- Comparative study of suitable modelling techniques for the DT application;
- Implementation energy usage estimation and prediction DT using explainable machine learning model;
- Demonstration of DT;
- Report on your work;
- Final presentation of work to the department colleagues.
- You are a master student in a relevant field, for example automotive engineering, machine learning;
- Knowledge in Matlab/Simulink;
- You enjoy developing innovative solutions to technically challenging problems and have the right mix of intellectual curiosity and pragmatism.
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Internship | Digital Twin for Vehicle Energy Estimation using Explainable Machine Learning Model - Helmond, Nederland - TNO
Beschrijving
About this position
The world needs powertrains with zero-environmental impact. We create them at TNO Powertrains. Do you want to be a part of this endeavour? Join us at the Automotive Campus in Helmond, and accelerate the energy transition today Among the various research questions being tackled at TNO Powertrains, we are enabling the seamless introduction of commercial electric vehicles (EV's) to comply with the upcoming Zero Emission Zones (ZEZ). To this effect, we analyse the electric energy implications from grid to wheel, research methods to optimize energy usage (such as battery energy storage systems, charge scheduling, energy usage prediction), and apply these methods for the benefit of the society.
What will be your role?
Energy usage prediction of electric vehicles is critical to cost saving in fleet operation as it is essential in optimizing the vehicles in a fleet, fleet scheduling, charge time optimization, minimizing battery degradation, grid-load balancing and more.
Your assignment, should you choose to accept, is to develop a Digital Twin (DT) to accurately estimate and predict the energy usage of EV's. You will explore and compare EV energy usage models such as physics-based models, data-driven models, and explainable machine learning models for use with DT's. The model should capture the effect of factors including temperature, payload, and auxiliary power load on the energy consumption. You will then proceed to implement the Digital Twin and showcase the benefits of using DT's for energy usage estimation and prediction in EV's. The assignment involves the following tasks:
Further details of the assignment are open to discussion and will be determined in consultation with you. The length of the assignment is also to be adjusted according to the needs of your educational institution.
What we expect from you
What you'll get in return
You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It's not just looking that interests us; you and your knowledge are essential to our innovation. That's why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.
