Postgraduate Diploma in Degradation Metrics and Useful Life Models
Online 800-hour Postgraduate Diploma in Degradation Metrics and Useful Life Models. 8 modules with IEC 62660-1, ISO 12405-4 and MATLAB. Assigned tutor and open enrolment.
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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
Identify the degradation mechanisms of lithium-ion batteries.
Calculate degradation metrics from tests.
Design calendar and cyclic ageing campaigns.
Fit empirical useful life models.
Apply physical and machine learning models.
Predict remaining useful life with uncertainty quantification.
Apply life models to design and warranty decisions.
Who it's for
- Battery durability engineer looking to specialise in Degradation Metrics and Useful Life Models.
- Battery data scientist looking to specialise in Degradation Metrics and Useful Life Models.
- Electrochemical modelling engineer looking to specialise in Degradation Metrics and Useful Life Models.
- Ageing test engineer looking to specialise in Degradation Metrics and Useful Life Models.
- Warranty engineer looking to specialise in Degradation Metrics and Useful Life Models.
- Researcher in applied electrochemistry looking to specialise in Degradation Metrics and Useful Life Models.
- Graduates in engineering or sciences seeking to enter the electric vehicle sector.
What you'll work with
- IEC 62660-1
- ISO 12405-4
- UN GTR n.º 22
- Reglamento (UE) 2023/1542
- IEC 61427-2
- IEC 62619
- SAE J2288
8 modules · 800 h
1.1Growth of the SEI layer over time1.2Lithium plating at low temperature and high rate1.3Cracking of active particles due to expansion1.4Dissolution of cathode transition metals1.5Loss of lithium inventory (LLI)1.6Loss of active material (LAM)1.7Electrolyte oxidation and decomposition1.8Corrosion of current collectors1.9Ageing differences between LFP and NMC1.10Practical: identification of modes from voltage curves
2.1Residual capacity and retention percentage2.2Increase in internal resistance under pulses2.3High-precision coulombic efficiency2.4Energy efficiency of charge and discharge2.5Power loss at low SoC2.6Energy throughput and equivalent full cycles2.7Quantification of LLI, LAM and ohmic resistance2.8End-of-life thresholds by application2.9Second-life suitability metrics2.10Workshop: calculating metrics from a cycling test
3.1Design of cyclic ageing tests3.2Calendar ageing tests in storage3.3Temperature, SoC and depth-of-discharge matrices3.4Periodic reference performance tests (RPT)3.5Acceleration factors and their limits3.6Duration and cost of test campaigns3.7Factorial design of the test matrix3.8Ageing results database3.9Scaling results from cell to pack3.10Case study: test matrix for an NMC811 cell
4.1Temperature dependence according to the Arrhenius law4.2Ageing proportional to the square root of time4.3Wöhler curves for batteries4.4Superposition of cyclic and calendar ageing4.5Parameter fitting by non-linear regression4.6Validation with independent usage profiles4.7Risks of long-term extrapolation4.8Life simulation with driving profiles4.9Sensitivity analysis to mean temperature4.10Practical: fitting a calendar model in Python
5.1Electrochemical models with degradation submodels5.2Modelling diffusion-limited SEI growth5.3Modelling lithium plating and its reversibility5.4Regression with features extracted from cycles5.5Recurrent neural networks for capacity series5.6Gaussian process regression5.7Combining physical and data-driven models5.8Uncertainty quantification in prediction5.9Transfer learning between cell chemistries5.10Laboratory: life prediction with Gaussian processes
6.1Definition of remaining useful life (RUL)6.2Prognostics with particle filters6.3Aggregated prediction across vehicle fleets6.4Calculating warranties from models6.5Influence of driver usage patterns6.6Charging strategies that extend life6.7Total cost of ownership and degradation6.8Confidence intervals for technical management6.9Model updating with field data6.10Case study: 8-year, 160,000 km warranty
7.1Cell selection based on durability7.2Pack oversizing due to degradation7.3Implementing life models in the BMS7.4Predictive maintenance based on degradation7.5Battery classification for second life7.6Durability requirements of UN GTR No. 227.7Customer durability specifications7.8Durability technical reports7.9Lessons from published industrial cases7.10Practical: durability report for a client
8.1Initial cell characterisation8.2Accelerated ageing plan8.3Data processing and metrics8.4Fitting the semi-empirical model8.5Complementary data-driven model8.6Life prediction in a real application8.7Validation against held-out data8.8Usage and warranty recommendations8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: 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 MATLAB, Python with scikit-learn, PyBaMM, Simscape Battery, in free, academic or evaluation versions. Tests are studied using real procedures for designing accelerated ageing campaigns, predicting useful life for warranties, and assessing batteries for second life. 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.
Identify the degradation mechanisms of lithium-ion batteries.
Calculate degradation metrics from tests.
Design calendar and cyclic ageing campaigns.
Fit empirical useful life models.
Apply physical and machine learning models.
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 level of Spanish to follow technical standards reading.
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 consists of 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
Work is carried out with MATLAB, Python with scikit-learn, PyBaMM, Simscape Battery. Free, academic or evaluation 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 qualification from the Ministry.
Yes. Modules passed in a course or diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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