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CourseAutomotive & Motorsport Applications open

Digital twin course in motorsport

Online course of 300 hours in digital twins in motorsport. 6 modules covering the digital twin concept, data acquisition and management, physical vehicle modelling, data-driven models and operational applications, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Define the architecture of a digital twin applied to racing.

02

Organise the acquisition and management of telemetry data.

03

Build physical vehicle models and calibrate them with real data.

04

Apply machine learning to performance prediction.

05

Use the twin for strategy and predictive maintenance.

06

Assess the uncertainty and value delivered by the twin.

07

Develop a digital twin prototype for a specific case.

Profile

Who it's for

  • Digital twin engineers.
  • Racing data engineers.
  • Strategy engineers.
  • Simulation engineers.
  • Motorsport data scientists.
  • Performance engineers.
  • Engineering or science graduates looking to join racing teams.
Standards & tools

What you'll work with

  • ISO 23247 (digital twin framework for manufacturing)
  • FMI 3.0 (Functional Mock-up Interface, Modelica Association)
  • ISO 11898 (CAN bus)
  • ISO/IEC 27001:2022 (information security)
  • ASME V&V 10 (verification and validation in computational mechanics)
  • ISO 10303 (STEP)
  • Regulation (EU) 2016/679 (GDPR applied to driver data)
  • Dymola
  • MATLAB/Simulink
  • Python (pandas, scikit-learn)
  • InfluxDB
  • Grafana
Syllabus

6 modules

  1. 1.1Definitions: model, digital shadow and digital twin
  2. 1.2Twin maturity levels
  3. 1.3Twins of the car, the driver and the circuit
  4. 1.4ISO 23247 reference architecture
  5. 1.5Data flow between track and factory
  6. 1.6Uses in Formula 1, WEC and Formula E
  7. 1.7Benefits versus costs
  8. 1.8Regulatory testing restrictions
  9. 1.9Security of team information
  10. 1.10Case study: analysis of a twin published by a team
  1. 2.1Vehicle sensors and sampling rates
  2. 2.2CAN bus and data loggers
  3. 2.3Real-time telemetry
  4. 2.4Time-series databases
  5. 2.5Data cleaning and synchronisation
  6. 2.6Set-up and conditions metadata
  7. 2.7Cloud and on-premise storage
  8. 2.8Data quality and anomaly detection
  9. 2.9Telemetry cybersecurity
  10. 2.10Workshop: data pipeline for a session
  1. 3.1Multi-domain modelling with Modelica
  2. 3.214-degree-of-freedom vehicle model
  3. 3.3Thermal models of brakes and tyres
  4. 3.4Battery and electric powertrain model
  5. 3.5Co-simulation with FMI
  6. 3.6Reduced-order models
  7. 3.7Parameter calibration with track data
  8. 3.8State estimation with a Kalman filter
  9. 3.9Model uncertainty
  10. 3.10Lab: calibrating a brake thermal model
  1. 4.1Machine learning on telemetry
  2. 4.2Regression to predict degradation
  3. 4.3Hybrid physics-data models
  4. 4.4Fault detection through anomalies
  5. 4.5Lap time prediction
  6. 4.6Cross-validation and overfitting
  7. 4.7Model explainability for engineers
  8. 4.8Deploying models on the pit wall
  9. 4.9Data ethics and ownership
  10. 4.10Practical exercise: predicting tyre degradation
  1. 5.1Set-up optimisation before the event
  2. 5.2Race strategy with Monte Carlo simulation
  3. 5.3Predictive maintenance of components
  4. 5.4Life management of homologated parts
  5. 5.5Integration with the driver simulator
  6. 5.6Dashboards for engineers
  7. 5.7Real-time decision-making
  8. 5.8Measuring the value delivered by the twin
  9. 5.9Implementation roadmap
  10. 5.10Case study: pit stop strategy with Monte Carlo
  1. 6.1Defining the scope of the twin
  2. 6.2Data sources and architecture
  3. 6.3Physical or hybrid model
  4. 6.4Calibration and validation
  5. 6.5Chosen operational application
  6. 6.6Results dashboard
  7. 6.7Value and uncertainty analysis
  8. 6.8Implementation plan
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Dymola, MATLAB/Simulink, Python (pandas, scikit-learn), InfluxDB and Grafana, in free, academic or evaluation versions. Practice is based on real procedures: calibrating thermal models with telemetry, predicting tyre degradation and Monte Carlo simulation of race strategy. Assessment is continuous by module, plus the final project.

Standards covered: ISO 23247, FMI 3.0, ISO 11898, ISO/IEC 27001:2022, ASME V&V 10, ISO 10303, Regulation (EU) 2016/679.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 23247, FMI 3.0 and ISO 11898 applied to real cases, not as a theoretical annex.
Industry software:Dymola, MATLAB/Simulink, Python (pandas, scikit-learn), InfluxDB and Grafana, in free, academic or evaluation versions.
Final project:a digital twin prototype for a specific racing case, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Scope and architecture
Define the scope of the twin, its data sources and architecture.
Physical or hybrid model
Build the physical or hybrid model of the system.
Calibration and validation
Calibrate and validate the model against track data.
Operational application
Apply the twin to a chosen operational case with a results dashboard and value analysis.
Final project
Deliver the implementation plan, report and presentation, reviewed by your tutor.
Career opportunities

Where this takes you

Digital Twin EngineerRacing Data EngineerStrategy EngineerSimulation EngineerMotorsport Data ScientistPerformance Engineer
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • An internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Dymola, MATLAB/Simulink, Python (pandas, scikit-learn), InfluxDB and Grafana, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required; you need a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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