Postgraduate Diploma in Digital Twins of Battery Packs and Failure Prediction
Online 800-hour Postgraduate Diploma in Pack Digital Twins and Failure Prediction. 8 modules with ISO 23247, ISO 26262 and MATLAB/Simulink. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Define the architecture of a battery pack digital twin.
Develop reduced-order multiphysics models.
Synchronise the digital twin with real in-service data.
Analyse pack failure modes using FMEA.
Apply anomaly detection and diagnostic techniques.
Forecast failures with uncertainty quantification.
Plan the industrial deployment of digital twins.
Who it's for
- Digital twin engineer looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- Battery data scientist looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- Diagnostics engineer looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- Reliability engineer looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- Multiphysics modelling engineer looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- IoT solutions architect looking to specialise in Digital Twins of Battery Packs and Failure Prediction.
- Engineering or science graduates seeking to enter the electric vehicle industry.
What you'll work with
- ISO 23247
- ISO 26262
- IEC 60812
- IEC 61025
- Modelica Association FMI 3.0
- ISO 13374
- Regulation (EU) 2023/1542
- ISO/IEC 42001
8 modules · 800 h
1.1Definition and maturity levels of the digital twin1.2Differences between model, shadow and digital twin1.3Physical, digital and connectivity architecture1.4Cell, module and pack twins1.5Use cases in design, manufacturing and service1.6Data requirements and update frequency1.7Execution on BMS, edge or cloud1.8ISO 23247 as a reference framework1.9Return on investment of the digital twin1.10Workshop: twin architecture for a bus battery pack
2.1Pack-level equivalent circuit electrical models2.2Thermal network models with nodes per module2.3Electrothermal and ageing coupling2.4Reduced-order models for real time2.5Representation of cell-to-cell dispersion2.6Modelling of the cooling system2.7Models of interconnections and busbars2.8Validation against pack tests2.9FMI standard for co-simulation2.10Practice: pack electrothermal model in Simscape
3.1Ingestion of BMS and telematics data3.2Online parameter estimation3.3Kalman filters to update the digital twin3.4Automatic calibration with field data3.5Handling noisy or missing data3.6Detection of drift between model and reality3.7Retraining frequency3.8Cloud infrastructure for digital twins3.9Scaling to thousands of vehicles3.10Laboratory: parameter updating with EKF
4.1Cell failures due to internal short circuit4.2Connection failures and increased resistance4.3Voltage and temperature sensor failures4.4Cooling system failures4.5Loss of electrical insulation4.6Contactor and precharge failures4.7Pack FMEA analysis4.8Fault trees for critical events4.9Failure signatures in service data4.10Case study: FMEA of a 400 V pack
5.1Model-based diagnosis and residuals5.2Anomaly detection through unsupervised learning5.3Isolation forest and autoencoders5.4Detection of incipient internal short circuit5.5Identification of defective sensors5.6Adaptive thresholds and false alarms5.7Explainability of diagnoses5.8Integration of diagnostics into the BMS5.9Detection metrics and ROC curves5.10Practice: anomaly detector with an autoencoder
6.1Prediction of time to failure6.2Survival models applied to batteries6.3Forecasting with LSTM networks6.4Prediction of connection resistance evolution6.5Fusion of physical and data-driven forecasts6.6Management of forecast uncertainty6.7Prioritisation of maintenance interventions6.8Communicating alerts to the operator6.9Economic evaluation of prognostics6.10Case study: failure prognosis of a pack fan
7.1Industrial digital twin platforms7.2MLOps and model version control7.3Data security and privacy7.4Validation and acceptance by engineering7.5Integration with maintenance systems7.63D visualisation of pack status7.7Twins for type approval and warranty7.8Scalability and computing costs7.9Governance of the twin throughout its life7.10Workshop: deployment plan for a pilot fleet
8.1Definition of scope and use cases8.2Multiphysics model of the pack8.3Data synchronisation strategy8.4Failure mode analysis8.5Anomaly detection algorithm8.6Failure forecasting model8.7Deployment architecture8.8Evaluation of expected benefits8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the enrolment period, which makes it easy to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, Simscape Battery, Python with PyTorch, Azure Digital Twins, Siemens Simcenter Amesim, in free, academic or evaluation versions. Case studies draw on real procedures for early detection of internal short circuits in fleets, predictive maintenance of bus packs, and validation of multiphysics pack models. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Define the architecture of a battery pack digital twin.
Develop reduced-order multiphysics models.
Synchronise the digital twin with real in-service data.
Analyse pack failure modes using FMEA.
Apply anomaly detection and diagnostic techniques.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow technical standards documentation.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 total hours spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with MATLAB/Simulink, Simscape Battery, Python with PyTorch, Azure Digital Twins, Siemens Simcenter Amesim. Free, academic or evaluation versions are used, and the tutor will point out an open-source alternative where one exists.
SEIUM's own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.
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