Development of Advance Emission Control using Machine Learning in SiL Environment

Scania; Stjärna; On-site; SEK 150,000 - 225,000; Full time

Job summary

Scania Nederland B.V. offers a master thesis project focusing on AI-based closed-loop SCR control in a SiL environment.

The work will use measured test data and synthetic data to develop data-driven control methods for dosing and emission control within a VTAB-based virtual powertrain. The requirement includes a background in machine learning, AI, and control engineering, with emphasis on handling large datasets and validating models in a simulated vehicle environment.

Qualifications

• Master's level education in ML/AI/control engineering/applied mathematics.
• Understanding of mathematical models and physical functions used in control systems.
• Experience handling, processing and analysing large data sets.

Responsibilities

• Literature study on SCR control, machine learning and AI-based control methods.
• Collect, structure and quality‑assure large volumes of test 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.

Skills

Machine learning

Artificial intelligence

Control engineering

Applied mathematics

Education

Master's degree in ML/AI/Control Eng./Applied Math.

Job description

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This position is within one of TRATON’s companies.

Development of Advance Emission Control using Machine Learning in SiL Environment

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.

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

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