AI-powered autonomy optimization course
Online course of 300 hours in range optimisation using AI, structured in 6 modules covering EV range factors, machine learning for consumption, intelligent optimisation and control, embedded and cloud implementation, and evaluation, ethics and regulation, with an assigned technical tutor.
AI-powered autonomy optimization course — studying from Ireland
Ireland's electromobility and energy storage sector is growing, supported by national climate and transport policy, public charging infrastructure programmes and renewable electricity generation. Research institutions and companies in Ireland work on battery systems, power electronics, hydrogen technologies and grid integration. Engineers in this area contribute to charging network design, battery management, energy storage projects and the decarbonisation of transport and industry. Policy and regulation are coordinated by government departments and the national energy regulator, alongside European rules on batteries and emissions.
Fees for Ireland: €743 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Which Irish bodies regulate or recognise electromobility, battery and hydrogen engineering?
Sustainable energy and related policy in Ireland involves the Sustainable Energy Authority of Ireland and the Department of the Environment, Climate and Communications. Engineers Ireland sets professional standards for engineers. SEIUM's qualifications are private and not accredited by any national body, so recognition depends on employers or the relevant body, and you should verify it before enrolling.
Do Irish employers in batteries and hydrogen value private online qualifications?
Ireland has growing interest in electrification, energy storage and hydrogen, with employers in energy, transport and manufacturing. They typically prioritise experience, technical competence and relevant degrees. A private online qualification may support an application, but how it is judged is for each employer. No career outcome can be guaranteed.
Can I combine this with a job, and does the workload suit part-time study?
The programme is fully online and taught in English, so you can study from anywhere in Ireland without travel or a student visa. Master's programmes total 1,900 hours, postgraduate diplomas 800 and specialist courses 300. You will have an assigned technical tutor, fees can be paid in instalments, and employer sponsorship may be possible if your employer supports energy-transition training.
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
Identify the factors that determine the range of an electric vehicle.
Train machine learning models to predict energy consumption.
Apply predictive control and reinforcement learning techniques to eco-driving.
Deploy AI models on embedded systems and in the cloud.
Assess the energy benefit through fleet trials.
Analyse the ethical and regulatory requirements of AI in vehicles.
Develop a data-driven range optimisation system.
Who it's for
- Mobility data scientists working with trip and telemetry data.
- Machine learning engineers moving into automotive energy applications.
- Vehicle control engineers.
- Embedded software engineers deploying AI models on ECUs.
- Electric fleet analysts looking to cut energy use.
- Navigation systems engineers improving range estimation.
- Engineering or science graduates looking to enter vehicle manufacturers.
What you'll work with
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- ISO/PAS 8800 (safety and artificial intelligence in road vehicles)
- ISO 26262 (functional safety of road vehicles)
- ISO 21448 (safety of the intended functionality, SOTIF)
- ISO/IEC 23894 (AI risk management)
- Regulation (EU) 2016/679 (General Data Protection Regulation, GDPR)
- ISO/IEC 42001 (AI management systems)
- SAE J1634 (energy consumption and range testing of battery electric vehicles)
- Python (scikit-learn)
- PyTorch
- XGBoost
- MATLAB
- CARLA
6 modules
1.1Vehicle energy balance1.2Influence of speed, aerodynamics and mass1.3Climate control and outside temperature1.4Driving style and accelerations1.5Topography and traffic conditions1.6Battery state of health1.7Estimation of remaining range1.8CAN, telemetry and map data1.9Estimation error metrics1.10Workshop: exploratory analysis of real trips
2.1Preparation of trip data2.2Energy feature engineering2.3Linear regression and gradient boosting2.4LSTM recurrent neural networks2.5Transformers for time series2.6Combining physical models and machine learning2.7Time-series cross-validation2.8Interpretability with SHAP values2.9Uncertainty quantification2.10Practical: consumption predictor with XGBoost
3.1Predictive speed control3.2Energy-optimal routes3.3Reinforcement learning in energy management3.4Climate control optimisation3.5En-route charging planning3.6Cooperative driving and platooning3.7Green light optimal speed advisory3.8Predictive battery thermal management3.9Controller safety constraints3.10Lab: reinforcement learning agent for eco-driving
4.1Deployment on ECUs and model quantisation4.2Edge versus cloud inference4.3Integration with the navigation system4.4Over-the-air model updates4.5System latency and robustness4.6Functional safety and SOTIF4.7Cybersecurity of AI models4.8Monitoring of model drift4.9Testing in the simulator and in the vehicle4.10Case study: remaining range in the navigation system
5.1Energy benefit metrics5.2A/B testing in fleets5.3European Artificial Intelligence Act5.4ISO/PAS 8800 on AI in vehicles5.5Driving data privacy and the GDPR5.6Bias and fairness in models5.7Driver communication and trust5.8Liability for incorrect estimates5.9Risk management according to ISO/IEC 238945.10Case study: evaluation of an eco-driving assistant
6.1Definition of the use case6.2Data collection and cleaning6.3Predictive consumption model6.4Optimisation or control algorithm6.5Validation in simulation6.6Assessment of energy savings6.7Risk and compliance analysis6.8Deployment proposal6.9Report 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 Python (scikit-learn), PyTorch, XGBoost, MATLAB and CARLA, in free, academic or evaluation versions. Practical work is based on real procedures for consumption prediction with trip data, training of eco-driving agents and validation of range estimators in fleets. Assessment is continuous by module, plus the final integrating project.
Standards covered: Regulation (EU) 2024/1689, ISO/PAS 8800, ISO 26262, ISO 21448, ISO/IEC 23894, Regulation (EU) 2016/679, ISO/IEC 42001, SAE J1634.
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 Python (scikit-learn), PyTorch, XGBoost, MATLAB and CARLA, 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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