Battery degradation prediction course
Online course of 300 hours in battery degradation prediction, structured in 6 modules covering ageing mechanisms, ageing tests and data, degradation models, applied machine learning, and applications and regulation, with an assigned technical tutor.
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
Explain the physico-chemical degradation mechanisms of lithium batteries.
Design ageing test campaigns and process their data.
Apply empirical, semi-empirical and electrochemical degradation models.
Train machine learning models to estimate SOH and remaining useful life.
Quantify the uncertainty of degradation predictions.
Interpret regulatory requirements for battery durability and the battery passport.
Develop a degradation prediction model applied to a real case.
Who it's for
- Battery engineers who need to forecast cell and pack lifetime.
- Battery data scientists.
- BMS engineers implementing state of health estimation.
- Warranty analysts assessing battery-related risk.
- Second-life battery specialists.
- Electrochemistry researchers.
- Engineering or science graduates looking to enter battery manufacturers.
What you'll work with
- Regulation (EU) 2023/1542 (EU Batteries Regulation)
- GTR No. 22 (in-vehicle battery durability for electrified vehicles)
- Regulation (EU) 2024/1257 (Euro 7) (vehicle emissions and battery durability type-approval)
- IEC 62660-1 (performance testing of lithium-ion cells for EVs)
- ISO 12405-4 (performance testing of lithium-ion traction battery packs and systems)
- SAE J2288 (life cycle testing of EV battery modules)
- IEC 61960-3 (secondary lithium cells and batteries for portable applications)
- IEC 62619 (safety of industrial lithium batteries)
- PyBaMM
- Python (scikit-learn)
- TensorFlow
- MATLAB
6 modules
1.1Cycle and calendar ageing1.2Growth of the SEI interphase1.3Lithium plating on the anode1.4Loss of active material1.5Loss of lithium inventory1.6Cracking of cathode particles1.7Effect of temperature, SOC and C-rate1.8Influence of depth of discharge1.9Differences between NMC, LFP and LTO1.10Workshop: incremental capacity diagnosis
2.1Design of ageing campaigns2.2Life tests according to IEC 62660-12.3Reference performance tests2.4Measurement of capacity and internal resistance2.5Electrochemical impedance spectroscopy2.6Public datasets from NASA, CALCE and Oxford2.7Data from fleets in service2.8Data preprocessing and alignment2.9Data quality and outliers2.10Practical: analysis of the NASA battery dataset
3.1Empirical and semi-empirical models3.2Arrhenius law and square-root-of-time dependence3.3Electrochemical models of SEI growth3.4Rainflow cycle counting3.5Knee point modelling3.6State of health estimation3.7Remaining useful life prediction3.8Particle filters for prognostics3.9Gaussian processes for degradation3.10Lab: semi-empirical calendar ageing model
4.1Neural networks to estimate SOH4.2Early prediction from the first cycles4.3LSTM and convolutional architectures4.4Transfer learning between chemistries4.5Physics-informed neural networks4.6Prediction intervals and uncertainty4.7Validation and error metrics4.8Deployment in the BMS and in the cloud4.9Model explainability4.10Case study: remaining useful life with LSTM
5.1Battery warranties and state of health5.2Battery passport under Regulation (EU) 2023/15425.3GTR No. 22 on battery durability5.4Euro 7 durability requirements5.5Valuation of used electric vehicles5.6Second life and reuse5.7Charge management to extend life5.8Mitigation strategies in the BMS5.9Independent SOH certification5.10Case study: battery valuation of a used vehicle
6.1Selection of chemistry and application6.2Data acquisition and preparation6.3Analysis of dominant mechanisms6.4Reference physical or semi-empirical model6.5Data-driven model6.6Comparison and uncertainty quantification6.7Application to a warranty or second-life case6.8Conclusions and future work6.9Report and presentation6.10Final project: final 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. There are no fixed timetables: you progress at your own pace within the intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is PyBaMM, Python (scikit-learn), TensorFlow and MATLAB, in free, academic or evaluation versions. Practical work is based on real procedures for accelerated cell ageing campaigns, state of health estimation with fleet data and valuation of batteries for second life. Assessment is continuous by module, plus the final integrating project.
Standards covered: Regulation (EU) 2023/1542, GTR No. 22, Regulation (EU) 2024/1257 (Euro 7), IEC 62660-1, ISO 12405-4, SAE J2288, IEC 61960-3, IEC 62619.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- 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.
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 PyBaMM, Python (scikit-learn), TensorFlow and MATLAB, 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 an internet connection, 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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