption fleet limits converge on a constantly reducing level worldwide. On the one hand, this requires complex drive systems with highly flexible components, but on the other hand also requires a more individualized adaptation to the most diverse boundary conditions and results in a multidimensional diversification of the drive systems (different energy sources, different degree of electrification, variant variety, etc.). In the future, the networking of the drive train of the vehicle with the entire vehicle environment (English: Connected Powertrain) also allows an optimal adaptation of the operating strategies to the real traffic situation and environmental conditions, in particular the topography. The wealth of information from vehicle infotainment, Driver assistance systems up to vehicle-to-vehicle communication (Car2Car) or vehicle-to-X communication (Car2X) make it possible to calculate many scenarios in advance and thus massively expand the optimization horizon. This provides the opportunity to use the various degrees of freedom of future drive systems to a much greater extent to reduce energy consumption. However, this requires highly complex operating strategies with extremely increased development, calibration and, above all, validation effort.

To give the consumer an orientation in terms of energy efficiency has been 1. December 201 1 the Regulation on C0 ₂ marking for passenger cars in the Federal Republic of Germany comes into force. Since then, a vehicle that has been issued or offered for sale or lease must be provided with the associated C0 ₂ label on the vehicle, which indicates its energy efficiency class. For vehicles, the size is divided by the vehicle weight. There is a direct correlation between the energy efficiency of a vehicle and the emissions of the vehicle.

In order to classify vehicles in energy efficiency classes, the reference value for CO ₂ emissions at the time of registration is determined by the weight of a vehicle. A statement on the extent to which the energy put into a vehicle is used efficiently, and what contribution individual devices A of the vehicle such as the drive train, the steering, the drive device or the ancillaries or other factors to the energy efficiency afford, can from the classification In contrast, in an energy efficiency class can not be won.

The emissions themselves are also subject to ever stricter legal regulations. In the European Community, the first single emissions legislation came into force in 1970. At that time, only the emissions of carbon monoxide and hydrocarbons were limited. In 1977, nitrogen oxides were introduced as additional limiting exhaust constituents. Particulate (soot) limits from diesel engines were introduced in 1988. For lorries and buses, limits for exhaust gas substances were established in 1988 for the first time in Europe. For motorcycles and mopeds, since 1997, Europe has set exhaust emission limits. The emissions regulations have since been tightened gradually. This tightening concerns the type and level of emission levels and their permanent compliance. The values for consumption and emissions are tested in an equally standardized driving cycle to check for legal standards. For decades, this has been the standard method for determining emissions during the approval test of vehicles on the test bench. In a laboratory environment, with clear parameters for temperature, fuel, test cycle and route profile, the engines and vehicles are optimized with regard to minimum exhaust emissions and fuel consumption. Improved combustion processes and the use of suitable exhaust aftertreatment will undercut all legal emission limits at the time of application. The new European driving cycle at the time of registration lasts a total of 1 .180 seconds (just under 20 minutes). It consists of a 780-second city cycle (urban conditions) and a 400-second overland cycle (extra-urban conditions). The ambient temperature during the measurement is 20 ° C to 30 ° C. Cold start conditions, acceleration and deceleration are detected and interpolated accordingly. The assessment of consumption and emissions based on the standardized driving cycle provides an average profile for comparing different vehicles. The driving cycles are usually only partially in line with the individual user profiles of customers, especially when a lot of short-distance and city traffic occurs at a customer. In addition, fuel consumption and emissions are not measured at speeds above 120 km / h and are not included in the average calculation. During a driving cycle, the search for causes of increased emissions aims to optimize the overall cycle.

DE 10 2009 048 615 A1 relates to a method for the electronic configuration of vehicles, wherein

a track-dependent driving profile is determined for the vehicle to be configured,

to simulate and quantify the expected energy flows on the vehicle based on this driving profile, individual, mutually compatible functional blocks of the vehicle are determined as a function of the energy flows to be expected in the vehicle, the functional blocks describing in particular energy-related properties of a component contained in the respective functional block,

- the individual function blocks are assembled and a driving profile-dependent partial or total energy balance is created,

- Individual function blocks for optimizing the driving profile-dependent partial or total energy balance and / or variant formation as long as replaced or replaced until a compiled for the desired driving profile energy efficient vehicle.

US 2008/039996 A1 relates to a system for detecting a failure of a steering angle sensor in an electronic power steering apparatus, the system comprising:

a steering angle sensor for generating and transmitting a steering angle of the signal by measuring a rotation angle of the steering wheel;

a motor for generating an assist power for smoothly steering and transmitting a current and a voltage based on the rotation of the motor; and

an electronic control unit for receiving the current and the voltage from the motor, which detects the rotational direction of the motor, the steering angle signal from the

Steering angle sensor receives, detects a first rotational direction of the steering wheel and then, if the direction of rotation of the motor does not match the first rotational direction of the steering wheel, determines that the steering angle signal has a fault and starts the operation of a fail-safe logic.

US 2007/01 12475 A1 relates to a device for managing the power consumption of a vehicle, having power management logic suitable for providing power applied to the vehicle engine based on information about the surroundings of the vehicle, information about the operating state of the vehicle, calculate one or more control inputs and one or more operating parameters of the vehicle. US 8 571 748 B2 relates to a method for estimating a propulsion related operating parameter of a vehicle for a link, the method comprising:

- estimating at least one operating parameter of the vehicle for the route section based on information about the route section;

- estimating the propulsion-related operating parameters for the route section using the at least one estimated operating parameter and at least one vehicle-specific parameter, wherein the at least one vehicle-specific parameter is determined by:

Acquiring driving data to determine a plurality of vehicle operating parameters while the vehicle is in operation;

Using at least two of the determined vehicle operating parameters in a predetermined relationship including the at least one vehicle-specific parameter; and

Determining the at least one vehicle-specific parameter from the driving data for the at least two vehicle operating parameters and the relationship,

- Identifying different driving phases in the driving data, which were detected for the plurality of vehicle operating parameters, wherein at least one vehicle-specific parameter for vehicle phases is determined, each identified driving phase is assigned to a set of vehicle-specific parameters, which are determined from the respective driving data, the vehicle-specific parameters determined for all identified driving phases can be used to estimate the propulsion-related operating parameters. An object of the invention is to provide a system and a method which enable a generally valid analysis of the energy efficiency of a motor vehicle. In particular, the analysis should not or only to a small degree depend on the vehicle weight and the driving cycle. This object is achieved by a system for analyzing an energy efficiency of a motor vehicle according to claim 1 and by a corresponding method according to claim 6. Advantageous embodiments of the teaching according to the invention are claimed in the subclaims. The invention is based in particular on the approach of segmenting complex driving profiles into individual driving elements or driving states or sequences of driving states and determining a characteristic value on the basis of this segmentation. The segmentation makes it possible to determine the influence of the individual driving elements or driving states on the energy efficiency of the vehicle. The characteristic value based on the segmentation is independent of driving cycles and can therefore be described as a generally valid parameter for energy efficiency. From the characteristic values of the energy efficiency for individual driving conditions, an arbitrary driving course can be reproduced and an energy efficiency for any, in particular a stochastic driving course, which corresponds to a real drive (real drive), be reconstructed. The Applicant has found that with such a segmented analysis of the energy efficiency of the vehicle, large improvements in vehicle efficiency can be achieved if optimization is made on the basis of such a characteristic.

The invention is further based on the approach, individual devices A of a vehicle, which may be designed as modules, components or components, each attributed its own energy efficiency. This categorization of the energy efficiency of the entire vehicle in the energy efficiency of individual vehicle elements can be used on the one hand to optimize the individual vehicle elements in their operating strategy as a function of the driving dynamics of the vehicle. On the other vehicle elements can be identified with poor energy efficiency and replaced if necessary. Furthermore, the categorization allows the investigation of the influence of different vehicle elements, so different devices of the vehicle on the mutual energy efficiency. This is particularly important in those devices whose energy output E (out) is passed as incoming energy E (in) to another device. Another advantage of the classification is that vehicles can be disassembled in terms of energy efficiency similar to the finite element method in vehicle elements, each of which can be described with a manageable number of parameters. This allows the vehicle in its entirety in relation to the Energy efficiency can be simulated particularly well. This enables a significant reduction in the time required for development, since changes in the design of devices of the vehicle can be examined with regard to the influence on the overall energy efficiency of the vehicle. Due to the possibility of energy efficiency analysis by vehicle simulation, a shift of validation tests from the test bench to fully simulated or partially simulated processes, a so-called front-loading of the development process, can be achieved.

If enough parameters are taken into account accordingly, the combination of the segmentation with the categorization enables the energy efficiency of a vehicle component to be released not only from the respective vehicle cycle, but also from the respective vehicle in which it is installed.

By determining such generally valid characteristic values, individual vehicle components or devices can be compared with each other across vehicles and across driving cycles. The determination of the general characteristic value can take place, for example for a vehicle registration, in real driving operation independently of a specific driving cycle. This leads to a much better comparability of vehicles of different vehicle classes and to results that better reflect the consumption in real road traffic. Furthermore, the manageable test area test bench with the partially stochastic component road travel is extended so that the synthetic test cycle can be supplemented by random real operation with an unmanageable number of different driving elements or driving conditions and boundary conditions.

Consumption, emission and thus the efficiency can be analyzed according to the invention with respect to individual driving conditions, a plurality of similar driving conditions and / or sequences of different driving conditions of the vehicle, so that influences of driving conditions on energy efficiency and vehicle operating behavior can be disclosed.

A drive device in the sense of the invention is set up to generate mechanical drive power by means of energy conversion. The term "detecting" in the sense of the invention includes reading in data sets which are generated in particular by simulations, presetting one

Operating state of an aggregate of the vehicle and / or performing measurements on a vehicle or on a test bench.

An operating state of a device according to the invention is characterized by operating parameters. In an internal combustion engine these are typically torque and speed. In particular, an operating state can only mean activation or deactivation of a device. Preferably, at least one operating state can determine the work or power provided by the device.

A driving state in the sense of the invention is defined by one or more values of one parameter or constellation or several constellations of values of several parameters, depending on whether the driving state is considered situationally (for example the presence of a cornering) or whether a driving state is first from the time course of parameters results (for example, the presence of a tip-ins). A driving state in the sense of the invention reflects in particular the driving dynamics of the vehicle. Driving conditions are, in particular, gliding at constant speed, acceleration, cornering, parking, straight ahead, idle (roll), tip-in, let-off, cruise, shift, standstill, uphill, down, electric Braking by recuperation, mechanical braking or even a combination of at least two of these driving conditions. In some of the driving states, the driving dynamics are also determined by the type of drive or by the operating state of vehicle components. Thus, in a full hybrid vehicle in principle three different tip-in driving conditions are possible, a tip-in, which is driven by the internal combustion engine, a tip-in, which is driven by the electric machine and a tip-in, in which the electric machine as additional Electric boost is used. Individual driving conditions can be refined up to the viewing of individual constellations, so that, for example, tip-ins can also be distinguished as different driving conditions at different speeds or from different output speeds. Driving resistance in the sense of the invention denotes the sum of the resistances which a land vehicle has to overcome with the aid of a driving force in order to drive at a constant or accelerated speed on a horizontal or inclined plane. The driving resistance is composed in particular of the components air resistance, rolling resistance, gradient resistance and / or acceleration resistance.

A topography in the sense of the invention is a terrain and indicates, in particular, the inclination of the road, the curve of a road and the altitude above sea level (for example the sea level).

A device A of a vehicle according to the invention is a component, in particular an accessory, a component, in particular a power electronics or a drive device, or a system, in particular a steering or a drive train.

A driving element according to the invention is preferably a driving condition. Further preferably, for the identification of a driving element, the development of further parameters can be taken into account, which characterize the criteria mentioned above. Here, e.g. It is conceivable that an increase of the first parameter, which characterizes the energy consumption of the vehicle, indicates a particularly relevant driving element for the energy consumption and thus for the energy efficiency.

Real-drive within the meaning of the invention means real driving, especially on the road or off-road. In partially simulated or fully simulated vehicles, RealDrive can also represent the mapping of such a real journey, for example by stochastic methods, on a test bench. Accordingly, real-drive emissions are generated during a (simulated) real journey, a real-drive efficiency is the energy efficiency of the vehicle during a (simulated) real driving operation. In an advantageous embodiment of the system according to the invention, the at least one first parameter further characterizes a power consumption of at least one further device B, the at least one second parameter further characterizes an operating state of the at least one further device B and The processing device is further configured to determine at least one second characteristic which at least characterizes the energy efficiency of the at least one further device B on the basis of the first data record and the second data set as a function of the at least one driving state, and the processing device is further for combining the at least one first characteristic, which characterizes at least the energy efficiency of the at least one device A, with the at least one second characteristic, which characterizes at least the energy efficiency of the at least one further device B, for each same driving state to a total characteristic value, which is an energy efficiency of the system at least two devices A and B characterized, set up.

By combining characteristic values of a device A and a device B into an overall characteristic value, superordinate systems of the vehicle can be successively assembled on the basis of the individual elements of the vehicle. Thus, energy efficiency can be assembled from the smallest to the largest unit of the vehicle.

In another embodiment, the overall characteristic value reflects the energy efficiency of, for example, a drive train, a steering or the entire vehicle. Alternatively, individual assessments, as shown in the following advantageous embodiment, can also be combined into an overall assessment.

In a further advantageous embodiment, the system according to the invention has a fourth device, in particular an interface, configured to detect a desired value for the at least one characteristic value, in particular based on a vehicle model or a reference vehicle, and further comprises a second comparison device, in particular part of a Data processing device configured to match the characteristic value with the desired value and an output device, in particular a display, configured to output a rating based on the calibration. On the basis of the evaluation of the energy efficiency, different devices of the same kind or even different systems or even vehicles can be easily compared with each other. The rating is preferably given in a type of energy efficiency class.

In a further advantageous refinement of the system according to the invention, the latter furthermore has a memory device which is set up to store a sequence of the driving states and the processing device is furthermore set up to take into account the sequence of driving states when determining the characteristic value.

Setpoints or setpoint functions can be carried out according to the invention both by comparison with a reference vehicle and by comparison with a statistically selected result of several comparison vehicles. In this case, a statistical evaluation can be carried out in particular on the basis of the regression analysis or else a simple average determination. For a rating, tolerance ranges can be specified for the setpoints or the setpoint function. Preferably, only a sequence of at least two singular driving conditions may also have a significant relevance to a criterion (e.g., a tip-in after a long coasting phase for the driveability criterion).

With this embodiment of the system according to the invention not only values can be determined on the basis of individual driving elements, in particular driving conditions, but also the influence of preceding and / or following driving conditions can be taken into consideration for existing driving conditions to be evaluated. In addition, characteristic values can also be determined via acquisition periods which capture a plurality of driving states, wherein the respective parameters used for the analysis can be summed or integrated over these periods. Preferably, all data sets can be stored here, so that an analysis can be carried out not only in online mode, but also in off-line mode. For the analysis of the stored values, a sliding evaluation window can be defined with which individual driving elements or driving states can be broken down into smaller units. Also, a statistical evaluation of the setpoint deviation during individual driving states or over several driving states of the same kind in an overall consideration possible. An event relevant for energy efficiency is preferably determined by the frequency and magnitude of the deviation from the desired value. Thus, both smaller deviations with higher frequency and large deviations with smaller frequency can be classified as relevant.

In a further advantageous embodiment of the system according to the invention, the processing device is further configured to correct an assignment of the values of the first data set and the second data set to the at least one defined driving state, a signal propagation time and / or a runtime of at least one measuring medium for detecting the respective Record up to a sensor.

By means of this embodiment of the system according to the invention, it can be avoided that detected values or measured values are assigned to incorrect driving conditions, or that elements are identified incorrectly.

The aspects of the invention described above and the features disclosed for the development of the system according to the invention also apply to the aspects of the invention described below and to the corresponding development of the method according to the invention correspondingly and vice versa.

In an advantageous embodiment, the inventive method further comprises the following steps: detecting a setpoint value for the at least one characteristic value, in particular based on a vehicle model or a reference vehicle, matching the characteristic value with the desired value and outputting an evaluation of the energy efficiency on the basis of the adjustment ,

In a further advantageous embodiment of the method according to the invention, the at least one first parameter furthermore characterizes a power consumption of at least one further device B, and the at least one second parameter further characterizes an operating state of the at least one further device B and the method further comprises the working steps Determining at least one second characteristic value which at least the energy efficiency of the at least one further device B characterized on the basis of the first data set and the second data set as a function of the at least one driving state, summarizing the at least one first characteristic value, which characterizes at least the energy efficiency of the at least one device A, with the at least one second characteristic value characterized at least the energy efficiency of the at least one further device B, for each same driving state to a total characteristic, which characterizes an energy efficiency of the system of the at least two devices A and B. As already stated with respect to the system according to the invention, in this way the energy efficiency of assemblies of individual devices can be characterized by overall characteristics.

In a further advantageous embodiment, such an overall characteristic value can indicate the energy efficiency of a drive train, a steering or the entire vehicle.

In a further advantageous embodiment of the method according to the invention, the first data set furthermore characterizes a power consumption of at least one further device B and the second data record further characterizes an operating state of the at least one further device B, wherein the at least one device A provides energy to the at least one device B. The method further comprises the step of cleaning the power consumption of the at least one device A and the power consumption of the at least one device B.

Modules and components of a vehicle usually exist as a combination of several devices, each of which has an energy consumption and thus a separate energy efficiency. In order to determine individual energy efficiencies of the various devices installed in a module or component of the vehicle, the respective energy consumption of the individual devices must be identified. In devices that provide each other with energy, this can be done by determining the incoming energy E (in) into a powered device B happen, with their energy is subtracted from the power consumption of the providing device A.

In a further advantageous embodiment of the method according to the invention, the device A is the steering system or one of these components or components and an operating state of the steering system from at least the group of following operating states can be present: steering, deflection, constant steering angle, deactivating or activated state of a steering actuator, servo operation , manual operation or even a combination of at least two of the operating states.

On the basis of these operating states of the steering and the driving state, it is possible, in particular, to calculate the energy which the steering must apply to achieve the required effect. In a further advantageous embodiment of the method according to the invention, the device A is the drive device or one of these components or components and there is an operating state of the drive device from at least the group of following operating conditions before: coasting, part load operation, full load operation, disabled, activated, starting operation, idle operation or a combination of at least two of these operating states.

Also in the case of the drive device, it can be preferably derived from a combination of the driving state in connection with the operating state, which energy the drive device has to provide for reaching the driving state.

In a further advantageous embodiment of the method according to the invention, the at least one second parameter is furthermore suitable for characterizing a topography of the surroundings of the vehicle. By determining the topography of the surroundings of the vehicle, an operating strategy of the vehicle can be adapted to a change in the course of the road which the vehicle follows before reaching the corresponding road course. As a result, significant efficiency gains can be achieved in relation to the overall efficiency of the vehicle.

In a further advantageous embodiment of the method according to the invention, the at least one device A is an internal combustion engine or a fuel cell system and the first parameter is at least one emission of the internal combustion engine or of the fuel cell system.

According to this embodiment, the energy consumption of the internal combustion engine or of the fuel cell system, which has a reformer, can be determined via an emission measurement, in particular via the CCVEmission. Preferably, the energy supply and energy outflows of an energy storage device are also taken into account.

In a further advantageous embodiment of the method according to the invention, the steps are carried out until the third record extends over several different driving conditions.

In a further advantageous refinement, the method according to the invention furthermore has the following working step: determination of the sequence of driving states, the sequence of the driving states being taken into account when determining the characteristic value.

In a further advantageous embodiment of the method according to the invention, the values of the first data record and / or of the second data record are integrated over the time duration of the respective vehicle operating state, in particular of the driving state.

This complex integration and in particular summation of the values makes it possible to determine a characteristic value over the entire duration of a driving state.

In a further advantageous embodiment of the method according to the invention, the values of a plurality of data sets of driving status types are combined to determine the at least one password. This allows a statistical evaluation of identical driving conditions are made.

In a further advantageous refinement, the method according to the invention furthermore has the following working step: assigning the values of the first data set and the second data record to the at least one predefined driving state, a signal propagation time and / or a runtime at least one measuring medium for detecting the respective data record up to a sensor. In a further advantageous refinement, the method according to the invention also has the following working step: determining an operating mode of the vehicle on which the evaluation additionally depends and which is selected, in particular, from the following group of operating modes: efficiency-oriented operating mode, driving-performance-oriented operating mode, comfort-oriented operating mode, consumption-oriented Operating mode, low-emission operating mode, drivability-oriented operating mode, NVH comfort-oriented operating mode.

By determining a rating in additional dependency on the further boundary conditions or criteria, emission, drivability and / or NVH comfort can be optimized in an optimization process not only to an absolute maximum of the energy efficiency of the device A, but it can also relative maxima for the energy efficiency are determined, which comply with the other boundary conditions. As a result, target conflicts can be solved particularly advantageous in the optimization of a vehicle.

In a further advantageous refinement of the method according to the invention, the parameters of the data records are recorded in a real drive mode of the vehicle, wherein the vehicle preferably covers a real driving route selected according to stochastic principles, preferably a real vehicle selects an at least partially simulated driving route selected according to stochastic principles even more preferably, an at least partially simulated vehicle travels and most preferably selects an at least partially simulated route selected according to stochastic principles a simulated vehicle travels selected simulated route according to stochastic principles.

A real-drive operation of a vehicle according to the invention is an operation of a vehicle according to the aspects of a real everyday journey of a user, for example, to work, for shopping or on vacation.

The method according to the invention makes it possible to uncouple the experimental operation of driving cycles, characteristic values being determined as a function of individual driving elements, in particular driving conditions. Based on this knowledge, an arbitrary driving cycle, which is a real-drive operation of a vehicle, can be composed.

The method according to the invention can be used both to evaluate a real vehicle and to evaluate a partially simulated or emulated or completely simulated or emulated vehicle. In the case of the real vehicle, this is subjected to a real operation and the parameters which form the data sets are determined by measurements with sensors. In the case of the partial simulation, a simulation model is created for the entire vehicle, by means of which parameter values for at least one parameter of a data set are calculated. The tests are carried out in particular on test stands, wherein parameter values for those parameters or data records for which measurements are possible are preferably determined by a measurement.

In a fully simulated evaluation, the entire vehicle is simulated and the test operation takes place as a pure simulation without a test bench, wherein parameter values measured for individual components or systems of the vehicle can be included in the simulation. In the case of the assessment of a real vehicle, the real vehicle can be operated both on the road or off-road or on a simulated road or simulated terrain on the chassis dynamometer. According to these possibilities of using the system according to the invention and the method according to the invention for assessing a real vehicle of a partial Simulation of the vehicle or a complete simulation of the vehicle means the term "capture" in the meaning of the invention, an import of data sets, which were generated in particular by simulation, predetermining an operating state of an aggregate of a real or simulated vehicle and / or performing measurements on real Vehicles or components or systems of a real vehicle on a test bench.

Other features, advantages and applications of the invention will become apparent from the following description taken in conjunction with the figures. Show it:

Figure 1 is a partial schematic representation of a vehicle with an embodiment of the system according to the invention for the evaluation and / or optimization of energy efficiency of a vehicle; FIG. 2 shows a partially schematic block diagram of an embodiment of the method according to the invention for analyzing an energy efficiency of a vehicle;

Figure 3 is a partial schematic diagram of a categorization of the system integration of an entire vehicle according to an embodiment of the system according to the invention and of the method according to the invention for analyzing an energy efficiency of a vehicle; and

Figure 4 is a partial schematic diagram of a segmentation of a driving profile of an embodiment of the system according to the invention and the invention

Method for analyzing the energy efficiency of a vehicle.

Figures 5 to 16 relate to further aspects of the invention.

FIG. 1 shows purely by way of example an embodiment of the system according to the invention in a vehicle 2 with a drive device 3. The drive device 3 is in this case in particular a component of the drive train which extends from the drive device 3 via a drive shaft to the transmission 19 and a differential 21 and then further extends over axles to wheels 18b, 18d, at a four-wheeled Wheel drive also to other wheels 18a, 18c. The drive device 3 is preferably an internal combustion engine or an electric machine. The drive device can preferably also have a fuel cell system, in particular with a reformer and a fuel cell, or a generator with which energy from a fuel, in particular diesel, can be converted into electrical energy. The drive device 3 draws the energy from an energy storage device 15, which may be designed in particular as a fuel reservoir or as an electrical energy storage, but also as a compressed air reservoir. By means of the drive device 3, the stored energy in the energy storage 15 energy is converted by conversion of energy into mechanical driving force. In the case of an internal combustion engine, the mechanical work is transmitted via a gear 19 and a differential 21 via drive shafts and the axle to the drive wheels 18b, 18d of the vehicle 2. A portion of the energy stored in the energy storage 15 is dissipated to ancillaries directly or by means of a conversion step by the drive device 3 as mechanical work. Auxiliary units here are in particular an air conditioner, a fan, but also servomotors, for example for the windows or an electromechanical or electro-hydraulic steering actuator 16 or the brake booster, that is, any aggregates that consume energy, but not directly to the generation of the drive of the vehicle. 1 involved. Exhaust gases or emissions, which may be incurred during operation of the drive device 3, for example, by the fuel cell system or the internal combustion engine are discharged via a device for exhaust aftertreatment 22, such as a catalyst or a particulate filter, and through the exhaust system 23 into the environment. Preferably, the vehicle 2 may also have two drive devices 3, in particular an internal combustion engine and an electric machine, in which case two energy stores 15, in particular a fuel reservoir and an electrical energy store are provided.

The invention may be used to analyze any other type of multi-dimensional drive system vehicle. In particular, the invention may be used in parallel hybrid, hybrid or hybrid hybrid vehicles. The aim of the invention is to determine the total energy consumption of the vehicle, to determine the energy required for the propulsion and any additional functions, and to determine therefrom a generally valid energy efficiency of the vehicle. The system 1 according to the invention intended for this purpose is explained below with reference to FIG. 1 in a real vehicle, wherein the data sets of the various parameters are preferably determined by measurements. In further embodiments, which are not shown, however, it may also be preferable to simulate or emulate parts of the vehicle 2 and to make only a few data sets based on measurements of the other real systems and components of the vehicle or at the outputs of the emulators. Further preferably, it may be provided to simulate the entire vehicle with all components and systems.

As a simulation model for the vehicle, a multi-mass oscillator can be used whose parameters are adapted to a specific vehicle or a group of vehicles.

The system 1 can be arranged with all components in the vehicle. In experiments on a real vehicle 2 and in partially simulated experiments, the components of the system 1, which are not required for measurement on the vehicle or DUT on a test bench, can also be arranged at a different location, for example in a back-end or a central computer ,

The analysis of the energy efficiency of a vehicle 2 is shown in the embodiment shown in FIG. 1 by means of the systems steering and powertrain or by means of the components electromechanical or hydromechanical steering actuator 16 and steering control 17, or drive device 3, energy storage 15 and possibly transmission 19 , However, it will be apparent to those skilled in the art that the methodology of the invention may be applied to other systems, components, and components of the vehicle 2, such as the brake system and optionally other drive means, etc. In the embodiment illustrated in FIG. 1, the drive device 3 is an internal combustion engine which has an exhaust aftertreatment 22 and an exhaust system 24. An energy storage 15 is divided here into the electrical energy storage, ie the battery of the vehicle, and the fuel reservoir.

The energy which is taken from this energy store is preferably determined with at least one second device, in particular a sensor 4a. Further preferably, at least one emission can be determined with a sensor 4b at an exhaust gas analysis device 23. This is particularly advantageous for the energy consumed by the internal combustion engine 3 representative. The exhaust gas analysis device 23 may be arranged before or after the exhaust aftertreatment.

The system 1 further preferably has a second device, which preferably comprises sensors 5a, 5b, 5c, 5d, which can detect an operating state of a device A. In the case where the device A is the internal combustion engine 3 of the vehicle 2, such operating conditions may include coasting, part-load operation, full-load operation, deactivation of the internal combustion engine, activation of the internal combustion engine, a starting operation, an idling operation or even a combination of at least two be such operating conditions. The sensors 5a, 5b, 5c and 5d may, for example, the torque, the rotational speed, the throttle position, the accelerator pedal position, the intake manifold vacuum, the coolant temperature, the ignition timing, the injection amount, the Δ value, the exhaust gas recirculation rate and / or the exhaust gas temperature as a parameter for Characterization of the operating state.

If the device A to be analyzed, for example, the steering of the vehicle 2, as operating conditions, for example, a steering, a deflection, ie a change in the steering angle and / or the rate of change, a constant steering angle, a deactivation of an electromechanical or hydromechanical drive, a servo operation, be a manual operation or a combination of at least two of these operating states. Second parameters which can be used to detect such an operating state are, in particular, the steering angle α, a force or a signal which is present between a steering control 17 and a steering actuator, detecting the operation of a steering wheel 20 and / or an energy consumption of the steering actuator 16.

As described with respect to the method according to the invention, it can be determined from the operating state which is determined by at least one sensor 5a, 5b, 5c and 5d of the device A, and the driving state of the vehicle 2, what energy a device A fulfills its requirements must apply appropriate function. In the case of the drive device 3 or an internal combustion engine, this intended function is the generation of a mechanical work, with which a propulsion of the vehicle 2 can be achieved, i. with which the driving resistance and resistances in the drive train can be overcome.

In the case of the steering system, such a designated function is to implement a driver's request with respect to the direction of travel of the vehicle 2. By assigning the energy required to fulfill the intended function of the device A by the processing device 9 to the actual power consumption of the device A, a characteristic value can be determined which indicates the individual energy efficiency of the device A, in particular as a function of the respective driving state.

The system 1 furthermore has a third device or at least one sensor 6, which makes it possible to determine at least one parameter which is decisive for the driving state of the vehicle 2. The parameter may be at least one of the following group of parameters: engine speed, throttle position or accelerator pedal position, vehicle speed, vehicle longitudinal acceleration,

Intake manifold vacuum, coolant temperature, ignition timing, injection quantity, λ value, exhaust gas recirculation rate, exhaust gas temperature, engaged gear and gear change. In Fig. 1, for example, via an incremental encoder 6 determines the speed at the drive wheel 18d, which can be closed to the vehicle speed, with which, for example, the driving state sliding at a constant speed, and various acceleration states can be determined. The system 1 further comprises an allocation device 8, which is in particular part of a data processing device, and which determines the determined energy consumption of the Vehicle and the driving resistance of the vehicle the respective driving condition, which was present at the time of measuring the respective parameter values, assign. The driving resistance to be overcome by the vehicle 2 can preferably be deduced from the energy to be applied which the vehicle 2 has to provide in order to provide a specific driving performance predetermined by the driver. By comparing this energy to be provided by the vehicle 2 with the energy consumption of the vehicle 2, which is preferably determined by the sensors 4 a, 4 b, it is possible to specify a parameter for the energy efficiency of the vehicle. This is preferably calculated by a processing device 9, which is also in particular part of a data processing device.

Preferably, the system 1 according to the invention has a further fourth device 10, which can detect a desired value for the at least one characteristic value. Preferably, this fourth device 10 is an interface with which corresponding desired values can be read in, more preferably, this fourth device 10 is a simulation device for a vehicle model, which generates a desired value for the at least one characteristic value. By means of a second comparison device 11, the system can preferably match the desired value with the characteristic value and then output it on a display 12.

A vehicle model in the sense of the invention is a mathematical model of a vehicle. This preferably includes both the hardware configuration and the corresponding operating strategies for the vehicle, as well as its systems and components.

The system 1 preferably also has a fifth device 14, which is set up to reproduce the current driving resistance of the vehicle 2. Such a fifth device 14 is preferably suitable for determining all driving resistance components which act on the vehicle 2, ie the aerodynamic drag, the rolling resistance, the gradient resistance and / or the acceleration resistance. In this case, vehicle data which are available, for example, from the manufacturer, such as the vehicle weight and the Cw value are preferably used. Other parameters, which change with the temperature or the mobile state, can by Sensors are determined. In particular, the drag coefficient, the end face of the vehicle and the speed are included in the air resistance, the elasticity of the wheel, the tire pressure and the wheel geometry, the road surface condition, which can be determined for example from a database, and the road condition. In particular, the vehicle weight and the gradient are included in the gradient resistance, wherein the gradient can be determined for a traveled Δ travel distance via a barometric or GPS altimeter. The acceleration resistance depends in particular on the mass and the acceleration of the vehicle 2.

All sensors 4a, 4b, 5a, 5b, 5c, 5d, 6 of the system 1 are preferably provided with a data processing device, which in particular a first comparison device 7, an allocation device 8, a processing device 9, a data interface 10, a second comparison device 1 1 and a Output device 12, by means of a data connection, in particular via the data interface 10, connected. The data connections are shown schematically in Fig. 1 with dashed lines.

Furthermore, the system 1 preferably has a data memory 25 in which a sequence of driving states and associated additional data can be stored.

Further preferably, the processing device 9, which in particular has a microprocessor with a main memory and in particular a computer, take into account the sequence of driving conditions when determining the characteristic value and the assignment by a signal transit time or by a transit time when assigning the respective data sets to the driving state of a medium to a sensor.

An embodiment of the method 100 according to the invention is explained below with reference to FIGS. 2, 3 and 4.

The method according to the invention serves to analyze the energy efficiency of a device A or of a vehicle 2, and in particular a characteristic value and a rating to determine which are universal and not based on a particular driving cycle. By specifying an energy efficiency and the evaluation of the energy efficiency of individual devices A of the vehicle 2, the vehicle 2 can be broken down into individual vehicle elements according to a finite element method. In particular, in the modeling of the overall system vehicle 2 can be achieved with such a splitting of the vehicle 2 in vehicle elements a much more accurate result.

The approach on which the invention is based is, on the one hand, a segmentation of complex driving processes into assessable driving elements, which in particular correspond to driving conditions, and on the other hand a categorization of the system integration of the entire vehicle 2 into individual vehicle elements, e.g. Systems, components or components of the vehicle, such as the drive device or the steering system.

In a first operation 101, parameters 101 are recorded which indicate the energy consumed by a device A. In the case of a steering system, which consists in particular of the components Lenkungsaktuator 16, steering control 17 and steering wheel or steering wheel 20 and other mechanical components such as the steering linkage, such energy consumption is characterized in particular by providing that force, which for driving the wheels 18 c, the steer the vehicle 2 and is required to hold these wheels 18a, 18c in position. If the vehicle 2 has no driver assistance systems, this energy must be provided by the driver via the steering wheel 20. Normally, vehicles 2 at the time of registration have at least one steering actuator 16, which steering instructions of a driver, which are given via the steering wheel 20, are converted into a change in the steering angle α. The steering angle α is in particular the angle between the vehicle central axis and the rolling direction of a wheel 18a, 18c, as shown in Figure 1. The steering actuator 16 is usually an electromechanical or hydromechanical device that converts electrical energy or hydraulic energy into mechanical energy for deflecting the wheels 18a, 18c. This energy is either provided directly by the drive device 3 by means of a hydraulic fluid or is provided by an electrical energy store 15 via an electric line. Further energy that consumes the steering system, for example, the electronics of a steering controller 17th or power electronics for controlling the steering actuator 16. In this example, a first parameter that can be determined by a sensor 4a is the electrical energy provided to the steering system. Another parameter which must be consulted in the case of a hydromechanical steering actuator 16 is the energy which the steering actuator 16 branches off from the hydromechanical supply, ie which energy is provided by the drive device 3 in this regard.

In the event that a drive device 3, in particular an internal combustion engine, is analyzed as device A, the energy intake is defined on the one hand by the supplied fuel. Furthermore, that energy is to be considered, which is optionally supplied to the internal combustion engine 3 via ancillaries, in particular electrical ancillaries, for example via an electrically operated compressor or even various pumps. Of course, these energy inputs of ancillary components must be taken into account in an energy balance for the internal combustion engine, as far as ancillaries are not operated directly from the internal combustion engine. For a rough energy balance for the internal combustion engine 3 can also be provided to neglect the energy input of the ancillaries, since in the entire system vehicle 2, the energy for operating the ancillaries in a permanent operation is provided exclusively by the internal combustion engine 3. The energy which is provided to the internal combustion engine chemically via the fuel can be determined by a sensor 4a, which records the consumption curve of the internal combustion engine 3. Another possibility is to measure emissions with a gas sensor 4b an exhaust gas analysis device 23, which can also be deduced on the energy consumption of the internal combustion engine. Any power flows of accessories may be approximated by the power consumption of the accessories or sensors determining the mechanical work that the accessories provide in the energy consumption of the internal combustion engine 3.

The method according to the invention further determines the operating state of the device A to be analyzed. In the case of the steering system, such an operating state is For example, a steering, a deflection, a constant steering angle, a deactivation or activation of a steering actuator 16, a servo operation, a manual operation or even a combination of at least two of these operating states. Two of the parameters by means of which these operating conditions can be detected are preferably the steering angle α and its change, an energy consumption of components of the steering system, for example the steering actuator 16, a rotation angle of the steering wheel 20 or its change. If the device A to be analyzed is an internal combustion engine 3, then possible operating states are a coasting operation, a part-load operation, a full-load operation, a deactivation or an activation, a starting operation, an idling operation or even a combination of at least two of these operating conditions. Two of the parameters that can be used to detect these operating conditions are engine speed, throttle position, vehicle speed, intake manifold vacuum, coolant temperature, ignition timing, injection quantity, λ value, exhaust gas recirculation rate, and / or exhaust gas temperature ,

In further operating steps, the driving state of the vehicle is determined based on parameter ranges predefined for one or more driving states 103, 104, 105, wherein a third parameter is detected which is suitable for characterizing a driving state 103 and this parameter with reference parameter ranges for vehicle states 104. From this, the respective measured parameter values can be assigned a driving state 105. Possible driving states here are, for example, gliding at constant speed, acceleration, cornering, parking travel, straight-ahead driving, idling (roll) driving, tip-in, let-off , Constantfahrt, switching, overrun, standstill, uphill, down or even a combination of at least two of these driving conditions. The driving states can be detected via the third parameter, for example the speed, which can be determined via a sensor 6, in particular an incremental encoder. Other third parameters are the state of the clutch (open, closed), detecting the engaged gear or a gear change, the position of the accelerator pedal, the topography of the environment, etc. Both for the determination of an operating state of the device A or B, as for the determination of a driving state, the respective state can be arbitrarily refined, so that parameters up to each individual constellation of parameter values of at least one second or at least one third parameter respectively an operating state or a driving state can be assigned.

From the determined driving state in conjunction with the determined operating state of the device A, it is possible to determine that energy which the respective device A requires to perform its intended function.

In the case of an analysis of the steering system, in order to determine the energy to be provided, a distinction must basically be made between operating states when the steering system is actuated and operating states during a rest phase of the steering system. When the steering system is actuated, force must be provided to overcome internal resistances in the steering system and, depending on whether the vehicle is stationary or stationary, to overcome static friction forces between the tire and the road and the moment of inertia of the wheels 18a, 18c. These factors can be determined by measurements and information provided by the manufacturer, as well as some simple assumptions. In the case of a constant steering angle α (a 5 =) and a moving vehicle 2, although no force must be provided for steering the steering wheel deflections or for overcoming resistances in the steering system, the restoring torque which the wheels have due to the weight force of the vehicle and exercise the geometry of the steering system during cornering. The energy required to overcome this restoring torque can be derived from the steering angle a, the weight of the vehicle and the speed of the vehicle and possibly other assumptions.

In the case of an internal combustion engine 3 as the device to be analyzed A, the energy to be provided is that energy that overcomes the driving resistance and possibly other resistors in the drive train, such. B. Frictions. The driving resistance can be determined by various information about the environment and topography as well as measurements. Vehicle-internal resistances can be determined for example via the sensors 5a, 5c, 5d and 6 in the drive train, which are shown in FIG. Another energy to be provided can be provided for charging the car battery or a larger electrical energy storage, such as the accumulators of a hybrid vehicle, depending on the respective operating strategy of the vehicle 2. From the driving condition of the vehicle 2 and the operating state of the device A can in particular all driving resistance components which act on the vehicle 2, ie the air resistance, the rolling resistance, the pitch resistance and / or the acceleration resistance are determined. In this case, vehicle data which are available, for example, from the manufacturer, such as the vehicle weight and the C _w value are preferably used. Other parameters that change with temperature or driving condition can be determined by sensors and are available as second or third parameters. In particular, the drag coefficient, the end face of the vehicle and the speed are included in the air resistance, the elasticity of the wheel, the tire pressure and the wheel geometry, the road surface condition, which can be determined, for example, on a database, and the road condition. In particular, the vehicle weight and the gradient are included in the gradient resistance, it being possible to determine the gradient for a traveled Δ travel path via a barometric or a GPS altimeter. The acceleration resistance depends in particular on the mass and the acceleration of the vehicle.

From the energy consumption of the device A or of the steering system or the internal combustion engine 3 and their energy to be applied in each case to fulfill their respective function, a characteristic value can be determined 1 1 1 a, which characterizes the energy efficiency of the at least one device A. In the simplest case, this is the ratio of applied energy to energy provided.

Furthermore, further devices B can preferably also be analyzed, for which the method according to the invention preferably has a step of determining at least one second characteristic value 1 1 1 b, likewise based on first, second and third parameters, which characterizes at least one energy efficiency of a further device B. The two characteristics of the energy efficiency of the device A and the Energy efficiency of the device B can be summarized further, preferably in a total value. By predetermining a desired value, for example by means of calculations, in particular vehicle simulations or reference vehicles, reference values or desired value functions can preferably be preset, with which the determined characteristic value can be adjusted in order to determine a rating of the vehicle, 1 15, from which a rating additionally depends. Examples of operating modes here are an efficiency-oriented operating mode, a driving-performance-oriented operating mode, a comfort-oriented operating mode, a consumption-oriented operating mode, an emission-reduction-oriented operating mode, a drivability-oriented operating mode or an NVH comfort-oriented operating mode. As a result, not only the absolute optimum energy efficiency of a device A can be determined with the evaluation, but also relative optima while maintaining further boundary conditions. For a common evaluation of multiple devices, e.g. As a device A and a device B, alternatively for determining a total characteristic value, in each case a characteristic value, a first characteristic value and a second characteristic value, are determined for the energy efficiency of the respective device and preferably evaluated, the individual ratings later in an overall rating can be summarized for the system. In particular, in this way overall characteristics and / or overall ratings for systems of the vehicle 2 can be given, which consist of several components or components, such as the powertrain, the steering or even for the entire vehicle. By means of the invention, complex, random driving courses can be broken down into small evaluable and reproducible individual elements, both driving elements and vehicle elements, with it being possible to identify the individual elements which are most relevant to the overall result or overall characteristic value. In a further step, an optimization of all relevant criteria with regard to the individual element can be optimized taking into account relevant dependency relationships between the criteria. The size of a driving element is preferably determined so that on the one hand a reliable reproducible evaluation can be performed, on the other hand given the characteristic of a finite element, so that any complex driving conditions in turn can be composed reproducibly from the individual driving elements.

An evaluation of the driving elements can be done in particular by clustering, that is, by classification in predefined driving condition categories as well as by determining the frequency of occurrence of the driving elements.

The method 100 may be used in on-line operation with immediate output of the characteristic. This is for example advantageous if the system 1 is completely installed in the vehicle 2 or a test driver wants to retrieve information about the energy efficiency or the vehicle operating behavior during a test drive. However, the method 100 can also be used in off-line operation, where values recorded during a test drive are analyzed. Furthermore, the method 100 can constantly run in the vehicles of vehicle owners and transmit periodically or in real time data for anonymous evaluation to a backend or a central computer. The relationship between a characteristic value and a setpoint value or a setpoint value function is preferably mapped in a mathematical function, so that, if the parameters are entered into the function, the evaluation of the energy efficiency is output as the result of a calculation.

A simple function for calculating a parameter KW can be represented as follows, wherein the value of the factors c depends on the respective determined driving state: KW = c ₂ · parameter + c ₂ ^■ parameter ₂

A calculation of a rating can be done accordingly, in which case the factors c, further depend on a corresponding setpoint function, which serves as a rating reference.

Both the general characteristic value as well as the general evaluation of the efficiency of the vehicle 2 are suitable quantities to previous consumption standards, which based on established driving cycles such as the NEDC (New European Consumption Cycle) or WLTP (Worldwide Harmonized Light Vehicles Test Procedures). Preferably, the characteristic value or the evaluation can also be a topography of an environment of the vehicle 2. In this way, for example, be considered whether a vehicle 2 in its operating strategy, the terrain, z. As the lying in front of the vehicle road, taken into account in order to achieve the best possible energy efficiency. Thus, the operating strategy of a vehicle 2 or a device A of the vehicle 2 z. B. provide that an electric or a compressed air energy storage 15 is fully charged on a sloping track to release this energy on a subsequent mountain driving distance again from the respective energy storage 15 can. To determine the topography, a laser or lidar system can be used from the vehicle side, but the topography can also be determined by means of a GPS system and map material available to the vehicle driver or the vehicle 2.

FIG. 3 shows a partially schematic diagram of the result of a segmentation according to the invention of real-drive measurements, in which an analysis was carried out for the criterion of energy efficiency on the basis of driven driving elements, in particular driving states.

The third parameter for determining the vehicle state is shown in the upper part of the diagram and is the vehicle speed over time, which represents the driving profile of the vehicle 2. In the lower part of the diagram identified driving elements are shown, which are applied individually with respect to the energy efficiency of the vehicle 2 with characteristics or for each of which an assessment is made individually. The efficiency of the vehicle is not averaged over the entire driving course from the outset, as is customary in procedures from the prior art. In the invention, individual driving conditions are identified and these driving conditions of the respective driving resistance of the vehicle and the energy, which in this driving condition consumed is assigned. On the basis of this assignment, a characteristic value is calculated, which represents the energy efficiency of the vehicle in the examined driving condition. A categorization according to the invention is shown by way of example in FIG. The vehicle 2 can be subdivided into modules such as powertrain and body. The individual modules can in turn be divided into components and components. Components of the drive train here are as shown in particular an internal combustion engine (ICE), an electric machine, a transmission and their electrical controls. A device A can be formed by a module, a component or by a component.

If the system 1 or a user determines which device A is to be analyzed when determining the at least one characteristic or the evaluation, its energy consumption can be determined.

In order to determine the energy consumption of a device A which partially consumes energy and partially dissipates the energy, such as an internal combustion engine or an electric machine or the transmission, there may be a need to determine both the energy of each of which to determine the energy consumption Device A is provided, as well as to determine the energy which the device A again gives off, ie with respect to the device A, an energy balance must be established. With regard to a drive device 3 of the vehicle 2, such an inserted energy E (in) is defined via the amount of fuel supplied or also via the carbon emission of the internal combustion engine, in the case of an electric machine via the consumption of electrical energy. With respect to the internal combustion engine, additional energy supplied via additional electric motors, so-called auxiliary units, is added in relation to the inserted energy E (in). The energy E (out) provided by the drive device, which is provided for the propulsion and for further ancillary units in the vehicle, can be tapped on the shaft via rotational speed and torque. If only the efficiency of the combustion process itself is to be determined, then it must also be taken into account that the energy which the Internal combustion engine is provided via electric motors via ancillaries, at the end optionally via the detour of the energy storage 15, is diverted again from the energy obtained by the combustion. However, for the evaluation of the state of development of a vehicle, it is preferable not only to compare with ideal characteristics and courses usually generated in the conceptual phase of an overall development, but also the positioning in a specific benchmark scatter band. This is particularly important for vehicle analyzes in which the basic data required for a setpoint calculation is not completely available. For the preparation of such a database examinations of the most recent vehicles can be carried out.

The actual optimization preferably takes place by transferring the results-relevant individual events into the respectively most suitable development environment. For individual events that primarily concern only one criterion, the optimization often takes place directly in the vehicle in direct interaction with an automated on-line evaluation (for example, compensation for certain drivability errors). For those individual events in which pronounced conflict of interest relationships exist between the different evaluation variables (eg efficiency, emissions, driveability, NVH comfort, etc.), the mapping of the relevant individual events into the XiL (Engl: Software / Hardware in the Loop) is preferable. , Engine and / or powertrain test bench makes sense. Here, the reproducible work according to the teaching of the invention allows an efficient development in the individual driving element, whereby not only an isolated optimization of a single size takes place, but the conflicting goals of the individual criteria can be optimized. In addition, the effects on the overall system "vehicle" can be directly assessed by means of a simultaneously running complete vehicle model.

A comparison with a real drive vehicle library (benchmark data) preferably allows a detailed classification in the competitive environment. This preferably immediate assessability allows a rapid and accurate response and thus a higher agility in the process. The driving element analysis on the basis of the events allows both an efficient calibration capability and an accurate, virtual identification of optimally adapted drive architectures. This also allows the creation of a refined development map, in which the relevant development tasks (both technical and subjective sizes) are marked.

Preferably, a comprehensive real-drive vehicle database is provided with a corresponding statistics on results relevant individual events, as well as a segmented consideration of relevant driving courses, which can address important results relevant tasks accurately not only in the calibration process, but also in the early concept phase of a powertrain or vehicle development ,

Driving conditions which are critical for energy efficiency or for further criteria are preferably mapped on the basis of the physical parameters for this driving state. On the basis of this figure, driving conditions which were determined, for example, in real driving in a real vehicle can be reconstructed on the vehicle roller test bench, on the drive train residue, on the dynamic backlog or in a XiL simulation environment. This makes it possible to examine critical driving conditions in detail at the test stand, for example for the purpose of solving target conflicts between different criteria.

Further aspects of the invention are described in the following exemplary embodiments, which relate in particular to FIGS. 5 to 16. Stricter legal requirements (eg: C02, WLTP, RDE) and increased customer requirements ("positive driving experience") as well as the inclusion of all relevant environmental information ("connected powertrain") result in drastically increased complexity and increasing variety of future drive systems. The development challenges are aggravated by shorter model change cycles and the additional increased involvement of the real customer driving operation ("Real Word Driving").

An efficient development under extended "real word" boundary conditions, such as the extension of the previous synthetic test cycles to the real operation with random driving cycles, requires on the one hand the objectification of subjective variables (eg: driving experience) but also a reproducible determination of complex, Characteristics influenced by stochastics (eg real drive emissions) For this purpose, random driving patterns are broken down into small, reproducible and assessable driving elements and the relevant trade-off relationships (eg drivability, noise perception, efficiency, emission) are optimized in the individual element An intelligent "event finder" makes it possible to concentrate specifically on those driving elements that have a significant influence on the overall result. In addition, a generated "real drive maneuver library" in conjunction with a comprehensive vehicle models forms a crucial basis for moving individual development tasks into the most suitable development environments and thus increasingly into the virtual world.

However, a shortening of the overall vehicle development process requires not only increased frontloading in the development of the individual subsystems, but also an increased cross-functional working in mixed virtual-real development environments. The step from digital mockup (DMU) to functional mockup (FMU) and the consistent evaluation from the overall vehicle view are making a major contribution to making the complexity of future drives manageable within a short development time. With the integrated open Development platform IOPD and the extended evaluation platform AVL-DRIVE V4.0 has created here AVL essential tool and methodological building blocks.

1 . Challenges for drive development

The most important impulses for the further development of passenger car drive systems will come in the medium and long term from both the legislation and the end customer.

The significant reduction of CO2 fleet emissions with imminent penalties, tightened test procedures (WLTP) and the additional limitation of pollutant emissions in real customer driving (Real Driving Emission) represent significant tightening of legal constraints and cause significant additional expenses in vehicle development. On the customer side, on the one hand, the topic of "total cost of ownership" is gaining in importance, on the other hand, purely subjective criteria such as social trends, social acceptance, etc. but especially the "positive driving experience" are increasingly becoming key selling points. Thus, the focus extends from the presentation of purely technical targets such as performance and fuel consumption to the fulfillment of a positive subjective customer experience - the "experience car" goes far beyond the behavior of the powertrain, taking the vehicle's characteristics and values, such as Styling, ergonomics, usability, infotainment and assistance systems, safety, ride comfort, agility, and drivability in a holistic context and as a vehicle behavior true.

Thus, the real driving for the development of new vehicle systems crucially important: not only Real World Emission and consumption, but also the positive driving experience of the customer becomes the key target. However, not only subjective evaluation criteria are subject to rapid fluctuations. New trends, individual demands and new technologies result in a significant unpredictability of a highly dynamic market [1]. The answer to this situation can only be an extremely rapid response to product configuration and development. The short-lived model cycles on the time scale of months, which are already common in the IT sector, are increasingly influencing automotive development through infotainment and assistance systems. So we have to be in the Set up the automotive sector for significantly shorter model change cycles and / or upgradeable solutions and introduce more agile development methods. A sensible technical solution is certainly in advanced modular systems, which enable software in highly diversified solutions. Agile, adaptive and test-based methods of model-based development will support this.

In terms of purely technical aspects, C02 legislation is certainly the most important technology driver. The future C02 and / or consumption fleet limits converge on a constantly reducing level worldwide. On the one hand, this requires complex drive systems with highly flexible components, but on the other hand also requires a more individualized adaptation to the most diverse boundary conditions and results in a multidimensional diversification of the drive systems (different energy sources, different degree of electrification, variant variety, etc.).

In future, networking the drivetrain with the entire relevant vehicle environment ("connected powertrain") will allow optimal adaptation of operating strategies to real traffic situations and environmental conditions.The wealth of information from vehicle infotainment, assistance systems to C2X communication allows many scenarios Thus, the various degrees of freedom of future drive systems can be used to a much greater extent to reduce energy consumption, but this requires highly complex operating strategies with extremely increased development, calibration and, above all, validation effort.

In addition to the secure mastery of this increasing complexity of the drive systems, there is another, very significant influence on the development methodology through the future RDE legislation. This is characterized by the extension of the synthetic test cycle to coincidental real operation with an unmanageable multitude of different driving conditions and boundary conditions.

From a customer perspective, however, Real World Driving involves much more than just RDE: • Positive Driving Experience - Driveability / Comfort / Agility / Usability

• Absolute functional safety

• Highest efficiency and minimum consumption

• Trust in driver assistance systems

· High reliability / durability

2. Driving element-oriented approach in the development process

Moving from the exact reproducibility of tests with well-defined cycles and set metrics to assessing real journeys with statistical randomness, as well as considering the subjectively perceived driving experience, is a major shift and requires both new development approaches and new development environments. Essential basic requirements are: · the objectification of subjective variables (eg: driving experience): With regard to the objectification of the subjectively perceived noise and driveability, AVL has gained decades of practical experience and created corresponding development tools - for example: For example, AVL-DRIVE [2] is well on the way to becoming a widely accepted tool for driveability evaluation.

• Reliable reproducible determination of complex, random-influenced characteristic values (eg Real Drive Emission): A very practicable approach is to break down such complex trajectories into reproducible and assessable segments - the driving elements -, to categorize them and to influence their influence on the vehicle statistically account for the integral characteristic value. This can be analogous to

Discretization of other tasks such. B. the fatigue analysis or process simulation can be seen. The size of these elements is determined by the demand for reproducible assessability. Here, the subjective perception of the human being becomes the reference variable for other evaluation parameters such as consumption, emissions, etc. The really decisive step, however, is the ability to identify from the large number of individual elements those that have a significant relevance to the overall result. Such a method has been used successfully at AVL for years in the field of drivability development (AVL-DRIVE). An arbitrary Real World driving course is broken down into defined individual elements, then assigned to around 100 individual categories, evaluated separately according to around 400 specific assessment criteria and statistically evaluated.

With comparably low adaptations, this method of using categorizable driving segments can be applied not only to assess drivability and noise comfort under real conditions, but also emissions, efficiency, and also for lateral dynamics, to the evaluation of driver assistance systems [3].

If one looks at the results of real-world measurements, it becomes apparent that there are individual driving elements that are only relevant to the overall rating in terms of an optimization variable. As a rule, however, the same driving elements are decisive for emission, efficiency, drivability and noise comfort. Due to these mutual dependencies, the goal conflicts within the individual driving element must be solved here.

By means of an intelligent "event finder", "bottlenecks" can be reliably identified. The identification of these "events" - that is, driving elements relevant to the result - requires the on-line specification of corresponding setpoint values for these travel elements and the comparison with the respectively measured actual values.The setpoint values for the individual evaluation variables are generated in different ways: · Efficiency: The On A setpoint calculation takes place in a complete vehicle model synchronized with the vehicle measurement, based on the measured longitudinal vehicle dynamics and taking into account the current topography as well as other driving resistances. The vehicle model includes not only the entire hardware configuration, but also the corresponding operating strategies. Of course, an accounting of all energy flows and energy storage is required.

• Emissions: In principle, the target value specification could be made in the same way as the "efficiency" evaluation variable, but in view of the upcoming RDE legislation, it makes more sense to carry out the assessment in accordance with the future RDE provisions enshrined in the legislation.

• Drivability: Here, the setpoint specification is based on objective subjective driving experience and specification of a desired vehicle characteristic according to the system developed in AVL-DRIVE [2]. For the objectification of subjective driving sensations, human sensations often have to be correlated with physically measurable variables via neural networks.

• NVH: Similar to the driveability, the setpoint specification is performed based on the objective subjective sound sensation and specification of the desired sound characteristic (e.g., AVL-VO ICE [4]).

However, when assessing the level of development of a vehicle, it is not only the comparison with ideal values and processes usually generated in the concept phase of an overall development that is of interest, but also the positioning in a specific benchmark scatter band. This is particularly important for vehicle analyzes in which the basic data required for a setpoint calculation is not completely available. In order to ensure sufficient statistical relevance of current benchmark data (Real Drive Maneuver Library), AVL performs eg. In 2014 alone, around 150 benchmark examinations of the most recent vehicles were carried out. The actual optimization takes place by transferring the results-relevant individual events into the most suitable development environment. For individual events, which primarily concern only one evaluation variable, optimization takes place often directly in the vehicle in direct interaction with the automated on-line assessment (eg compensation for certain driveability errors).

For those individual events in which pronounced trade-off relationships exist between the different evaluation parameters (eg efficiency, emissions, driveability, etc.), it makes sense to map the relevant individual events on the XiL, engine and / or powertrain test bench , Here, the reproducible work permits efficient development in the individual driving element, not just an isolated optimization of a single size is done, but the ^trade-off's (typically emission / efficiency / driveability / noise) can be optimized. In addition, the effects on the overall system can be directly assessed by means of a simultaneously running complete vehicle model. In addition, the comparison with a "real driving maneuver library" (benchmark data) allows a detailed objective classification in the competitive environment.This immediate status assessment enables a rapid and accurate response and thus a higher agility in the development process.

The driving element analysis on the basis of an intelligent event finder allows both an efficient calibration capability and an unerring, virtual identification of optimally suitable drive architectures. This also allows the creation of a refined development map that highlights the relevant development tasks (both technical and subjective).

The availability of a comprehensive maneuver database with corresponding statistics on results-relevant individual events, as well as a segmented consideration of relevant driving profiles, is thus not only essential in the calibration process, but also in the early concept phase of a powertrain development for an accurate addressing of important results-relevant tasks.

3. Simultaneous mastery of development processes at several development levels

In addition to a segmentation of complex driving courses into small assessable individual elements (vertical segmentation), a categorization of the system integration of the Whole vehicle into different system and component levels (horizontal categorization) a proven basis for efficient development processes.

The networking of the in-vehicle data and control network with the environment ("Connected Powertrain") results in an additional superordinate system level, the "Traffic Level".

The segmentation of driving histories originally started at the vehicle module level with the optimization of the longitudinal dynamics behavior of the powertrain (drivability optimization) and was broken down to the level of the individual powertrain modules (eg engine, transmission, etc.).

A comprehensive acoustic and comfort rating, on the other hand, already requires segmentation at the vehicle level. It is also necessary to act on the vehicle level for the development of lateral dynamics-relevant functions (such as suspension tuning through to vehicle dynamics regulations [5]).

For the objectified evaluation of Advanced Driver Assistance Systems (ADAS), networking with all relevant environmental information and thus the inclusion of the highest system level ("traffic level") is necessary.

Also for most optimizations on vehicle or traffic level basically similar requirements apply with regard to the segmentation of complex driving courses and the objectification of subjective variables. The tools already used for evaluating powertrain longitudinal dynamics can also be used to optimize lateral dynamic functions [2]. However, since the segmentation of the driving courses for longitudinal and lateral dynamic aspects differ and (with the exception of the vehicle dynamics control) hardly trade-off relationships exist, at present still a separate processing of longitudinal and lateral dynamic tasks in terms of a controllable development complexity seems to be expedient. In racing, on the other hand, longitudinal and lateral dynamic problems are already being optimized across the board. Although the essential subsystems (eg powertrain, body & chassis, electrics & electronics) are developed along their own processes at the vehicle module level, the overall vehicle development process is the dominant reference variable for all other system developments. The total vehicle development thus synchronizes all individual development tasks and also controls the development of software and hardware integration levels (concept and prototype vehicles) with predefined functions. However, the fact that in general the development processes of the individual subsystems run in different timelines is aggravating. Thus, the common synchronization points in the overall vehicle development process (integration levels 1 to X) not only require work on a purely virtual or purely real basis, but increasingly also in mixed virtual-real development environments. A key to mastering the complexity of today's and tomorrow's drive concepts is the early functional integration of the subsystems in an overall system that can be fully, partially or even virtually available. The now well-established, purely real integration process (with real hardware and software) will be expanded in the sense of frontloading to include earlier development phases in purely virtual and combined virtual-real development environments.

Thus, developments at module or component level can be analyzed and developed in the overall vehicle context even if no complete vehicle prototypes are available. Complex relationships can thus be evaluated early on in purely virtual or combined virtual / real development environments and thus bring the transition from digital mockup (DMU) to functional mockup (FMU).

Although the final safeguarding of the functions continues in the vehicle, increased frontloading is also used here. With the new possibilities of a combined virtual-real development process, the rapidly increasing number of subtasks in the development can be handled not only efficiently, but already in earlier stages of development. Only in this way will the complexity of drive development be manageable in the future.

During the entire development process, an assessment from the perspective of the entire vehicle under relevant operating conditions (driver + road + environment) is required. For this, virtual and real experiments are coupled via a parallel running vehicle model.

Both the functional development and initial validations of the internal combustion engine run on stationary and dynamic engine test benches. The development of the engine control and the corresponding software functionalities incl. Diagnostic functions is expediently outsourced to XiL test benches. The parallel running virtual total vehicle model (residual vehicle) with driving resistances, body, axles, suspension, steering, brake system allows a continuous assessment of the target achievement with regard to vehicle consumption, emission, and dynamics.

In particular, for the tuning, calibration and validation of hybrid functions, placing the engine, transmission and electric motor hardware on the powertrain test bench is a highly efficient development environment. However, all development tasks that do not require the entire powertrain hardware (eg, development / Calibration of diagnostic functions), are processed in parallel in a XiL environment.

Depending on the task and available vehicle hardware, the test is carried out on the powertrain test stand with or without a vehicle, on the chassis dynamometer and on the road in the subframe or vehicle prototype. Since the test conditions (driver, distance, load, wind, altitude, climate, etc.) as well as the parameters of the rest of the vehicle (driving resistance, body, axles, suspension, steering, etc. - variant simulation) can be varied comparatively quickly on the powertrain test stand, it is itself With the availability of the entire hardware, including the vehicle, it is often advantageous to carry out both the development and the validation of complex systems (for example of a completely new hybrid system) on the drive train test bench. The division of work content into the most suitable development environment is becoming increasingly important, especially in the area of validation. The combination of drastically increasing system complexity and shortened development times requires increased frontloading not only in functional development, but also in functional validation in particular. The validation in the overall system is no longer based solely on hardware but in a wide variety of combinations of real and virtual components in mixed virtual-real development environments (eg "virtual highway at the test bench - virtual route - virtual driver).

For complex systems, efficient and comprehensive validation of functional safety is crucial. Base for validation in this case represents a precise generated collective relevant test sequences, the ^'s must be created by means of a detailed system analysis, evaluation and classification of possible operating and abuse scenarios and comprehensive FMEA (Failure Mode and Effects Analysis). By means of a high degree of systematization and automation, potentially critical operating conditions can be checked in a much shorter time than in the conventional road test. Of course, the pre-selection of these potentially critical states involves the risk that the test program will only provide answers to explicitly asked questions, but that other risk points will not be addressed. Additional validation sequences generated from the maneuver database will reduce this risk in the future.

4. From the DMU (Digital Mock Up) to the FMU (Functional Mock Up) or from the "tool chain" for the classic development process to the "tool network" for an integral, multi-layered development process , Numerical component models and actually available hardware construction stages already today and strengthened in the future many times a "jumping" between virtual and "real" experiment, today many times the "real" experiment involves simulations. For agile development, simulation and hardware must be seamlessly intermeshed and interchangeable. In many cases, the required consistency of development tools is not yet available. The integrated, open development platform AVL-IODP (Integrated Open Development Platform) consistently maps this continuity of the entire development environment.

Key aspects of the consistent application of an integrated, integrated development platform, which is also open to a wide variety of tools, are:

• Consistent processes and methods allow a "front loading" of development tasks that were previously carried out, for example, largely on the road, in earlier development phases on the engine or powertrain test stand - in extreme cases also in a purely virtual simulation environment (Office Simulation). Thus, for example, the pre-calibration of an engine in a combined real-virtual development environment with a comparable result quality can be carried out much more quickly than in a purely road test.

• Consistency of the simulation models: Situational models created in early development stages can also be reused in downstream development phases and environments. These simulation models complement (as virtual components) the hardware development environments (i.e., test benches) to a mixed virtual-real development environment that can be used to represent interactions at the overall vehicle level.

• Consistent comparability of virtual and real experiments through consistent data management and seamless consistency of models and methods. On the one hand, results generated by simulation must be consistent with the corresponding real experiments and, on the other hand, in the course of the development process also allow a further development of the simulation models on the basis of test results. The possibility of this constant, consistent alignment between virtual, real and combined virtual-real world is the prerequisite for an agile, modern development process. • Consistent parameterization of models and tests: Especially in the case of ECU calibration, a large number of input parameters must be set, such as: As environmental conditions, driving maneuvers, calibration records, etc. are managed. In order to be able to compare the results between the virtual and the real experiment later, the input data sets must be comparable and consistent in the

Process be available.

Consistent integration into existing process environments: Of course, it is necessary to be able to continuously integrate new or improved development tools into existing processes and process environments. Therefore, such a development platform must be open in the sense of, on the one hand, the integration of virtual, real and combined virtual-real tools and, on the other, data management. Preferably, a "bottom up approach" is sought, which also allows existing tools and tools to be integrated, thereby allowing to build on existing know-how and well-established tools.

Thus, this development platform IODP becomes the basis for a continuous, model-based development process and expands conventional tool chains into an integrated and consistent network: "From a sequential tool chain to tool network." In this platform, virtual and real components of the drive can be used at any time The development of the development process can be integrated into the overall vehicle level and the appropriate development environments can be configured, making this tool network a toolkit for the most agile development process possible.

Consequently, the networking of development tools also requires a networked evaluation platform in which the development result can be permanently evaluated not only at the component and system level, but also at the overall vehicle level.

A drivability assessment with AVL-DRIVE has been a first approach towards an overarching evaluation platform for years. The structure of this Assessment platform makes it possible to carry out an end-to-end driveability evaluation with all relevant tools - from office simulation to the road test of the real vehicle. AVL DRIVE-V 4.0 extends this evaluation platform in the next expansion stages o Emission evaluation in accordance with the requirements of RDE legislation o Efficiency evaluation with on-line calculation of the ideal target value incl.

Positioning in the benchmark environment

o Evaluation of subjective noise perception

This enables a consistent evaluation of the most important assessment variables from simulation over engine, powertrain and chassis dynamometer to road tests. 5. Outlook

The consistent continuation of these model-based development methods with vehicle-element-based assessment will allow Advanced Driver Assistance Systems (ADAS), automated driving and the Connected Powertrain in the "Connected Vehicle" group to be developed in a virtual environment in the future, thus providing comprehensive frontloading Effective implementation of the approach [2]. As an extension to the test bench and simulation setup, the road, infrastructure, traffic objects and the corresponding environmental sensors such as radar, lidar, ultrasound, 2D and 3D camera on the powertrain test rig as residual vehicle and environment must also be simulated here. In order for the card-based functions, such as for predictive energy management based on the navigation system (eg e-Horizon), to work in the test cell, GPS signals can be emulated and sent anywhere in the world. Finally, with the structure shown, the functional safety, the correct functions and the performance with regard to emission, consumption, driving performance, safety and comfort behavior in various driving maneuvers and Evaluate traffic scenarios in the overall network as well as the subjective driver perception reproducibly.

The increasing complexity of development tasks and the need to handle comprehensive tooling networks instead of tool chains in the future make it increasingly difficult for design engineers to make the best use of all these tools and to correctly evaluate the feedback and results of virtual and real tests and into the others To incorporate development. It will therefore be necessary to make the tools themselves even more "intelligent" into "smart cyber-physical systems". Such "intelligent" tools will help the engineer better in their work, understanding the physical processes of the UUT and the context of the development task, and understanding the measurement data, from automatic data plausibility to efficient analysis and interpretation Nevertheless, these increasingly complex tasks in overarching development environments also require a generic working method of the developer - the "networked development engineer" - who, among other things, can quickly move between different system levels.

Literature:

[1] List, H. O.: "Future propulsion systems in a rapidly changing global environment";

International Vienna Motor Symposium, 7.-8. May 2009

List, H .; Schoeggl, P .: "Objective Evaluation of Vehicle Driveability", SAE Technical

Paper 980204, 1998, doi: 10.4271 / 980204

[3] Fischer, R; Küpper, K .; Schöggl, P .: "Drive Optimization through Vehicle Networking"; 35th International Vienna Motor Symposium, 8-9 May

2014

[4] Biermayer, W .; Thomann, S .; Brandl, F .: "A Software Tool for Noise Quality and Brand Sound Development", SAE 01 NVC-138, Traverse City, April 30 - May 3, 2001

[5] Schrauf, M .; Schöggl, P .: "Objectivation of the Driveability of Automated / Autonomous Driving", AVL Motor und Umwelt Conference 2013, 5-6 September 2013, Graz

[6] Hirose, T .; Sugiura, T .; Wake, T :; Pfister, F .: "How To Achieve Real-Life Test Coverage Of Advanced 4-Wheel Drive Hybrid Applications," CTI Berlin, 2013 LIST OF REFERENCE NUMBERS

System 1

Vehicle 2

Drive device 3

First device 4

Second device 5a, 5b, 5c, 5d

Third device 6

First comparison device 7

Assignment device 8

Processing device 9

Fourth device 10

Second comparison device 1 1

Output device 12

Selection device 13

Fifth device 14

Energy storage 15

Steering actuator 16

Steering control 17

Wheel 18a, 18b, 18c, 18d

Gear 19

Steering actuator or steering wheel 20

Differential 21

Exhaust gas aftertreatment 22

Exhaust gas analysis device 23

Exhaust system 24

Data memory 25

Claims

claims

System (1) for analyzing an energy efficiency of a motor vehicle (2), comprising: a first device (4a, 4b), in particular a sensor, configured to detect a first data set of at least one first parameter which is suitable, an energy which at least one Device A, in particular a steering or a drive device (3), consumed to characterize;

a second device (5a, 5b, 5c, 5d), in particular a sensor, configured to detect a second data set of at least one second parameter which is suitable for characterizing at least one operating state of the at least one device A;

third means (6) arranged to detect a third data set of at least one third parameter which is suitable for characterizing at least one driving condition of the vehicle (2);

a first comparison device (7), in particular part of a data processing device, configured to compare the values of the second data set with predefined parameter regions which correspond to at least one operating state and to compare the values of the third data set with predefined parameter regions corresponding to at least one driving state;

an association device (8), in particular part of the data processing device, configured to associate the values of the first data record and the values of the second data record with the respective at least one driving state present; and

a processing device (9), in particular part of the data processing device, configured to determine at least one first characteristic which at least characterizes the energy efficiency of the at least one device A, based on the first data set and the second data set as a function of the at least one driving state.

System (1) according to claim 1, characterized in that the at least one first parameter further characterizes a power consumption of at least one further device B, that further comprises at least one second parameter characterized in that the at least one further device B is in use and that the processing means (9) is further adapted to determine at least one second characteristic which at least characterizes the energy efficiency of the at least one further device B based on the first data set and the second data set Dependence of the at least one driving state and for combining the at least one first characteristic, which characterizes at least the energy efficiency of the at least one device A, with the at least one second characteristic, which characterizes at least the energy efficiency of the at least one further device B, for each same driving state to one Overall characteristic, which characterizes an energy efficiency of the system of the at least two devices A and B.

The system (1) according to claim 1, characterized by further comprising:

a fourth device (10), in particular an interface, configured to detect a desired value for the at least one characteristic value, in particular based on a vehicle model or a reference vehicle;

a second comparison device (1 1), in particular part of a data processing device, configured to match the characteristic value with the desired value for determining a rating; and

an output device (12), in particular a display, configured to output the rating based on the matching.

A system (1) according to any one of claims 1 to 3, characterized in that it further comprises memory means (25) arranged to store a sequence of driving conditions, and that the processing means (9) is further adapted to Determining the characteristic to take into account the sequence of driving conditions.

5. System (1) according to one of claims 1 to 4, characterized in that the processing device (9) is further set up, an assignment of the values of the first data set and the second data set to the at least to correct a predefined driving state, in order to take into account a signal propagation time and / or a transit time of at least one measuring medium for detecting the respective data set up to a sensor (4a, 4b, 5a, 5b, 5c, 5d, 6).

Method (100) for assessing and / or optimizing energy efficiency of a motor vehicle (2), comprising the following working steps:

Detecting (101) a first data set of at least one first parameter which is suitable for characterizing an energy which consumes at least one device A, in particular a steering system or a drive device (3);

Detecting (102) a second data set of at least one second parameter which is suitable for characterizing at least one operating state of the at least one device A;

Detecting (103) a third data set of at least one third parameter which is suitable for characterizing at least one driving state of the vehicle (2);

Comparing (104) the values of the second data set with predefined parameter ranges which correspond to at least one operating state and the values of the third data set with predefined parameter ranges which correspond to at least one driving state;

Associating (105) the values of the first data set and the values of the second data set with the respective at least one driving state present; and determining (1 1 1 a) at least one first characteristic value which at least characterizes the energy efficiency of the at least one device A, on the basis of the first data set and the second data set as a function of the at least one driving state.

A method according to claim 6, characterized in that it further comprises the following steps:

Detecting (1 13) a target value for the at least one characteristic value, in particular based on a vehicle model or a reference vehicle;

Comparing (1 14) the characteristic value with the desired value to determine a valuation; and Issue (1 16) the energy efficiency assessment based on the reconciliation (1 15).

8. The method according to claim 6 or 7, characterized in that the at least one first parameter further characterizes a power consumption of at least one further device B, that the at least one second parameter further characterizes an operating state of the at least one further device B and that the method further comprises the following steps:

Determining (1 1 1 b) at least one second characteristic value, which at least one

Energy efficiency of the at least one further device B characterized on the basis of the first data set and the second data set in dependence on the at least one driving state;

Combining (1 12) the at least one first characteristic value, which characterizes at least the energy efficiency of the at least one device A, with the at least one second characteristic value, which characterizes at least the energy efficiency of the at least one further device B, for each same driving state to a total characteristic value an energy efficiency of the system from the at least two devices A and B characterized.

9. The method according to claim 8, characterized in that the overall characteristic characterizes the energy efficiency of a drive train, a steering or the entire vehicle 2. 10. The method according to claim 6 or 7, characterized in that the first data set further characterizes a power consumption of at least one further device B, that the second data set further characterizes an operating state of the at least one further device B, wherein the at least one device A of at least one further device B provides energy, and that the method further comprises the step of:

Cleaning up (109) the energy consumption of the at least one device A by the energy consumption of the at least one further device B.

1 1. Method according to one of claims 6 to 10, characterized in that the device A is the steering, or one of these components or components, and that in particular in each case an operating state of the steering is present from at least the group of the following operating states:

Steering, steering (steering angle), constant steering angle, activated and deactivated state of a steering actuator 16, servo operation, manual operation or even a combination of at least two of these operating states. 12. The method according to any one of claims 6 to 1 1, characterized in that the device A, the drive means (3), or one of these components or components, and in particular each an operating state of the drive means (3) from at least the group of the following operating states is present:

Shifting operation, partial load operation, full load operation, deactivated condition, activated condition, starting operation, idle operation or even a combination of at least two of these operating states.

13. The method according to any one of claims 6 to 12, characterized in that the at least one second parameter is further suitable to characterize a topography of an environment of the vehicle (2).

14. The method according to any one of claims 6 to 13, characterized in that the at least one device A is an internal combustion engine or a fuel cell system and the first parameter indicates at least one emission of the internal combustion engine or the fuel cell system.

15. The method according to any one of claims 6 to 14, characterized in that the steps are carried out so long that the third record extends over several different driving conditions.

16. The method according to any one of claims 6 to 15, characterized in that it further comprises the following operation: Determining (107) the sequence of driving conditions, taking into account the sequence of driving conditions when determining the characteristic value.

17. The method according to any one of claims 6 to 16, characterized in that the values of the first data set and / or the second data set over the period of the respective control station are integrated.

18. The method according to any one of claims 6 to 17, characterized in that the values of a plurality of third data sets are summarized for a same type of driving state for determining the at least one characteristic value.

19. The method according to any one of claims 6 to 18, characterized in that it further comprises the following operation:

Correcting (108) an assignment of the values of the first data set and the second data set to the at least one predefined driving state, a signal propagation time and / or a transit time of at least one measuring medium for detecting the respective data set up to a sensor (4a, 4b, 5a, 5b, 5c, 5d, 6).

20. The method of claim 7, further comprising the following operation:

Determining (1 15) an operating mode of the vehicle on which the evaluation additionally depends and which is selected in particular from the following group of operating modes:

Efficiency-oriented operating mode, mileage-oriented operating mode, comfort-oriented operating mode, consumption-oriented operating mode, emission reduction-oriented operating mode, drivability-oriented

Operating mode, NVH comfort mode.

21. A computer program having instructions which, when executed by a computer, cause it to execute a method according to any one of claims 6 to 20.

22. Computer-readable storage medium on which a computer program according to claim 21 is stored.