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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.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
Enrol now View syllabus Ask an advisor

AI-powered autonomy optimization course — studying from the United States

The United States is rapidly expanding its electric vehicle, battery and hydrogen industries, supported by federal incentives and private investment. Battery manufacturing and recycling facilities are growing in states such as Georgia, Tennessee, Michigan and Nevada. Automakers, energy companies and startups are developing cells, charging infrastructure and hydrogen fuel systems. Engineers in this sector work on electrochemistry, power electronics, thermal management and standards for safe and reliable energy storage.

Fees for the United States: 830 US$ (US Dollars), with instalments and employer sponsorship available.

Relevant bodies in the United States

  • U.S. Department of Energy (DOE)
  • Society of Automotive Engineers (SAE International)
  • Underwriters Laboratories (UL Solutions)
  • Electrochemical Society (ECS)

Typical roles

  • Battery Systems Engineer
  • Electrochemical Engineer
  • Power Electronics Engineer
  • Hydrogen Systems Engineer

Questions from the United States

Are fees shown in US Dollars?

Yes. All fees on this site are shown in US Dollars, and instalment options are available for many programmes.

Do I get a tutor?

Yes. Every learner is assigned a dedicated technical tutor who supports progress throughout the programme and the final project.

Virtual lab · try it before you enrol

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.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Identify the factors that determine the range of an electric vehicle.

02

Train machine learning models to predict energy consumption.

03

Apply predictive control and reinforcement learning techniques to eco-driving.

04

Deploy AI models on embedded systems and in the cloud.

05

Assess the energy benefit through fleet trials.

06

Analyse the ethical and regulatory requirements of AI in vehicles.

07

Develop a data-driven range optimisation system.

Profile

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.
Standards & tools

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
Syllabus

6 modules

  1. 1.1Vehicle energy balance
  2. 1.2Influence of speed, aerodynamics and mass
  3. 1.3Climate control and outside temperature
  4. 1.4Driving style and accelerations
  5. 1.5Topography and traffic conditions
  6. 1.6Battery state of health
  7. 1.7Estimation of remaining range
  8. 1.8CAN, telemetry and map data
  9. 1.9Estimation error metrics
  10. 1.10Workshop: exploratory analysis of real trips
  1. 2.1Preparation of trip data
  2. 2.2Energy feature engineering
  3. 2.3Linear regression and gradient boosting
  4. 2.4LSTM recurrent neural networks
  5. 2.5Transformers for time series
  6. 2.6Combining physical models and machine learning
  7. 2.7Time-series cross-validation
  8. 2.8Interpretability with SHAP values
  9. 2.9Uncertainty quantification
  10. 2.10Practical: consumption predictor with XGBoost
  1. 3.1Predictive speed control
  2. 3.2Energy-optimal routes
  3. 3.3Reinforcement learning in energy management
  4. 3.4Climate control optimisation
  5. 3.5En-route charging planning
  6. 3.6Cooperative driving and platooning
  7. 3.7Green light optimal speed advisory
  8. 3.8Predictive battery thermal management
  9. 3.9Controller safety constraints
  10. 3.10Lab: reinforcement learning agent for eco-driving
  1. 4.1Deployment on ECUs and model quantisation
  2. 4.2Edge versus cloud inference
  3. 4.3Integration with the navigation system
  4. 4.4Over-the-air model updates
  5. 4.5System latency and robustness
  6. 4.6Functional safety and SOTIF
  7. 4.7Cybersecurity of AI models
  8. 4.8Monitoring of model drift
  9. 4.9Testing in the simulator and in the vehicle
  10. 4.10Case study: remaining range in the navigation system
  1. 5.1Energy benefit metrics
  2. 5.2A/B testing in fleets
  3. 5.3European Artificial Intelligence Act
  4. 5.4ISO/PAS 8800 on AI in vehicles
  5. 5.5Driving data privacy and the GDPR
  6. 5.6Bias and fairness in models
  7. 5.7Driver communication and trust
  8. 5.8Liability for incorrect estimates
  9. 5.9Risk management according to ISO/IEC 23894
  10. 5.10Case study: evaluation of an eco-driving assistant
  1. 6.1Definition of the use case
  2. 6.2Data collection and cleaning
  3. 6.3Predictive consumption model
  4. 6.4Optimisation or control algorithm
  5. 6.5Validation in simulation
  6. 6.6Assessment of energy savings
  7. 6.7Risk and compliance analysis
  8. 6.8Deployment proposal
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

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.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2024/1689, ISO/PAS 8800 and ISO 26262 applied to real cases, not as a theoretical annex.
Industry software:Python (scikit-learn), PyTorch, XGBoost, MATLAB and CARLA, in free, academic or evaluation versions.
Final project:a data-driven range optimisation system, from the predictive model to the deployment proposal, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Define the use case
Set the range optimisation use case and gather and clean the data.
Build the consumption model
Train a predictive model of energy consumption.
Design the algorithm
Develop an optimisation or control algorithm and validate it in simulation.
Assess savings and risks
Quantify energy savings and analyse risks and regulatory compliance.
Final project
Present the deployment proposal in a report and presentation, reviewed by your tutor.
Career opportunities

Where this takes you

Mobility data scientistMachine learning engineerVehicle control engineerEmbedded software engineerElectric fleet analystNavigation systems engineer
Admissions & scholarships

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.

Apply now Scholarships
FAQ

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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