Postgraduate Diploma in Energy Strategy and Lift-Coast for Electric Sprint Racing
Online 800-hour Postgraduate Diploma in Energy Strategy and Lift-Coast for Electric Sprint Racing. 8 modules covering FIA Formula E Technical Regulations, FIA Formula E Sporting Regulations 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
Quantify the energy balance of an electric sprint race.
Build vehicle models suitable for strategic optimisation.
Apply the lift and coast technique using a time-energy cost criterion.
Solve optimal control problems for energy management.
Manage energy targets in real time from the pit wall.
Calibrate regeneration maps and braking distribution.
Produce post-race analyses oriented to continuous improvement.
Who it's for
- Strategy engineer looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Energy engineer looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Performance engineer looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Race data analyst looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Simulation engineer looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Track engineer looking to specialise in Energy Strategy and Lift-Coast for Electric Sprint Racing.
- Graduates in engineering or sciences seeking to enter electric motorsport.
What you'll work with
- FIA Formula E Technical Regulations
- FIA Formula E Sporting Regulations
- FIA International Sporting Code
- ISO 12405-4
- SAE J1634
- IEC 62660-1
- ISO 26262
8 modules · 800 h
1.1Race format and available energy1.2Regulatory limits on usable energy1.3Race power and attack mode power1.4Front and rear regeneration1.5Aerodynamic and rolling losses1.6Consumption per lap and per sector1.7Battery temperature sensitivity1.8Influence of the safety car on the energy balance1.9Public Formula E race data1.10Case study: energy balance of an ePrix
2.1Point-mass longitudinal model2.2Powertrain efficiency model2.3Battery and state-of-charge model2.4Speed profile by circuit2.5Lap time simulator2.6Model calibration with telemetry2.7Uncertainty in key parameters2.8Reduced models for real time2.9Validation against race data2.10Practical: lap simulator in Python
3.1Physical principle of early lift3.2Lift point and braking point3.3Time cost versus energy saved3.4Time-energy exchange coefficient3.5Selecting optimal corners for L&C3.6Interaction with braking regeneration3.7Driver guidance: beeps and displays3.8Driver consistency and repeatability3.9Effect on brake temperature3.10Workshop: lift and coast point map
4.1Formulation as an optimal control problem4.2Dynamic programming applied to the stint4.3Lagrange multipliers and marginal cost4.4Convex optimisation of the power profile4.5Thermal and battery constraints4.6Safety car and full course yellow scenarios4.7Robustness to uncertainty4.8Overtaking and defence strategies4.9Comparison of heuristic solutions4.10Laboratory: optimisation with CasADi
5.1Live energy telemetry5.2Energy targets per lap5.3Recalculating targets after incidents5.4Radio communication with the driver5.5In-race prediction tools5.6Attack mode management5.7Coordination between track engineers5.8Decision-making under pressure5.9Decision logging for analysis5.10Practical: race simulation from the pit wall
6.1Regeneration limits per axle6.2Brake-by-wire and brake distribution6.3Interaction with ABS and stability6.4Regeneration maps by corner6.5Energy recovery efficiency6.6MGU temperature during regeneration6.7Pedal feedback to the driver6.8Calibration for wet conditions6.9Slip analysis during regeneration6.10Case study: regeneration tuning on an urban circuit
7.1Energy efficiency indicators7.2Driver comparisons7.3Deviations from the strategy7.4Loss analysis by sector7.5Lessons from the safety car and traffic7.6Updating the vehicle model7.7Technical report for the team7.8Preparation for the next race7.9GPS benchmarking against rivals7.10Practical: post-race energy report
8.1Selecting the study circuit8.2Building the vehicle model8.3Calibration with available data8.4Optimal strategy calculation8.5Design of lift and coast points8.6Neutralisation scenarios8.7Pit wall support tool8.8Validation and sensitivity analysis8.9Report 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 enrolment period, making 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/Simulink, Python (NumPy, SciPy), CasADi, OptimumLap, MoTeC i2 Pro, in free, academic or evaluation versions. The case studies are based on real race simulation procedures with different neutralisation scenarios, lap-by-lap energy telemetry analysis, and model calibration with track data. Assessment is continuous by module plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Quantify the energy balance of an electric sprint race.
Build vehicle models suitable for strategic optimisation.
Apply the lift and coast technique using a time-energy cost criterion.
Solve optimal control problems for energy management.
Manage energy targets in real time from the pit wall.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish 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.
You will work with MATLAB/Simulink, Python (NumPy, SciPy), CasADi, OptimumLap, MoTeC i2 Pro. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute own diploma upon passing the continuous assessment and the integrative project. This 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 recognition policy when you enrol.
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