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Scania

Development of Advance Emission Control using Machine Learning in SiL Environment

Scania Södertälje kommun, Sweden

Automotive · 1,001-5,000 employees

Oct 06 Closes in 1d
machine-learning Junior (0-2 yrs) Internship Sweden
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About the role

The candidate will develop and evaluate an AI-based, closed-loop SCR control methodology using machine learning in a Software-in-the-Loop environment. Responsibilities include data preparation, model training, performance benchmarking against reference models, and documenting the results in a master thesis report.

What they look for

Machine Learning Artificial Intelligence Control Engineering Applied Mathematics Data Analysis Software-in-the-Loop SCR Control Powertrain Systems Data Preparation Model Training Validation Control Integration Mathematical Modeling Physical Functions Structured Problem Solving

Requirements

Applicants should be pursuing a degree in machine learning, artificial intelligence, control engineering, or applied mathematics. Candidates must have experience in processing large data sets and a strong understanding of mathematical models and physical control systems.

Full description

Introduction

Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.

Background

Scania develops advanced powertrain and exhaust aftertreatment systems to achieve high efficiency and low emissions across a wide range of operating conditions. Selective Catalytic Reduction (SCR) control requires accurate dosing decisions, robust emission prediction and effective use of signals available in the vehicle control system.

Objective

The objective of this master thesis is to develop and evaluate a methodology for AI-based, closed-loop SCR control. The controller shall use machine learning to determine dosing and support emission control over a representative operating cycle.

The work will use measured test data and additional data generated in a Software-in-the-Loop (SiL) environment. Inputs may include engine operating point, load, speed, temperatures, flow conditions and other control-system variables. The AI model will be benchmarked against an existing reference SCR model in SiL using a virtual truck powertrain named VTAB (Virtual Truck and Bus).

The project will establish a traceable workflow for data preparation, model training, validation and control integration. It will assess whether a data-driven model can reduce calibration effort and enable adaptable control while maintaining robust dosing and emission performance in a real powertrain.

Job description

The assignment includes the following activities:

  • Literature study on SCR control, machine learning and AI-based control methods
  • Collect, structure and quality-assure large volumes of test and SiL data
  • Select relevant control-system signals and define model inputs, outputs and constraints
  • Develop and train an AI model for SCR dosing and emission control
  • Integrate and evaluate the model in a virtual truck powertrain SiL environment
  • Compare performance with an existing reference SCR control model
  • Analyse robustness, accuracy, limitations, calibration effort and implementation potential
  • Document the methodology and present the results in a master thesis report

Education/program/focus

Machine learning, artificial intelligence, control engineering or applied mathematics.

Understanding of mathematical models and physical functions used in control systems

Experience in handling, processing and analysing large data sets. We value curiosity, structured problem solving, initiative and the ability to work both independently and collaboratively.

Contact persons and supervisors

Kim Petersson, +46 (0)8 553 825 24 kim.petersson@scania.com

Application:

Your application must include a CV, personal letter and transcript of grades

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.

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