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Master'sElectromobility, Batteries & Hydrogen Applications open

Master’s in Field Battery Diagnostics and Data Analytics Engineering — Telemetry, Digital Twin, Predictive Maintenance

Online master's programme of 1,900 hours in field battery diagnostics and data analytics engineering (telemetry, digital twin and predictive maintenance): 12 modules covering batteries in the field, telemetry and data architecture, data quality and preparation, model-based diagnostics, machine learning for batteries, anomaly detection and safety, battery digital twins, predictive maintenance, state of health certification, data governance and business models, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design telemetry and data architectures for batteries in the field.

02

Process and clean battery operating data.

03

Estimate state of health using physics-based and data-driven methods.

04

Detect anomalies and safety risks in fleets.

05

Build digital twins and predictive maintenance models.

06

Apply data access, privacy and security requirements.

07

Develop a diagnostics and analytics platform for batteries in the field.

Profile

Who it's for

  • Battery data scientists working with large volumes of telemetry.
  • Battery diagnostics engineers estimating health and detecting faults in service.
  • Fleet analytics engineers monitoring electric vehicle and bus fleets.
  • Digital twin engineers modelling battery behaviour over its life.
  • Battery technical service engineers handling maintenance and repairs.
  • Battery warranty analysts using data to assess claims and residual value.
  • Engineering or science graduates aiming to join electric vehicle manufacturers.
Standards & tools

What you'll work with

  • Regulation (EU) 2023/1542 (EU Batteries Regulation)
  • ISO 12405-4 (performance testing of lithium-ion traction battery packs and systems)
  • IEC 62660-1 (performance testing of lithium-ion cells for EVs)
  • ISO 23247-1 (digital twin framework for manufacturing)
  • ISO 8000-8 (data quality concepts and measurement)
  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • Regulation (EU) 2023/2854 (European Data Act)
  • ISO/IEC 27001 (information security management systems)
  • Python with pandas and scikit-learn
  • PyBaMM
  • Apache Spark
  • Grafana
  • AWS IoT
Syllabus

12 modules · 1900 h

  1. 1.1Service life cycle
  2. 1.2Real-world operating conditions
  3. 1.3Field failures
  4. 1.4Degradation in different climates
  5. 1.5Differences between applications
  6. 1.6Value of field data
  7. 1.7Battery warranties
  8. 1.8Vehicle residual value
  9. 1.9Ecosystem players
  10. 1.10Case study: degradation analysis of a fleet
  1. 2.1BMS data
  2. 2.2Frequency and resolution
  3. 2.3Vehicle telematics units
  4. 2.4Cloud IoT platforms
  5. 2.5Time-series storage
  6. 2.6Batch and real-time processing
  7. 2.7Data schemas
  8. 2.8Data costs
  9. 2.9Model scalability
  10. 2.10Lab: telemetry ingestion pipeline
  1. 3.1Handling missing data
  2. 3.2Sensor errors
  3. 3.3Time synchronisation of sources
  4. 3.4Segmentation of charging events
  5. 3.5Temperature normalisation
  6. 3.6ISO 8000 data quality
  7. 3.7Feature engineering
  8. 3.8Fault labelling
  9. 3.9Data documentation
  10. 3.10Practical: cleaning charging data
  1. 4.1Equivalent circuit models
  2. 4.2Online identification
  3. 4.3Incremental capacity analysis in the field
  4. 4.4Differential voltage analysis
  5. 4.5Internal resistance in use
  6. 4.6Comparison with laboratory data
  7. 4.7Estimation uncertainty
  8. 4.8Limitations in the field
  9. 4.9Validation of results
  10. 4.10Workshop: capacity estimation with partial charging data
  1. 5.1State of health regression
  2. 5.2Recurrent neural networks
  3. 5.3Tree-based models
  4. 5.4Transfer learning
  5. 5.5Learning with limited data
  6. 5.6Model interpretability
  7. 5.7Cross-validation by vehicle
  8. 5.8Model deployment
  9. 5.9Model monitoring
  10. 5.10Lab: SOH model with scikit-learn
  1. 6.1Cells with abnormal self-discharge
  2. 6.2Detection of growing imbalances
  3. 6.3Detection of internal short circuits
  4. 6.4Detection of lithium plating
  5. 6.5Early warnings of thermal runaway
  6. 6.6Statistical detection methods
  7. 6.7Unsupervised methods
  8. 6.8Alert prioritisation
  9. 6.9Operational response to alerts
  10. 6.10Practical: detector of anomalous cells
  1. 7.1Digital twin concept
  2. 7.2ISO 23247 framework
  3. 7.3Physics-based and data-driven models
  4. 7.4Updating with telemetry
  5. 7.5Scenario simulation
  6. 7.6Remaining useful life prediction
  7. 7.7Operational optimisation
  8. 7.8Integration with the BMS
  9. 7.9Scaling to fleets
  10. 7.10Case study: digital twin of an electric bus
  1. 8.1Maintenance strategies
  2. 8.2Workshop diagnostics
  3. 8.3Module replacement
  4. 8.4Intervention planning
  5. 8.5Spare parts management
  6. 8.6Remote fault diagnosis
  7. 8.7Software updates
  8. 8.8Service indicators
  9. 8.9Maintenance costs
  10. 8.10Workshop: predictive maintenance plan
  1. 9.1State of health certificates
  2. 9.2Batteries Regulation requirements
  3. 9.3Rapid test methods
  4. 9.4Residual value of used vehicles
  5. 9.5Second-hand market
  6. 9.6Second-life batteries
  7. 9.7Data-driven warranties
  8. 9.8Trust and auditing
  9. 9.9Emerging certification standards
  10. 9.10Practical: SOH certification protocol
  1. 10.1Data ownership
  2. 10.2Access to vehicle data
  3. 10.3European Data Act
  4. 10.4Personal data protection
  5. 10.5Anonymisation of personal data
  6. 10.6ISO/IEC 27001 cybersecurity
  7. 10.7Data contracts
  8. 10.8Ethics of data use
  9. 10.9Auditing of data use
  10. 10.10Case study: battery data access policy
  1. 11.1Battery analytics platforms
  2. 11.2Services for fleets
  3. 11.3Services for insurers
  4. 11.4Services for finance companies
  5. 11.5Data-driven extended warranties
  6. 11.6Charging optimisation
  7. 11.7Dashboards
  8. 11.8Service pricing
  9. 11.9Scaling the business
  10. 11.10Practical: fleet dashboard in Grafana
  1. 12.1Use case definition
  2. 12.2Data architecture
  3. 12.3Data preparation
  4. 12.4Diagnostic models
  5. 12.5Machine learning models
  6. 12.6Anomaly detection
  7. 12.7System digital twin
  8. 12.8Evaluation and deployment
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme can be combined with full-time work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with pandas and scikit-learn, PyBaMM, Apache Spark, Grafana and AWS IoT, in free, academic or evaluation versions. Practical work is based on real procedures: estimating the state of health of a fleet from charging data, detecting anomalous cells with machine learning, and building a battery digital twin for predictive maintenance. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2023/1542, ISO 12405-4, IEC 62660-1, ISO 23247-1, ISO 8000-8, Regulation (EU) 2016/679, Regulation (EU) 2023/2854, ISO/IEC 27001.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2023/1542, ISO 12405-4 and IEC 62660-1 applied to real cases, not as a theoretical annex.
Industry software:Python with pandas and scikit-learn, PyBaMM, Apache Spark, Grafana and AWS IoT, in free, academic or evaluation versions.
Master's Thesis with defence:a diagnostics and analytics platform for batteries in the field, defended before a panel.
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.

Define the use case
Set the field diagnostics use case and design the data architecture.
Prepare the data
Clean, synchronise and engineer features from battery telemetry.
Build diagnostic models
Develop model-based and machine learning estimators of state of health.
Detect anomalies and twin the system
Add anomaly detection and a digital twin, then evaluate and deploy them.
Master's Thesis
A diagnostics and analytics platform for batteries in the field, defended before a panel.
Career opportunities

Where this takes you

Battery data scientistBattery diagnostics engineerFleet analytics engineerDigital twin engineerBattery technical service engineerBattery warranty analyst
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running 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 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python with pandas and scikit-learn, PyBaMM, Apache Spark, Grafana and AWS IoT, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), 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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