Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M99/00—Subject matter not provided for in other groups of this subclass
  • G01M99/004—Testing the effects of speed or acceleration
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M17/00—Testing of vehicles
  • G01M17/007—Wheeled or endless-tracked vehicles
  • G01M17/06—Steering behaviour; Rolling behaviour
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
  • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
  • B60W30/14—Adaptive cruise control
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
  • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
  • B60W50/0097—Predicting future conditions
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M15/00—Testing of engines
  • G01M15/02—Details or accessories of testing apparatus
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M15/00—Testing of engines
  • G01M15/04—Testing internal-combustion engines
  • G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M17/00—Testing of vehicles
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M17/00—Testing of vehicles
  • G01M17/007—Wheeled or endless-tracked vehicles
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
  • G06F30/20—Design optimisation, verification or simulation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
  • G06F30/20—Design optimisation, verification or simulation
  • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
  • G06N3/09—Supervised learning
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N7/00—Computing arrangements based on specific mathematical models
  • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/40—Business processes related to the transportation industry
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/01—Detecting movement of traffic to be counted or controlled
  • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
  • G08G1/0125—Traffic data processing
  • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/01—Detecting movement of traffic to be counted or controlled
  • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
  • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
  • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
• • G—PHYSICS
  • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
  • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
  • G09B9/00—Simulators for teaching or training purposes
  • G09B9/02—Simulators for teaching or training purposes for teaching control of vehicles or other craft
  • G09B9/04—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles
  • G09B9/042—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles providing simulation in a real vehicle

Definitions

• the invention relates to a computer-assisted method and a device for generating a driving cycle for a vehicle, which is suitable for simulating ferry operation, in particular real ferry operation.
• Emission regulations for vehicles with internal combustion engines are subject to constant changes, which aim to take into account driving conditions that are increasingly closer to real driving conditions on the road.
• An example of such emission guidelines is the European Union's regulation of test procedures to determine emissions from vehicles under real driving conditions, so-called Real Driving Emissions (RDE).
• RDE Real Driving Emissions
• These test procedures are part of the approval procedure for vehicle types, for example. Emission tests can therefore no longer be carried out exclusively on a vehicle test stand with generally defined driving cycles, but must be carried out under real driving conditions, for example in order to take into account the influence of real traffic conditions and the real driving behavior of a driver.
• RDE-compliant driving cycle which serves as the basis for emission determination in accordance with the guidelines.
• emission guidelines with the aim of taking into account real driving conditions on the road allow a large number of different driving cycles, which means that vehicle manufacturers have to carry out an enormous amount of testing during vehicle development.
• the consumption is typically determined for approx. 1000 RDE-compliant driving cycles. This test effort can be reduced by simulating a large number of different, guideline-compliant driving cycles that take realistic driving behavior into account.
• driving cycles can be simulated using Markov chains or neural networks. The ones generated in this way However, driving cycles show significant deviations from driving cycles measured under real road conditions. Alternatively, short driving distances measured under real road conditions can be combined in different ways to generate a driving cycle. However, driving cycles generated in this way are relatively similar to one another and therefore only offer insufficient variability to determine a real, average vehicle consumption.
• a first aspect of the invention relates to a computer-assisted method for generating a driving cycle for a vehicle, which is suitable for simulating, in particular, real, ferry operation.
• the computer-aided method includes determining a state vector of the driving cycle for a current time interval from a speed history of the past, providing an acceleration prognosis model, determining an acceleration value taking into account probabilities resulting from the acceleration prognosis model and the state vector, integrating the determined acceleration value the current time interval to obtain a predicted speed value for a next time interval in the future and appending the predicted speed value to the past speed history to generate the driving cycle.
• a driving cycle within the meaning of the invention is in particular a time interval to which a constant speed value is assigned, or a chronological sequence of a plurality of time intervals to which a constant speed value is assigned in each case.
• a current time interval of a driving cycle within the meaning of the invention is in particular a time interval which directly follows past time intervals of the Driving cycle connects and which is assigned a current speed value, which can have a finite amount or be zero.
• a speed profile of the past within the meaning of the invention is in particular a current time interval to which a speed value is assigned and/or a past time interval to which a speed value is assigned and/or a large number of past time intervals to which a speed value is assigned.
• a velocity value can be of finite magnitude or zero.
• An acceleration value within the meaning of the invention is a positive value in the case of a positive acceleration or a negative value in the case of a negative acceleration, also referred to here as deceleration.
• a state vector of a driving cycle for a current time interval within the meaning of the invention is in particular a vector whose components correspond to one or more speed values and/or one or more acceleration values and/or one or more values of a change in acceleration and/or one or more values which correspond to a Show number of time intervals.
• An acceleration prognosis model within the meaning of the invention is in particular a model for determining one or more acceleration values for a current time interval or for one or more time intervals which follow the current time interval in terms of time.
• An acceleration prognosis model within the meaning of the invention can also be used as a conditional acceleration prognosis. called Conditional Acceleration Prediction (CAP).
• CAP Conditional Acceleration Prediction
• the invention is based in particular on the approach that a state vector, which represents the current state of a driving cycle at a current time interval, is determined from a speed profile in the past, i.e. at least one speed value which is assigned to a current time interval and/or at least one past time interval , and using the state vector and a probability-based acceleration prediction model acceleration value for the current time interval is determined.
• a state vector which represents the current state of a driving cycle at a current time interval
• a speed profile in the past i.e. at least one speed value which is assigned to a current time interval and/or at least one past time interval
• a probability-based acceleration prediction model acceleration value for the current time interval
• the computer-assisted method for generating a driving cycle for a vehicle according to the present invention has the advantage over the prior art that any number of driving cycles can be generated, which on the one hand do not resemble each other in terms of their speed profile and on the other hand are very similar to one another through the use of the acceleration forecast model driving cycles measured under real conditions.
• the determination of an acceleration value involves determining, using the acceleration prognosis model as a function of the state vector, a probability value for a current scenario of an acceleration and a probability value for a current scenario of deceleration and a probability value for a current scenario of a constant velocity state.
• Determining an acceleration value considering probabilities further includes randomly selecting, for the current time interval, an acceleration, deceleration, or constant velocity scenario based on the probability values for current acceleration, deceleration, and constant velocity scenarios and/or Determining, using the acceleration prognosis model depending on the state vector, a probability distribution of acceleration values of the randomly selected scenario and randomly selecting, for the current time interval, an acceleration value based on the probability distribution of acceleration values of the randomly selected scenario.
• a current scenario within the meaning of the invention is in particular an acceleration, a deceleration or a state of constant speed in a current time interval.
• a random selection within the meaning of the invention is in particular a drawing of a random sample or a random drawing in the statistical sense.
• Randomly selecting an acceleration, deceleration, or constant velocity scenario based on probability values for current acceleration, deceleration, and constant velocity scenarios, and randomly selecting an acceleration value for the current time interval based on a probability distribution of acceleration values has the following advantages: Outbound From the same speed profile of the past, a large number of driving cycles can be generated with the help of the acceleration prognosis model, which are not similar to each other. In addition, these offer sufficient variability to determine the average fuel consumption of a vehicle under real driving conditions.
• the driving cycle is generated by iteratively executing the working steps of the method in the order listed and the predicted speed values are each appended to the speed profile of the past from a previous iteration.
• multiple predicted speed values are obtained for the same time intervals in the future based on the speed profile of the past, so that statistical speed distributions are obtained for time intervals in the future.
• the statistical speed distributions for the time intervals in the future allow a statistical evaluation of the generated driving cycle. For example, a driving cycle can be created in this way, the speed values of which correspond to the respective expected value of the statistical speed distributions.
• the state vector has at least one current speed value and/or one or more speed values for a current time interval of the past and/or one or more acceleration values of one or more time intervals and/or one or more values of a change in acceleration of one or more time intervals and/or a value corresponding to a number of time intervals according to the duration of an ongoing acceleration maneuver and/or a value corresponding to a Number of time intervals according to the duration of a currently ongoing deceleration maneuver and/or a value corresponding to a number of time intervals according to the duration of a currently ongoing constant speed state.
• a change in acceleration of a time interval within the meaning of the invention is in particular the difference between an acceleration value within the time interval and an acceleration value in a preceding time interval.
• An acceleration maneuver within the meaning of the invention is in particular an uninterrupted acceleration process of any acceleration values over one or more time intervals, which started in the past, i.e. in a past time interval before the current time interval.
• a currently ongoing acceleration maneuver means that the acceleration maneuver continues up to the time interval immediately prior to the current time interval.
• a deceleration maneuver within the meaning of the invention is in particular an uninterrupted process of deceleration of any negative acceleration values over one or more time intervals, which started in the past, i.e. in a past time interval before the current time interval.
• a currently ongoing deceleration maneuver means that the deceleration maneuver continues up to the time interval immediately prior to the current time interval.
• a state of constant speed within the meaning of the invention is in particular the maintenance of a constant speed value over one or more time intervals, which started in the past, ie in a past time interval before the current time interval.
• a currently ongoing state of constant speed means that the state is more constant speed up to the time interval immediately before the current time interval.
• the state vector of a current time interval has a value corresponding to the duration of a currently ongoing acceleration maneuver, a value corresponding to the duration of a currently ongoing deceleration maneuver and/or a value corresponding to the duration of a currently ongoing constant speed state
• the duration of acceleration maneuvers, deceleration maneuvers and/or or the duration of a state of constant speed within a part of a driving cycle that has already been generated influences the further course of the driving cycle.
• the probability determined according to the present invention for the continuation of the acceleration maneuver in the current time interval and in future time intervals of a driving cycle is influenced by the duration of an acceleration maneuver in the past of the driving cycle.
• the determined probability distribution of acceleration values of a randomly selected scenario for a current time interval and future time intervals is also dependent on the duration of an acceleration maneuver, deceleration maneuver or state of constant speed in the past of the driving cycle. This has the advantage that the similarity of the generated driving cycle to driving cycles measured under real conditions is further increased.
• the acceleration value which is determined taking into account probabilities resulting from the acceleration prognosis model and the state vector, is based on the duration of a currently ongoing acceleration maneuver, a currently ongoing deceleration maneuver or a currently ongoing state of constant speed .
• the speed profile of the past has at least one speed value.
• the at least one speed value can have a finite magnitude or can be zero. This has the advantage that a driving cycle can be generated from a single speed value.
• the current scenario of an acceleration and/or the current scenario of a deceleration and/or the current scenario of a state of constant speed each have a probability distribution of acceleration values.
• This enables a statistical evaluation of the generated driving cycle.
• a driving cycle can be created in this way, the speed values of which are based on acceleration values that correspond to the respective expected value of the probability distributions of acceleration values in the individual time intervals.
• an expected value of the probability distribution of acceleration values is set based on the speed profile of the past.
• the expected value of the modeled probability distribution is preferably derived based on the current speed value and a speed value from the past.
• the expected value of the modeled probability distribution is preferably set based on the current speed value and the direct temporal predecessor of the current speed value.
• the acceleration prognosis model is based on a statistical Evaluation of measured driving data of at least one real vehicle, the measured driving data of the at least one real vehicle preferably exclusively having a time sequence of speed values.
• the driving data of the real vehicle measured under real driving conditions are preferably used for model training, in particular for determining model parameters. This has the advantage that the similarity between the driving cycles generated using the acceleration prognosis model and driving cycles measured under real conditions is increased.
• Another aspect of the invention relates to a method for driving a vehicle using an adaptive cruise control system, in particular a driver assistance system, in particular for predictive driving functions, the driving cycle of the vehicle driving ahead of the vehicle being determined using a computer-assisted method according to one of the specified embodiments.
• the distance between the vehicle and the vehicle ahead is not used as an input value or boundary condition for the adaptive cruise control, the distance between the vehicle and the vehicle ahead preferably being based on a solution of a cost function or cost optimization function based.
• the distance between the vehicle and the vehicle driving ahead is therefore not a constant variable, which has the advantage that the distance can be adapted to current traffic conditions.
• a further preferred embodiment of the method for driving a vehicle several predicted speed values are obtained for the same time intervals in the future based on the speed profile of the past, so that statistical speed distributions are obtained for time intervals in the future, with safety conditions for driving from the statistical speed distributions of the vehicle can be derived.
• These safety conditions for driving the vehicle include, in particular, determining a minimum distance between the vehicle and the vehicle driving ahead, which distance must be maintained to prevent the two vehicles from colliding. As a result, safety when driving the vehicle can be increased.
• a further aspect of the invention relates to a method for generating a driving cycle for a vehicle, which is suitable for use by driver assistance systems, in particular for predictive driving functions, and has the working steps of a method for generating a driving cycle according to one of the specified embodiments.
• Another aspect of the invention relates to a method for analyzing at least one component of a motor vehicle, the at least one component or the motor vehicle being subjected to a real or simulated test operation based on at least one driving cycle, which is carried out using a method for generating a driving cycle according to one of the above Embodiments is determined.
• the method for analyzing at least one component of a motor vehicle includes checking the conformity of the multiple predicted speed values with at least one boundary condition, in particular Real Driving Emissions (RDE) guidelines, after a defined number of iterations has elapsed, with in particular Checking takes place periodically, in each case after the specific number of iterations has elapsed, with the specific number of iterations corresponding in particular to a predefined overall time interval, for example 5 minutes.
• RDE Real Driving Emissions
• the method for analyzing at least one component of a motor vehicle includes correcting, for the current time interval, based on checking, the probability value for a current scenario of acceleration and/or the probability value for a current scenario of deceleration and/or the probability value for a current constant velocity state scenario and/or correcting the acceleration value for the current time interval based on checking on.
• This has the advantage that RDE guideline-compliant driving cycles can be generated.
• a further aspect of the invention relates to a computer program product which has instructions which, when executed by a computer, cause it to carry out the steps of a method according to one of the specified embodiments.
• a further aspect of the invention relates to a computer-readable medium on which a computer program product according to one of the specified embodiments is stored.
• a further aspect of the invention relates to a device for generating a driving cycle for a vehicle, which is suitable for simulating, in particular real, ferry operation, and means for determining a state vector of the driving cycle for a current time interval from a speed profile of the past, means for providing a acceleration prognosis model, means for determining an acceleration value taking into account probabilities resulting from the acceleration prognosis model and the state vector, means for integrating the selected acceleration value over the current time interval in order to obtain a predicted speed value for a next time interval in the future, and means for appending of the predicted speed value to the speed history of the past to generate the driving cycle.
• a means within the meaning of the invention can be configured as hardware and/or software and in particular a processing unit (CPU) and/or a or have several programs or program modules.
• the CPU can be designed to process commands that are implemented as a program stored in a memory system, to detect input signals from a data bus and/or to emit output signals to a data bus.
• a storage system can have one or more, in particular different, storage media, in particular optical, magnetic, solid-state and/or other non-volatile media.
• the program may be arranged to embody or be capable of performing the methods described herein such that the CPU performs the steps of such methods can and thus in particular can control and/or monitor a reciprocating piston engine.
• FIG. 1 shows a preferred exemplary embodiment of a computer-aided method according to the invention for generating a driving cycle for a vehicle, the method being suitable for simulating real ferry operation;
• FIG. 2 shows a preferred exemplary embodiment of a computer-assisted method according to the invention for generating an RDE-compliant driving cycle for a vehicle
• FIG 3 shows a preferred exemplary embodiment of a device for generating a driving cycle for a vehicle, which is suitable for simulating real ferry operation.
• FIG. 1 shows a preferred exemplary embodiment of a computer-aided method 100 according to the invention for generating a driving cycle for a vehicle, the method 100 being suitable for simulating real ferry operation.
• past data is provided.
• the data from the past represents speed data from the past or a speed profile from the past and consists of speed values which are respectively assigned to consecutive defined time intervals.
• the defined time intervals can be constant time intervals or can vary in length.
• the historical data may consist of a single velocity value associated with a single time interval. This single speed value can also be zero.
• the last speed value of the speed profile of the past is assigned to a current time interval.
• the state vector x t is determined for the current time interval from the speed data or the speed profile of the past.
• the state vector x t has as components the current velocity value v t of the current time interval t, the acceleration value at the time interval t-1 which is immediately before the current time interval t, a value s a , t which corresponds to the number of time intervals in which an acceleration maneuver took place immediately before the current time interval, a value s e , t corresponding to the number of time intervals in which a deceleration maneuver took place immediately before the current time interval, and a value Sk,t corresponding to the number of time intervals corresponds to where a constant velocity state was maintained immediately prior to the current time interval.
• the designation S j , t used in FIG. 1 designates the three values s a ,t, s e ,t and Sk,t, with the index j taking on either a for an acceleration maneuver, e for a deceleration maneuver or k for a state of constant speed can.
• the state vector may have other components or other components that correspond to velocity values, acceleration values, changes in acceleration values, or a number of time intervals.
• a probability value p(x t ) for a current acceleration scenario and a probability value q(x t ) for a current deceleration scenario are determined from the state vector using an acceleration prognosis model.
• the acceleration prognosis model is preferably based on a statistical evaluation of measured driving data of a real vehicle, the measured driving data consisting exclusively of a chronological sequence of speed values which are assigned to time intervals that follow one another in time.
• one of the three scenarios is then randomly selected in the sense of a statistical random draw, ie an acceleration scenario, a deceleration scenario or the scenario of the state of a constant Speed based on those determined in step 103
• a probability distribution of acceleration values is determined with the aid of the acceleration prognosis model as a function of the state vector for the randomly selected scenario; a continuous probability distribution is preferably modeled for this purpose. More preferably, a probability can be assigned to each possible acceleration value within the randomly selected scenario.
• a random selection takes place in the sense of a statistical random drawing of any acceleration value a t for the current time interval t from the ascertained probability distribution of the randomly selected scenario.
• a step 106 the randomly selected acceleration value a t is integrated over the current time interval t to obtain a next predicted velocity value v t+i for a next time interval t+1 in the future.
• a step 107 the new speed value v t+i is appended to the speed history of the past.
• the new speed value v t+i for the time interval t+1 is then treated as the current time interval in step 102 in a second iteration of the method.
• FIG. 2 shows a preferred exemplary embodiment of a computer-aided method 200 according to the invention for generating an RDE-compliant driving cycle for a vehicle.
• Step 201 of method 200 is identical to step 101 of method 100 described above.
• Past speed data is provided.
• Step 202 of the method 200 includes the steps 102 and 103 of the method 100 described above.
• the state vector x t is determined from the speed profile of the past.
• a probability value p(x t ) for a current acceleration scenario and a probability value q(x t ) for a current deceleration scenario are determined from the state vector using the acceleration prognosis model.
• step 203 of the method 200 periodically recurs after a defined number of iterations of the method 100 a check of the previously predicted, i.e. previously generated speed values Compliance with the criteria of the RDE guidelines. For example, this periodically recurring check can take place after a number of time intervals, which correspond to the expiry of a time span of five minutes of the driving cycle, but other time spans for the periodic check are also possible.
• step 202 After the probability values p(x t ) and q(x t ) for a current acceleration scenario and for a current deceleration scenario have been determined in step 202, if the check in step 203 has shown that the criteria of the RDE guidelines are exceeded by those already predicted , speed values appended to the speed profile of the past are not maintained, the probability values p(x t ) and q(x t ) determined are corrected accordingly in a step 204 .
• the current acceleration scenario or deceleration scenario thus receives corrected probability values p'( x 0 and q'(x t ). If, for example, the check in step 203 has shown that the duration of a motorway trip at increased speed according to the criteria of the RDE guidelines by the already predicted speed values and corresponding time intervals are not adhered to, in step 204 the probability of an acceleration scenario is increased by the correction and the probability of a deceleration scenario is correspondingly reduced.
• one of the three scenarios is then randomly selected, ie an acceleration scenario, a deceleration scenario or a scenario of the state of a constant speed based on the probability values p'(xt), q'(xt) and 1 - p′(xt) ⁇ q′(x t ) and a random selection of an arbitrary acceleration value a t from the probability distribution of the randomly selected scenario determined as described in the context of the method 100 .
• the randomly selected acceleration value a t can be corrected in step 206 in accordance with the check in step 203, as a result of which the corrected acceleration value a' t is generated.
• a step 207 the corrected acceleration value a' t is integrated over the current time interval t to obtain a next predicted velocity value v t+i for a next time interval t+1 in the future.
• a step 208 the new speed value v t+i is appended to the speed history of the past.
• the new speed value v t+i for the time interval t+1 is then treated in the next iteration of the method 200 in step 202 as the current time interval.
• FIG. 3 shows a preferred exemplary embodiment of a device 300 for generating a driving cycle for a vehicle, which is suitable for simulating real ferry operation.
• the device for generating the driving cycle for a vehicle has means 301 for determining a state vector of the driving cycle for a current time interval from a speed curve from the past.
• the device for generating the driving cycle has means 302 for providing an acceleration prognosis model, means 303 for determining an acceleration value taking into account probabilities which result from the acceleration prognosis model and the state vector, means 304 for integrating the determined acceleration value over the current time interval to obtain a predicted speed value for a next time interval in the future; and means 305 for appending the predicted speed value to the past speed history to generate the driving cycle.

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• 2020
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