ISO/DIS 11010-3

ISO/TC 22/SC 33

Secretariat: DIN

Date: 2025-12-17

Passenger cars — Simulation model classification —

Part 3:
Generation process of tyre model parameter sets

Voitures particulières — Classification des modèles de simulation —

Partie 3: Processus de génération des ensembles de paramètres du modèle de pneumatique

DIS stage

© ISO 2025

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Contents

Foreword

Introduction

Scope

Normative references

Terms and definitions

Tyre simulation

Tyre simulation model type

Simulation applications

Tyre model selection

Input Data Requirements

Suitability

Overview tyre model parameterisation process

General

Input data

Data evaluation and pre-processing

Parameter identification

Parameter set check

Data exchange and reporting

General

Result Delivery

(informative) Guidance on Input Data and Test Conditions for Parameterisation

(informative) Tyre model requirements

(informative) Data exchange and reporting

Bibliography

Foreword

ISO (the International Organization for Standardization) is a worldwide federation of national standards bodies (ISO member bodies). The work of preparing International Standards is normally carried out through ISO technical committees. Each member body interested in a subject for which a technical committee has been established has the right to be represented on that committee. International organizations, governmental and non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.

The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular the different approval criteria needed for the different types of ISO documents should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives).

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of any patent rights identified during the development of the document will be in the Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents).

Any trade name used in this document is information given for the convenience of users and does not constitute an endorsement.

For an explanation on the meaning of ISO specific terms and expressions related to conformity assessment, as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see the following URL: www.iso.org/iso/foreword.html.

This document was prepared by Technical Committee ISO/TC 22 , Road vehicles, Subcommittee SC 33, Vehicle dynamics, chassis components and driving automation systems testing.

This is the first edition.

A list of all parts in the ISO 11010 series can be found on the ISO website.

Any feedback or questions on this document should be directed to the user’s national standards body. A complete listing of these bodies can be found atwww.iso.org/members.html.

Introduction

Simulation is a common tool in the development process. The definition and selection of suitable tyre simulation models and their parameter sets is an important step to achieve reliable results.

Tyre models can be based on test data, simulation data or other input. Tyre properties depend amongst others on conditioning history and operation temperature, which can vary in vehicle manoeuvers, and when testing tyres on rigs in laboratories.

As there is no standardization, various data sources and characterization procedures including an unknown range of differences in tyre operation points can lead to significant differences in representing the tyre properties in tyre simulation models.

This project will increase the comparability of model parameter sets which are created for the same application.

In addition, it will raise awareness about relevant influencing effects in the generation process such as used data sources, selected operating conditions, applied post-processing steps in the data evaluation and utilized fitting methods and contents.

This information is collected in a summary report attached to the tyre model parameter set to allow an evaluation of the suitability for a certain application and the comparability to other similar parameter sets.

Passenger cars — Simulation model classification —

Part 3:
Generation process of tyre model parameter sets

This document specifies a framework for the generation process of tyre model parameter sets, including a classification of the data sources and methods used.

This document focuses on the process of generating the tyre model parameter sets covering the definition of required input data for certain standard applications.

This document enables a structured exchange between tyre model users and tyre model providers by defining a fitting report for the tyre model parameter set. The exchanged information shall ensure that the tyre model parameter sets are suitable for the intended range of applications, and enable the model providers to ensure that all relevant model features are parameterized for the intended operating conditions and based on suitable input data by increasing transparency and traceability.

The following documents are referred to in the text in such a way that some or all of their content constitutes requirements of this document. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies.

ISO 11010-1, Passenger cars — Simulation model classification — Part 1: Vehicle dynamics

For the purposes of this document, the following terms and definitions apply.

ISO and IEC maintain terminological databases for use in standardization at the following addresses:

• IEC Electropedia: available at http://www.electropedia.org/
• ISO Online browsing platform: available at http://www.iso.org/obp

tyre model classification

systematic arrangement of tyre models and their generation processes into groups or categories based on established criteria, such as data sources and validation levels

tyre model type

structure and the kind of modelling approach to define a simulation tyre model

tyre properties

measurable data that describes the physical characteristics of a tyre

tyre model parameter set

set of numerical values for all related tyre model parameters

tyre model user

person or company, who/which uses the tyre model for simulation or evaluating the characteristics defined in the tyre model parameter set

parameter set provider

person or company, who/which is in charge of identifying the model parameters of a specific model type for a specific tyre and who/which provides these parameter sets to the model user

application

specific use case employing a tyre simulation model in a virtual environment

model features

capability of the tyre simulation model to describe certain tyre properties

tyre simulation model developers

person or company, that creates and develops software code and mathematical formulae to enable the employment of a tyre simulation model in a vehicle or system simulation environment

fitting report

document providing information on the tyre model parameter set, its generation process and the model performance by comparing input and output data.

The following section serves as a guidance for tyre model users to select a suitable tyre model type for a certain application and to help the tyre model parameter set providers consider that the tyre model parameter set is containing all relevant information to support a proper usage of the model features.

In the context of vehicle simulations, various tyre model approaches have been developed. ISO 11010-1 states a definition of tyre model types which is supplemented by further details and a more commonly used classification of established tyre model types in the Annex A.

The applications of tyre models considered in this standard are related but not limited to simulations in the context of automotive system and/or component design and development.

NOTE The proposed methodology in this standard is generic and could also be applied to any other utilization of tyre simulation models (e.g. for motorcycles, special vehicles, aircrafts, etc.).

Typical automotive standard manoeuvres are listed in ISO 11010-1 . This list is not exhaustive and can be extended by the model user for example by not yet standardised manoeuvres, which may be based on local legal regulations, or proprietary events with undisclosed definitions.

Typically, the application manoeuvres are characterised mainly by the direction of excitation (e.g. longitudinal, lateral, vertical or a combination), the level of acceleration (e.g. low g or high g) and its severity, and the frequency range (e.g. steady-state or transient). Each application generates a list of requirements for the tyre model and the parameterised tyre model features which has to be considered during the parameterisation process.

While pure horizontal vehicle dynamics investigations can be simulated on an ideally flat and smooth 2D surface, vertical excitations are simulated on 3D road surfaces or 3D obstacles where the capability of a tyre model to scan the road and interpret the tyre-road- contact accurately is important. The more complex a desired simulation application is, the more complex are typically also the tyre models, and thus the requirements (see section 4.4) for a tyre model.

Some ADAS and vehicle dynamics attributes are not only evaluated in offline desktop simulation but also increasingly using driving simulators with a driver in the loop, which require real time capability. This is an essential detail when selecting a tyre simulation model and requesting a tyre simulation model parameter set.

The model user shall collect and share the information of the planned application with the model parameter set provider, in support of the selection of appropriate input data, and consultation regarding the suitability of a tyre model parameter set incl. its real-time capability, if required.

In the context of simulations, various tyre model approaches with different capabilities and features have been developed. The tyre model type definition stated in ISO 11010-1 is supplemented by further details and a more commonly used classification of established tyre model types in Annex A.

The selection of a suitable tyre model type is important to achieve reliable results. Several different appropriate solutions can exist but not every tyre model type is suitable to solve every application. The selection can depend on multiple reasons and can not generally be pre-defined. The suitability of a tyre model type can only be confirmed when the tyre model features are parameterised properly, based on adequate input data which fulfil the requirements of the tyre model.

Therefore, the model user shall provide information on the planned simulation applications as described in section 4.2 to the tyre model parameter set provider.

The parameter set provider shall have in-depth knowledge of the tyre model type and must appreciate the capabilities and prerequisites of the requested tyre model type. In cases of doubt, the model parameter set provider shall request support from the tyre model developers to clarify any open questions.

The parameter set provider shall support and consult the model user in selecting a suitable tyre model type, based on the requirements (4.4) and prerequisites, as well as available input data (5.2) for the parameterisation process.

The information exchanged shall be documented in the tyre model fitting report and contain information such as:

• tyre brand and product name;
• tyre dimension and specification;
• selected tyre model type and version;
• expected load range and nominal load;
• expected speed range and nominal speed;
• desired inflation pressure / inflation pressure range;
• desired rim dimension;
• list of desired manoeuvre types, if applicable (incl. information on the expected boundaries of severity, frequency range, required temperature dependency, required inflation pressure dependency, etc.)

NOTE The more details can be shared upfront, the better the tyre model parameter set providers can check and select appropriate input data. However, some details may be considered confidential and thus can not be shared.

Each tyre simulation model is based on mathematical formulae to describe certain tyre properties. These formulae contain parameters to adopt the tyre simulation model parameter set to the behaviour of a specific tyre type and operating condition. Only if the parameters are set to appropriate values, the tyre simulation model replicates the behaviour of a specific tyre type.

In addition, the simulation applications themselves generate certain requirements on the input data.

EXAMPLE 1 To simulate stopping distance typically requires information on the longitudinal tyre slip behaviour and surface friction properties. They can vary in highly transient conditions during an ABS intervention with dynamically changing slip, temperature and load conditions compared to steady state input data. Therefore, it can be beneficial to validate the parameter set with additional transient input data.

EXAMPLE 2 The event "braking in a pothole" requires a precise description of longitudinal slip behaviour and vertical tyre stiffness until rim contact. If the rim contact point is not properly depicted by the tyre model type or the tyre model parameter set, the results can deviate significantly.

Ideally, the input data allow an isolated identification of the model parameters related to certain features.

The requirements for the input data can vary depending on the tyre simulation model. Thus a list of recommended input data and related requirements for a specific simulation model type shall be requested from the tyre simulation model developers by validated tyre simulation model parameter set providers directly. The list shall be updated when new model features are introduced and thus regularly checked with issuing of new model release versions.

NOTE A global list of requirements may be subject to confidentiality and thus only be shared with validated tyre simulation model parameter set providers.

Some information may be considered as sensitive information by the model developers and therefore not be shared.

Tyre model input data are required to fulfil some implicit criteria to properly enable the determination of the related tyre model parameters. Not all expected prerequisites can always be guaranteed. Therefore, a knowledge of potential implications in case of eventual deviations from the requirements is important. Any such known deviation shall be noted in the fitting report by the editors of the report.

EXAMPLE 3 Assuming a certain tyre model type is capable to depict the influence of temperature on the tyre behaviour in general, the model user can only rely on this feature if the related model parameters are identified thoroughly on suitable input data that allow to extract the temperature behaviour. If the available input data do not fulfil the requirements, e.g. because the test procedure was not covering these aspects, or the tyre wear status changes significantly at the same time, the thermal model feature may not be properly parameterised, and thus the model will fail to depict the thermal behaviour accurately. Therefore, as a requirement, the input data shall contain information to describe the thermal behaviour in a way that does not affect the wear condition in this example.

Clause B.1 contains further information on commonly known general requirements to input data.

Upon request by the model user, the tyre model parameter set provider shall perform an assessment of the model type and parameter set regarding suitability for the required simulation applications, and feedback to the model user whether the selected (or available) tyre model type is suitable to simulate a certain manoeuvre, and which prerequisites that depends on.

The tyre model parameter set providers can declare the compliance of the input data with the requirement list per self-declaration. Any observed deviation from the model requirements as compiled by the model developers shall be mentioned in the fitting report.

The model parameterisation process is describing the whole chain from defining the input data basis, including scope of data, source of data and range of operating conditions through pre-processing treatment, to the parameter identification of the tyre model for the various properties, and eventually its validation.

The following sections shall mention important and relevant (but not exhaustive) influencing factors, that can affect the results of the parameterisation process. The documentation of this process in the Fitting report is specified in Clause 6.

This chapter provides a list with information on the input data basis for the tyre model parameterisation process.

Most of the tyre properties are dependent on the operating conditions, the way of excitation in their testing, as well as on the ambient conditions. Therefore, the model user shall share information on the intended use case of the tyre model parameter set with the parameter set providers, when requesting a specific tyre model parameter set. The model parameterisation providers can use this information to select suitable input data in combination with recommendations of the tyre model developers with regards to parameterisation of the necessary tyre model features.

To achieve the desired comparability of tyre model parameter sets, information on the considered boundary conditions are important to be appreciated by both parties. Hence, the operating conditions used as input data for the parameterisation (testing or virtual data), if not confidential, shall be stated and confirmed by the tyre model parameter set providers, via the tyre model fitting report.

NOTE Some procedures and testing conditions to create input information are based on methodologies that may be considered confidential, and thus can not be shared. Nevertheless, the following sections provide relevant information and mention the major relevant factors so that model parameter set providers and model users can negotiate which information may be exchanged, and eventually by mutual agreement, be covered by additional protective measures, such as non-disclosure agreements.

To trace differences and gain a reliable understanding of the model parameter set, the classification code of the utilised data source shall be stated in the fitting report for each tyre model parameter.

Depending on the source, some additional details on the particular operating conditions during the characterization process are relevant, to ensure that the model parameter set will cover the desired operating range during the intended simulation manoeuvres.

To indicate which parameters have been processed during the parameter identification process, a status code shall be stated for each parameter in the parameter set according to Table 1:

Table 1 — Parameter processing status

The required data to base the related tyre model parameter identification on, can be gained from different sources, such as listed in table 2:

Table 2 — Input sources

The data source used, and the parameter status, shall be stated for each tyre simulation model parameter in the fitting report. Different sources can be used for different parameters. In case of redundancy, the most appropriate source shall be stated.

Further information on the data source types can be found in Annex Clause B.2

As mentioned, the tyre properties depend on the operation conditions and the tyre state.

To achieve comparability of different parameter sets and desired traceability, the fitting report shall ideally disclose the relevant details of the input data used to generate it. Where this is not possible because of confidentiality, the report shall contain a unique procedure ID code to enable traceability. In addition to the procedure ID code, technical details may be shared as an option, if agreed between model parameter set provider and the model user. Further information is stated in Clause 6

NOTE Some procedure related information may be considered confidential and may thus not be shared by any tyre model parameter set provider.

The unique ID code shall allow tracking and identifying the applied conditions by the parameter set providers such as

• tyre handling and conditioning procedure (break-in / warm-up);
• used friction partner / surface;
• thermal conditioning procedure;
• inflation pressure control procedure;
• operational control procedure (wheel load, slip ratio, slip angle, camber angle, displacements, change rates / dynamics);
• number, order and transition between tests on each tyre sample;
• time history data of each tyre sample.

Any changes applied to the items above will result in a different unique ID code for that particular combination (versioning).

Further information on the different influencing factors can be found in Clause B.3

The input data usually requires an evaluation to ensure that e.g. all coordinate systems are aligned, all expected channels are included, only the relevant manoeuvre sections are considered, etc. This is usually done in a data evaluation routine.

Depending on the data type, additional cleaning operations such as cropping, offset-removal, symmetrisation or filtering are usually applied. These steps are not standardised and typically not documented in the result files. 

NOTE Typically all measurement data published in fitting reports or literature is filtered and smoothened to gain a better fitting result. Raw data can contain physically plausible and realistic noise in the signals which are outliers and which may make the fitting process more difficult if used 'raw'.

The table below shall provide an exemplary list of the most commonly used post-processing operations and define a code to feature a traceable and comparable documentation of such treatment in the fitting report.

Table 3 — Common Post Processing Steps

The most common tyre models are designed to import data in the TYDEX (Tyre Data Exchange) format.

The applied evaluation operations shall be stated in the fitting report for transparency and traceability.

In case the applied post-processing operations are not disclosed in detail in the fitting report, at least a unique ID code of the post-processing procedure shall be stated to enable traceability.

Each tyre model parameter is linked to a related tyre characteristic. These characteristics can be described by single parameters (e.g. tyre mass) or a group of parameters (e.g. lateral force curve).

During the parameter identification process, the particular tyre model parameters are varied until the simulated tyre behaviour matches the physical test measurements under equivalent conditions. This can be done in various ways (e.g. manually, semi-automated, or fully automated) and each tyre model type utilises different approaches or software tools. In addition, the set preferences on the achievable residual error impact the fitting results (the point at which the parameter identification process results in an acceptable error, is subject to engineering judgement).

In the course of parameterisation, the complex and interlinked tyre behaviour is attempted to be broken down into isolated changes in the operating conditions (e.g. applying a pure vertical load to the standing tyre to focus on just some of the stiffness parameters). The parameterisation process starts from rather simple parameters and, as soon as they are identified and fixed, proceeds to more complex conditions, and next parameter groups. A structured approach is important to avoid infinite adjustment loops. The approach used, the selected starting parameters and boundary conditions will affect the final result. A structured sequence is a critical component of the parameter identification method. The method used shall be documented or covered by the unique ID code of the fitting procedure.

The following table provides a code to obtain information on the used parameter identification tool and method for each parameter set.

Table 4 — Parameter identification procedures

The applied fitting method and used tool version shall be indicated in the fitting report.

In case the applied parameter identification procedures and fitting tool versions are not disclosed in detail, at least a unique ID code of the fitting procedure shall be stated, to enable traceability.

During the parameter identification, the model fidelity is checked and optimised in the range of the input data, which usually aims to vary only a limited number of boundary conditions (e.g. slip angle sweep measurements run with constant wheel load and constant velocity). During a full vehicle manoeuvre though, many operating parameters vary at the same time and transiently (e.g. wheel load, camber angle, slip angle, speed, etc.). 

An optional check can provide a final check to determine if the tyre model parameter set is fulfilling the requirements as defined in clause4.4. The fitting report shall state if any kind of tyre model parameter set check has been applied to ensure the model works well within, and/or beyond, the covered range of operating conditions, or if there is a “quality check” applied. This can be done, for example, by comparing the tyre model parameter set performance against some additional data that has not been used in the parameterisation process. A high check level can help to increase the confidence in the parameter set.

The following table provides a code which allows to obtain information on the degree of validation.

Table 5 — Check Status and Level

The applied validation method, and tool version used, shall be indicated in the fitting report.

In case the applied validation procedures are not disclosed in the fitting report, due to confidentiality conflicts, a unique ID code of the validation procedure shall be stated to enable traceability.

This standard just defines the contents and the structure of a tyre model fitting report. It does not cover the layout or format. Each parameter set provider can create a suitable layout or format according to their needs and options. The intended structured information exchange between tyre model user and tyre model parameter set provider shall be bi-directional.

The tyre model parameter set provider shall collect relevant information from the model user about his desired application. All relevant information about the tyre model generation process and selected input data shall be documented in the fitting report to support desired traceability.

Both the model user and the provider of the model parameter set shall reach an agreement on the specific contents of the model parameter set (e.g. which features shall be parameterised), to ensure its suitability for certain applications. This agreement is based on model requirements, availability, and specific operating conditions tailored to particular simulation applications. In case certain required model input data is not available (e.g. as recommended by the model developers), the fitting report shall state the basis for the decision to use alternative input data instead.

The tyre model parameter set shall be delivered together with a fitting report as described in the following.

The fitting report shall be modular and consist of different chapters and sections which cluster relevant information for certain tyre properties or certain input data, to make it easier to find and compare similar information in different reports, see Figure 1 .

The fitting report layout can be adjusted according to the specific tyre model type and the input data used. Only relevant chapters covering processed input information used shall be compiled in the fitting report. If certain input information has not been processed during the parameter identification, it does not need to be included in the fitting report.

If relevant input information is required to parameterise a model feature, but it was not available for the parameter identification process, there shall be a note in the report, and an indication of the implication of this. The same applies for known deviations from model requirements as stated in clause 4.4 .

Figure 1 — Structure of the modular fitting report

The first chapter is always required and shall be included in all fitting reports. It shall contain sections with details about

• Information exchanged on the intended use case, and expected operation conditions of the tyre model incl. relevant information on the decision of the selected tyre model type and input data (as long as this does not conflict with any confidentiality agreements)
• Basic information on the specific tyre type to be represented by the tyre model parameter set
• The identified tyre model parameters (as long as they are accessible and editible to the tyre model user and not considered as confidential) to have a back-up of the delivered parameters incl. the status and data source classification codes for each processed parameter

As stated above, only the information which has been used to create the tyre model parameter set is required to be documented. Of course, any further helpful information can be included as an additional option, to support the model user, or to increase traceability and comparability, but without any obligation. See also Annex C

Table 6 — Examplary contents of chapter one sections (this list is not exhaustive and can be customised)

In the following modular chapters and sections, information on the processed input data shall be documented. In addition, the model output shall be compared with the processed input data in adequate plots or diagrams.

Figure 2 defines the structure of the modular chapters and the sections. Each chapter describing the various used input data shall consist of a header section with general information, followed by a section containing information relevant to the procedure, and finally a section providing depictions of the processed data used in the fitting process.

Figure 2 — Sections of each chapter

The section contents shall contain information, which is needed to trace the executed process steps. The tyre model parameter set provider can decide to disclose details or provide unique procedure ID codes to avoid obligation to disclose confidential data, but allow traceability. Traceability shall cover (but not limited to):

• General Information:

Information about the input data source used, and the procedures applied, via ID codes, to allow traceability of the input data source used (e.g. test rig type), rim information, considered road surface, data acquisition settings, coordinate system definitions, sensor positions, and other relevant information, even if they are not disclosed directly (this list is not exhaustive).

• Operating Conditions:

Procedure ID codes and evtl. details regarding tyre conditioning, velocities, inflation pressures, loads, thermal conditionings, etc., to allow traceability and comparison even if they are not disclosed directly (this list is not exhaustive).

• Postprocessing:

Post-processing procedure ID codes and evtl. details of steps like coordinate system transformation, filtering, smoothing, offset correction, symmetrisation, and hysteresis handling, etc., to allow traceability and comparison, even if they are not disclosed directly (this list is not exhaustive).

• Model performance vs. input data:

The fourth section contains plots of the model performance vs. the input data used. The required information from Table 7 shall be contained in the fitting report. Further additional information can be documented as an option, or can be customised specificly to support the model user.

Based on input data required / used to identify the parameters of a certain tyre model type, the reporting structure shall be assembled by combining different chapters to show the relevant information of the processed input data and the model performance for each model feature or tyre property and for each operating condition used (e.g. different loads, different speeds, different inflation pressures, different camber angles, etc.). Not all plot examples listed in Table 7 are applicable to all tyre model types or relevant for certain model features.

Table 7 — Modular report sections for tyre model parameter set validation

In case the tyre model parameter set is not created by identification from input data but rather by means of subjective evaluation in a driving simulator, offline simulations or other tools (e.g. modification or scaling of existing parameter sets), the model performance cannot be compared against standardised input data. In such case, either a generic set of load conditions shall be simulated (similar to the classical test procedures based on the model developer recommendations) and plotted without further reference data. Alternatively, the performance of a selected reference tyre model parameter set shall be plotted against the simulated results as an additional comparison reference in the relevant diagrams.

If a model check has been executed as described in clause 5.5, the results shall be briefly documented in a final chapter.

Proprietary templates for documentation and reporting shall be developed and can be expanded by further information as listed in Annex A.

1. 
   (informative)
   
   Guidance on Input Data and Test Conditions for Parameterisation

In the context of simulations, various tyre model approaches have been developed. The tyre model type definition stated in ISO 11010-1will be supplemented by further details and a more commonly used classification of established tyre model types in the following section.

• 1. Empirical Models
     1. Look-up table

A Look-up Table (LUT) tyre model is a type of empirical model that uses pre-calculated data stored in tables to predict tyre behaviour under various conditions. Instead of relying on real-time calculations, the model references these tables to quickly retrieve the necessary information. This approach is particularly useful for real-time applications, such as vehicle simulations, where computational speed is crucial.Look-up Tables are created by data collection; extensive testing is conducted on tyres under various conditions such as different loads, slip angles, camber angles, and road surfaces. The data gathered includes measurements of forces and moments acting on the tyre. The collected data are processed and organised into tables. These tables typically include:Longitudinal force vs slip ratioLateral force vs slip angleAligning torque vs slip angleCombined forces under different operating conditionsThe tables contain discrete data points. Interpolation techniques (linear or spline interpolation) are used to estimate tyre behaviour at points that fall between the measured data points.Because the look-up tables store pre-calculated data, the tyre model can provide rapid responses, making it suitable for real-time applications such as driving simulators, vehicle dynamics simulation, and control system testing.Advantages of Look-up Table ModelsSpeed: LUT models are computationally efficient, enabling real-time simulations.Accuracy: They can be highly accurate if the underlying data are comprehensive, well-organised, and the data required falls within data points in the tables.Simplicity: These models are relatively easy to implement and use, as they rely on direct data retrieval.Limitations of Look-up Table ModelsData dependency: The accuracy is highly dependent on the quality and range of the underlying data, with often extensive data being required.Lack of flexibility: LUT models can fail to accurately predict tyre behaviour outside the tested conditions.Storage: They can require significant storage space for extensive data sets in large tables.This approach ensures a balance between computational efficiency and accuracy, making it a popular choice in various simulation applications.

• • 1. Formula-based Models

Formula-based empirical tyre models are derived from experimental data and observations rather than theoretical foundations. They are built by conducting extensive physical tests on tyres under various conditions and using the gathered data to create mathematical relationships that show a similar curve shape as observed from the tyre behaviour in the test data.

These models rely heavily on real-world data collected from tyre tests. The data includes measurements of forces, moments, and other performance metrics under different conditions like load, slip angle, and road surface.

Empirical models use mathematical formulae to represent complex tyre behaviours. The focus is on accurately capturing the observed phenomena rather than understanding the underlying physics.

The models are relatively straightforward to implement and use because they involve fitting curves or surfaces to the measured data. These models are often tailored to specific applications or conditions, making them highly effective within those contexts but less versatile outside them.

Advantages of empirical tyre models:

• accuracy: When derived from high-quality data, these models can accurately predict tyre behaviour under the tested conditions.
• speed: They are computationally efficient, making them suitable for real-time applications.
• simplicity: Easy implement, relatively simply based on mathematical formulae.

Limitations of empirical tyre models:

• data dependency: Their accuracy and reliability depend on the quality and comprehensiveness of the underlying data.
• limited extrapolation: They can fail to perform well under conditions not covered by the input data set and have usually a limited validation range.
• lack of physical insight: These models do not provide insights into the underlying physical mechanisms of tyre behaviour.

Empirical tyre models are useful in applications where quick and accurate predictions are needed, especially when the focus is on practical results rather than theoretical understanding.

• 1. Physical Models
     1. Semi-physical Models

Semi-physical tyre models combine elements of both empirical and physically based models to balance accuracy, computational efficiency, and physical insight. These models use empirical data to calibrate and validate the parameters of a physical model, which ensures that the model captures the essential physical phenomena while being informed by real-world test data.

While these type of tyre models incorporate physical principles, they rely on experimental data for calibration. This helps improve the model's accuracy and relevance to practical applications. The models simplify complex physical processes into manageable mathematical forms, making them more computationally efficient than fully physically based models. They blend empirical approaches with physical modelling, capturing the benefits of both methods. This includes the flexibility and efficiency of empirical models and the explanatory power of physical models.

Advantages of semi-physical tyre models:

• balance of accuracy and efficiency: They offer a good compromise between computational efficiency and accuracy, making them potentially suitable for real-time applications and detailed analyses or designs of experiment.
• insightful and practical: These models provide insights into the physical behaviour of tyres while being practically useful due to their reliance on real-world data.
• flexibility: Semi-physical models can be adapted for various applications and conditions by adjusting the empirical data used for calibration.

Limitations of semi-physical tyre models:

• dependence on data quality: Their accuracy is dependent on the quality and range of the experimental data used for calibration.
• simplifications: While they simplify physical phenomena, these simplifications can sometimes overlook complex interactions that occur in real tyre behaviour.

Overall, semi-physical tyre models are a robust option for applications requiring a balance between detailed physical representation and practical computational requirements. They are widely used in vehicle dynamics simulations, control system development, and other automotive engineering applications.

• • 1. Physical Models

Physical tyre models are based on the fundamental physical principles governing tyre behaviour. These models aim to simulate the actual physical processes that occur when a tyre interacts with the road surface, providing a more detailed and realistic representation of tyre dynamics.

The models rely on the laws of physics to describe the forces and moments generated by the tyre. They consider factors such as material properties, tyre geometry, and contact mechanics.

Physical models aim to capture the detailed interactions within the tyre, such as deformation, stress distribution, and thermal effects, providing a comprehensive understanding of tyre behaviour. With a detailed 3D contact patch description, they can interact with uneven and textured road surfaces.

Due to their detailed nature, physical models are often more complex and computationally intensive compared to empirical or semi-physical models, however, there are real-time capable versions available.

Advantages of physical tyre models:

• extrapolability: Even under conditions not covered by the input data set, predicted tyre properties would be realistic based on the physics.
• realism: They provide a highly realistic and detailed simulation of tyre behaviour, capturing complex interactions that empirical models might miss.
• Insight: By modelling the physical processes, these models offer insights into the underlying mechanisms of tyre performance, which can be valuable for design and development.
• versatility: Physical models can be adapted to different tyre designs and conditions, providing a broad range of applications.

Limitations of physical tyre models:

• computational demand: Due to their complexity, these models require significant computational resources, making them less suitable for real-time applications.
• data requirements: They often require considerable input data and design details, which can be challenging to obtain.
• complexity in implementation: Developing and calibrating physical models can be time-consuming and require specialised knowledge.

Physical tyre models are essential for detailed analysis and optimisation in tyre design and vehicle dynamics, especially when high accuracy and detailed insights are required.

• • 1. Finite Element Models

Finite Element (FE) type tyre models are advanced simulation tools used to analyse the detailed behaviour of tyres under various conditions. These models rely on finite element analysis (FEA) to break down the tyre into smaller, manageable elements, each governed by physical laws.

FE models simulate the tyre’s structural behaviour by dividing it into a mesh of finite elements. Each element represents a small part of the tyre, allowing for detailed analysis of stress, strain, and deformation. The models incorporate the tyre's material properties, including elasticity, plasticity, and visco-elasticity, to accurately simulate its physical behaviour.

FE models can capture complex interactions such as contact with the road surface, temperature effects, and wear, providing a comprehensive understanding of tyre performance.

FE models are used extensively in the design and development of new tyres, allowing for virtual testing and optimization before physical prototypes are made. They can help in understanding tyre performance under different operating conditions, including cornering, braking, and acceleration, up to and including crash simulations.

Advantages of FE tyre models:

• extrapolability: Even under conditions not covered by the input data set, predicted tyre properties would be realistic based on the physics
• high accuracy: These models provide highly accurate simulations by capturing detailed physical interactions and material behaviours.
• comprehensive analysis: They allow for a thorough examination of various factors affecting tyre performance, such as load distribution, stress concentrations, and thermal effects.
• design optimisation: FE models are invaluable for optimising tyre designs, enabling engineers to test different materials, structures, and tread patterns virtually.

Limitations of FE tyre models:

• computational intensity: These models require significant computational resources and time due to their detailed nature.
• complexity in setup: Developing and calibrating FE models can be complex and time-consuming, often requiring specialised knowledge and expertise.
• data requirements: Accurate simulations depend on detailed material properties and geometric data, which can be challenging to obtain.

Finite element tyre models are powerful tools in the automotive industry, providing detailed insights and enabling the optimisation of tyre designs for enhanced performance and safety. FE tyre models are predominantly used by tyre manufacturers in their design processes, or for special applications (e.g. crash simulation).

• 1. Physical models

1. 
   (informative)
   
   Tyre model requirements
   1. Tyre model input data

This chapter has an informative character, and provides information regarding common prerequisites, to ensure that the available model features can be parameterised based on suitable input data.

Tyre model input data is required to fulfil some implicit criteria, to fully enable the determination of the related tyre model parameters. Not all expected prerequisites can always be guaranteed. Therefore, a knowledge of any potential shortfall, and implications thereof, is important.

EXAMPLE An empirical tyre model typically requires input data based on flat surface tyre contact. If there is no such input data available (e.g. because there is only measurement data from a drum test bench available), the model requirement is not fulfilled. Knowing this allows a judgement of the potential effects, and to discuss potential solutions with the model parameter set provider.

NOTE 1 Model type specific requirements can be requested from the model developers directly. Some information may be considered as sensitive information by the model developers and therefore not to be shared.

In general the commonly known expectations are:

• same boundary conditions apply to all input data (especially when using different test tyre samples or input sources):
• same tyre specs (when multiple tyres samples are used for characterisation tests);
• same and stable tyre properties (break-in conditioning applied, no aging effects);
• same rim size and type;
• same mounting orientation (especially for directional tyre tread patterns);
• same road surface conditions (especially when using multiple input sources);
• same tread depth - no change during the testing (this cannot be fully avoided though, since ANY use of a tyre to generate forces and moments generates some wear);
• same thermal state (requires warm-up and thermal conditioning of the test tyre);
• constant ambient conditions (requires test cell conditioning);
• "clean" data:
• no effects from test rig design (e.g. camber axis aligns with contact patch plane);
• no effects from test rig compliance (to avoid side spring rates and vibrations);
• no effects from test rig controls (to gain stable data with repeatable excitation);
• no effects from rim compliances (to avoid side spring rates and vibrations);
• no effects from data acquisition system (time synchronous recording of channels, filtering, etc.);

NOTE 2 Not all test rigs are capable or suitable to execute all different kinds of required tests.

For example, there are conflicting targets to measure force and moment characteristics with a highly dynamic and versatile test bench that allows variations of many operating conditions (e.g. slip angle, camber angle, load, etc.), with constructive joints and related degrees of freedom on the one side, and cleat tests that require a completely rigid rig structure without any compliance. It is possible to use a force and moment test bench for cleat testing, but the data quality is unlikely to match the results of a dedicated cleat test rig.

Other examples exist.

NOTE 3 The test procedure applied has an impact on the observed tyre properties.

Exciting the tyre in a different way, order, intensity, frequency or duration, etc. will affect the operating condition the tyre is subject to, and therefore also the observed tyre reaction, and the forces and moments measured. Since the tyre model parameterisation process strives to match recorded test data as input with model output values, a difference in the input data will automatically lead to a different parameter set unless it is possible to link all the differences to related model features (which is typically unlikely). Thus a difference in the input data due to an incorrect operating condition will lead to a difference in the simulation results, which is not related to the tyre, but rather due to a disconnect in the tyre simulation model parameterisation process chain, and failure to address all relevant influencing factors accurately.

The target of a good test procedure is therefore to achieve a high level of robustness and to characterise the tyre properties ideally in all consistent operation condition (same thermal state, same inflation pressure, same wear state, same age, etc.). In practice there is a trade-off to save time and costs by running a batch of several tests, with a single test tyre sample.

NOTE 4 Most tyre models are still "tyre models" and not "wheel models". In the simulation the rim is usually considered to be rigid and the rim properties are not described in detail, unless there are specific rim stiffness parameters to address the rim compliance and wheel design.

Nevertheless, details of the exact rim used for testing shall be recorded and be traceable to each data set.

• clear definition of data content:
• statement of used data source;
• statement of used test procedure;
• statement of date and place;
• statement of used reference coordinate system;
• statement of used units;
• statement of sampling rate;
• statement of time channel (to take manoeuvre dynamics into account);
• statement of additional measurement data context (e.g. position of a temperature sensor and covered measurement area);
• statement of applied post-processing procedure and measures.

NOTE 5 In the context of this standard, any information that helps to increase transparency and traceability of the contents of the input data used in model is welcome and can be added to the fitting report. However, some confidentiality restrictions can limit the disclosure of details.

• 1. Data sources
     1. Template / Default values (TMP)

Some tyre model values may be left untouched or filled with common default values during the parameterisation process in case there is no better data source available.

• • 1. Generic values (GEV)

Generic values can be obtained from database, literature, parameter sets of similar tyres, empirical data, estimations, or other sources.

Tyre model parameters can also be identified based on subjective feedback of a driver-in-the-loop approach with real-time simulations in a driving simulator. In such case, the subjective evaluation does not provide an input file but rather a trend to be addressed.

Since generic values may not relate to a single specific tyre type or tyre sample, they may rather provide representative characteristics.

• • 1. Virtual simulation results (VSR)

Values can be gained from other offline simulations, such as FE simulation or simulations with other available tyre models and related parameter sets. Information may be reduced in fidelity by this "model from a model" approach, in particular if the source model does not feature all relevant tyre model requirements of the target model. Ideally, this approach is only applied in the direction from complex tyre models to simpler approaches.

NOTE The lack of coupling in some empirical models between longitudinal and lateral slip conditions, can lead to inconsistencies, when using empirical models to parameterise physical models with just single friction maps for different slip conditions.

Therefore, the approach to parameterise physical models and derive synthetic test data through off-line simulation for empirical models is one of the valid options.

• • 1. Physically measured value (PMV)

Direct use of a physically measured characteristic value as a single figure (e.g. mass or diameter).

• • 1. Physically measured data (PMD)

Using physical measurement data of real tyre samples to characterise tyre properties is still the most common approach. This type of source describes look-up tables or curves of tyre characteristics (e.g. stiffness properties, handling characteristics) and/or characteristics that are typically not measured directly but evaluated from test procedures (e.g. relaxation length or cornering stiffness).

The measurements can be performed either indoors (laboratory conditions) or outdoors (real world environment) with different setups.

Table B.1 — Measurement data sources

• • • 1. Outdoor measurements

Measuring tyre properties on real road surfaces is in principle closest to the native field of tyre operations.

These kind of measurements are typically done with instrumented test vehicles or special test trailers. While a test vehicle automatically operates the tyres in at least its relevant operating conditions, the applicable load ranges and the controllability of the operating conditions are limited to the vehicle and its motion. To optimise these, a test vehicle would have to be modified away from standard production, or developed specifically for the purpose of tyre testing.

EXAMPLE If the test vehicle performs a brake manoeuvre, the wheel load and the vehicle speed changes. In addition, the test tyre may face additional lateral forces due to suspension kinematics and compliances. These changes in conditions need to be measured and to be taken into account during the parameter identification.

A test trailer is usually operated in straight line motion, while the test tyre can be loaded and positioned independently. This allows a more isolated investigation of the tyre - ideally without influences from the carrying trailer rig motion (which cannot be fully excluded though).

To record the tyre properties and related operating conditions, a large amount of sensors is needed as the rig is moving, and there is therefore no fixed reference. The motion of the outdoor measurement device and potential road unevenness challenges a precise load and position control, and demands high efforts in the data evaluation, to determine the tyre operating conditions.

Due to varying ambient conditions, results obtained from real road measurements can vary considerably. While this represents the real actual physical behaviour, a repeatable result is preferred with regards to tyre modelling.

• • • 1. Indoor laboratory measurements

Laboratory measurements are considered advantageous due to their high level of repeatability, taking place in controlled environments, and having the option to independently vary only limited test parameters. Various laboratory test rig concepts exist, used to determine tyre properties. Typical concepts are:

• static stiffness test rig (typically used for structural stiffness and footprint tests);
• flat-belt test rig with flat contact patch (typically used for force and moment tests, dynamic stiffness tests, loaded radius test);
• drum test rig (can be 'inner' drum, where the tyre is tested running on the inside surface of a round drum, or 'outer' drum, where the tyre is tested running on the outside surface of a round drum) with a curved contact patch (typically used for cleat tests, high speed uniformity tests, rolling resistance tests - but also for force and moments for large tyres that cannot be tested on flat-belt machines);
• moving road/flat plank or guided wheel carrier on flat real surfaces;
• other.

Not every test bench is suitable for all types of measurements. Therefore, an indication of the test rig concept used is important, when comparing tyre model parameter sets.

Futher laboratory tests can be related to material analysis (e.g. dynamic mechanical analysis, DMA). This kind of testing is not routine, and only required by some types of tyre models.

• • • 1. Other

Please specify the data source.

• 1. Observation conditions

The manner in which tyre properties are examined, influences the outcome.

• • 1. Tyre conditioning

New tyres, which are loaded into a test rig for first time after being manufactured, will be subject to a change of their properties, until the so-called 'curing process' has finished, and stress has relaxed (Payne and Mullins effects). To achieve a stable tyre behaviour, and avoid drift in the test results, a tyre needs to be mechanically and thermally pre-conditioned, using so-called break-in and warm-up phase. A thorough thermal warm-up also ensures a stabilised temperature and inflation pressure.

The severity of the break-in has an impact on the tyre behaviour that is subsequently measured, i.e. a tyre with less severe break in than an identical tyre with more severe break in applied, will perform differently. Ideally, during break-in, the tyre is facing all input conditions that are expected to be achieved during the measurements, but with as little wear as possible.

• • 1. Friction partner / surface

The test surface used, i.e. the 'friction partner', affects the force transmission. It can be characterised by

• surface condition:
• dry (typically "high" friction);
• wet (typically "medium" friction);
• icy (typically "low" friction);
• deformable (snow, soft soil);
• other;
• surface texture, see Table B.2;

Table B.2 — Texturised surface types

• other.

Most of the laboratory force and moment characteristic measurements are performed on an industry standard corundum sandpaper which provides a stable and reproducible friction condition, for some considerable time, and until worn out.

• • 1. Thermal conditions

The thermal conditions have a major impact on the tyre properties. It is important that the drift in temperature during a manoeuvre is either as small as possible, and if possible controlled (by modifying the test parameters) to keep it constant. Ideally, temperature is monitored in all cases, but in particular if temperature is allowed to vary more widely during testing. Any thermal effects can then be neglected, or considered during the parameterisation process. The following needs to be considered:

• location of reference temperature sensors, incl. sensor type (e.g spot or average of a larger tread area, inner liner, sidewall, tread groove, etc.);
• 'thermal logic', to trigger all test condition input sweeps at the same temperature (and applying warm-up and cool-down phases intentionally).
  • 1. Inflation pressure

The inflation pressure of the tyre has a major impact on the tyre properties.

Inflation pressure can be set in various different ways:

• set cold (i.e. rig ambient temperature), kept capped ("free pressure evolution" - similar to what happens on an actual vehicle);
• set cold, warm-up (to the planned test temperature), re-adjusted, kept capped ("stable pressure", and thus with no pressure control, which will result in some increase in pressure with any increase in temperature, which is virtually unavoidable);
• set cold, warm-up, controlled ("controlled" pressure, i.e. pressure is regulated by actively removing or adding air into the tyre, whilst it is being tested).

In the terminology of tyre test rigs the following definitions are common:

• "capped" (pressure set by system once, then valve closed - free pressure evolution, pressure is measured by a sensor);
• "disconnected" (no pressure control device installed - free pressure evolution, no measurement);
• "controlled" (pressure is continuously measured, and actively adjusted to the target pressure by the system - this can lead to gas volume or temperature changes).

Target: The providers of tyre model parameter sets should advice the model user, which inflation pressure value to state when requesting a tyre model parameter set.

• • 1. Load conditions
       1. Wheel load

In general, vertical or horizontal forces acting on the tyre can be applied using either a force or a displacement control with a certain change rate. The higher the change rates, the higher the effect of undesired mass and inertia forces, which need to be compensated.

The same control type should be used during simulations to compare the same load condition.

The parameterisation input data shall ideally cover the load ranges that are expected to be achieved in the simulation manoeuvres.

• • • 1. Lateral forces and moments

Lateral forces are typically measured by deflecting the tyre laterally.

Deflections such as steering (non-rolling tyre) or side slip (rolling tyre) angles can be applied by either steering the tyre or the test surface with a certain change rate. The higher the change rates, the higher the effect of undesired mass and inertia forces, which need to be compensated for.

The coordinate system used has to be stated.

The results shall be evaluated in such a way that the effective side slip angle can be reproduced in the simulation. This is typically achieved by aligning the steering axis with the centre of the tyre contact patch.

Camber angles can be applied by tilting the tyre or the test surface with a certain change rate. The higher the change rates, the higher the effect of undesired mass and inertia forces, which need to be compensated.

The results shall be evaluated in such a way that the effective camber angle can be reproduced in the simulation. This is typically achieved by aligning the camber axis with the centre of the tyre contact patch.

• • • 1. Longitudinal forces

Longitudinal forces are usually applied by creating a tangential deflection or longitudinal slip. The change rate applied has an effect on the achievable peak force, and a drop in friction is typically seen once a certain deflection is reached.

The longitudinal slip the tyre experiences cannot be measured directly, just calculated from wheel rotational speed, trajectory velocity and effective rolling radius (which can vary). Hence, controlling slip accurately is challenging for most test rigs.

Alternatively, a brake or drive torque can be applied similarly to real vehicle brake systems. Controlling the torque can rely on direct measurements, and is usually more easily achievable on most test rigs.

Alternatively, the relative speed between tyre and test surface can be controlled, by varying the rotational speed of the tyre, and/or the linear speed of the test surface. For all types of control, the fact that the tyre behaviour is highly non-linear, and time dependent, makes longitudinal characteristic measurements challenging.

• • • 1. Transitions between the load conditions

The order of the events in a test procedure, and the phases between the events (transitions), will affect the tyre operating conditions, and thus the results.

Some changes in the tyre operating conditions require a certain period to stabilise (e.g. inflation pressure change), and sufficient time shall be allowed during transition, to avoid undesired drift (i.e. change in conditions that are assumed to be constant), during an event.

1. 
   (informative)
   
   Data exchange and reporting

To make sense of any data shared, in essence, details need to list all the information that would be required to exactly replicate a particular test on a particular tyre, should this ever be needed.

Taking into account the literally infinite levels of details, and the related complexity, at least a unique ID code for the data sets shall be defined in such a way that the tyre model parameter set providers can relate their output to what exactly has been used as data source, and can replicate this procedure at any time in future, if needed. The ID code shall facilitate versioning and thus indicate a model user if the same, or a different, procedure has been applied.

The following Table C.1 shall provide a guidance to collect and compile relevant information in the report:

Table C.1 — Potential Report Contents

Target: It needs to be possible, to trace back from the fitting report and relevant meta data what exact tyre specification was characterised, and exactly how the test and model input data was treated.

Bibliography

[1] ISO 13473-1, Characterization of pavement texture by use of surface profiles — Part 1: Determination of mean profile depth