Postgraduate Diploma in Electric Fleet Operation and Predictive Maintenance
Online 800-hour Postgraduate Diploma in Electric Fleet Operation and Predictive Maintenance. 8 modules covering ISO 55001, Real Decreto 614/2001 and Python (scikit-learn). 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
Organise the daily operation of electric fleets.
Exploit vehicle telematics data using standard protocols.
Plan the maintenance of electric vehicles and their high-voltage systems.
Assess the health and degradation of batteries in service.
Develop predictive maintenance models using machine learning.
Apply reliability and asset management techniques to the fleet.
Ensure safety and regulatory compliance in maintenance.
Who it's for
- Electric fleet maintenance manager wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- Predictive maintenance data scientist wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- Fleet operations manager wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- Reliability engineer wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- High-voltage diagnostics technician wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- Vehicle telematics analyst wishing to specialise in Electric Fleet Operation and Predictive Maintenance.
- Graduates in engineering or sciences seeking to enter public transport.
What you'll work with
- ISO 55001
- Real Decreto 614/2001
- SAE J1939
- ISO 6469-3
- IEC 60812
- SAE JA1011
- Reglamento (CE) 561/2006
- Reglamento (UE) 2023/1542
8 modules · 800 h
1.1Shift and charging planning1.2Fleet state-of-charge management1.3Thermal preconditioning of vehicles1.4Efficient driving techniques1.5Real-time telematics monitoring1.6Incident management on the road1.7Daily vehicle availability1.8Coordination with the charging depot1.9Fleet operational indicators1.10Case study: operational routine of a bus fleet
2.1Telematic units and CAN bus2.2SAE J1939 and FMS protocols2.3Battery data from the BMS2.4Fleet management platforms2.5Data quality and cleansing2.6Cloud data storage2.7Manufacturer data APIs2.8Privacy and data protection2.9Integration with ERP systems2.10Laboratory: decoding J1939 frames
3.1Differences from diesel vehicles3.2Electric powertrain maintenance3.3High-voltage system inspection3.4Regenerative braking and brake wear3.5Maintenance of cooling circuits3.6Tyre wear on electric vehicles3.7Preventive maintenance plans3.8Workshop equipment for high voltage3.9Training of maintenance technicians3.10Practice: preventive maintenance plan
4.1State of health and state of charge4.2Cell degradation mechanisms4.3Influence of temperature and fast charging4.4Battery ageing models4.5Diagnosis by impedance spectroscopy4.6Cell balancing in the pack4.7Warranties and replacement thresholds4.8Second-life options4.9Strategies to extend service life4.10Laboratory: SOH estimation with real data
5.1Corrective, preventive and predictive strategies5.2Condition-based maintenance5.3Anomaly detection techniques5.4Applied supervised machine learning5.5Remaining useful life models5.6Time series and LSTM networks5.7Digital twins of components5.8Deploying models in production5.9Model validation and metrics5.10Laboratory: remaining useful life model in Python
6.1Asset management according to ISO 550016.2Reliability-centred maintenance (RCM)6.3FMEA of critical components6.4MTBF and fleet availability6.5Spare parts and warehouse management6.6Maintenance contracts with manufacturers6.7Maintenance management software6.8Life-cycle costs6.9Fleet reliability indicators6.10Workshop: FMEA of a traction system
7.1Work on high-voltage systems7.2Application of Real Decreto 614/20017.3Battery incident management7.4Technical inspection of electric vehicles7.5Driving time regulations7.6Maintenance documentation records7.7Insurance and claims management7.8Workshop safety audits7.9Continuous safety improvement7.10Case study: battery failure protocol
8.1Fleet diagnosis8.2Telematics data architecture8.3Preventive maintenance plan8.4Failure prediction model8.5Battery management strategy8.6Indicators and dashboard8.7Life-cycle cost analysis8.8Continuous improvement plan8.9Report 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. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), TensorFlow, Geotab, IBM Maximo, Power BI, in free, academic or trial versions. Tests are studied using real procedures for implementing predictive maintenance in bus fleets, battery degradation analysis in service, and asset management of logistics fleets. 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.
Organise the daily operation of electric fleets.
Exploit vehicle telematics data using standard protocols.
Plan the maintenance of electric vehicles and their high-voltage systems.
Assess the health and degradation of batteries in service.
Develop predictive maintenance models using machine learning.
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 English to follow technical regulatory texts.
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 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The programme uses Python (scikit-learn), TensorFlow, Geotab, IBM Maximo, Power BI. Free, academic or trial versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM 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 credited towards the advanced programme. Check the credit transfer policy when you enrol.
Related programmes
Postgraduate Diploma in NIS2/IEC 62443 Compliance for EV Infrastructure
Online Postgraduate Diploma of 800 hours in NIS2/IEC 62443 compliance for EV Infrastructure. 8 modules covering Directive (EU) 2022/2555, IEC 62443-3-3 and Wazuh. Assigned tutor and…
Postgraduate Diploma in Current Collection, Storage and Regeneration in Rail
Online Postgraduate Diploma of 800 h in Current Collection, Storage and Regeneration in Rail. 8 modules covering EN 50163, EN 50367 and OpenPowerNet. Assigned tutor and…
Postgraduate Diploma in Magnetism, Inductors and High-Frequency Losses
Online 800-hour postgraduate diploma in magnetism, inductors and high-frequency losses. 8 modules covering IEC 62044-3:2000, IEC 60404-6:2018 and Ansys Maxwell. Assigned tutor and open enrolment.
More in Electromobility, Batteries & Hydrogen
All Electromobility, Batteries & Hydrogen programmes → · How to apply · Fees & financing · Scholarships