Thesis worker - Look Ahead Energy - Optimal Control Of An Integrated Thermal Management System

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · Machine Learning

In short

This thesis position involves developing and evaluating a machine-learning framework for identifying health states in a heavy-duty vehicle’s thermal management system. The work will focus on optimal control strategies using look-ahead information to minimize energy consumption and improve performance, comparing different estimation methods and classification approaches.

Responsibilities

Develop and evaluate a machine-learning framework to identify five health states in a heavy-duty vehicle’s active cooling refrigerant system.
Compare two ways of estimating conditions before the expansion valve: an observer-based estimate and direct sensor measurements across TXV and EXV systems.
Prepare and qualify test data, establish reliable fault labels, and develop features based on the system’s physical behaviour.
Compare different classification approaches and assess their ability to distinguish faults such as low refrigerant charge, expansion-valve underfeeding or overfeeding, and compressor degradation.
Test how well the models generalize to unseen hardware units and test sessions.
Calibrate confidence levels and define when a diagnosis should be reported as inconclusive.
Provide recommendations for further validation toward production use.

Requirements

Enrolled student pursuing a Master's degree.
Background in thermal management, energy management, electric vehicles, control systems, or related fields.
Experience with machine learning techniques.
Familiarity with data analysis and interpretation.
Proficiency in relevant software tools such as Python and Simulink.
Ability to work independently and as part of a team.
Strong analytical and problem-solving skills.

Desired Qualifications

A consistent graph, valve, and mode representation of the integrated thermal architecture.
A validated reduced-order model suitable for look-ahead control.
A hybrid predictive thermal-energy management strategy.
A quantified comparison of heat recovery, cab-circuit thermal buffering, electric heating, heat-pump operation, and heat rejection.
An assessment of the value and required accuracy of different forecast signals.
Real-time feasibility results and recommendations for production implementation.
Identified architecture or instrumentation limitations that prevent optimal coordination.
Demonstrated ability to predict temperatures for supervisory decisions with validated accuracy.
Maintained safety-critical constraints and invoked fallback strategies when optimization is infeasible.
Demonstrated lower thermal-management energy than the reference controller in scenarios where look-ahead information has actionable value.
Quantified scenarios where charging the cab coolant circuit reduces subsequent heat-pump energy.
Explanation of any scenarios with no benefit or degraded performance.
Quantified sensitivity to forecast errors and model mismatch.
Demonstrated a credible route to real-time execution or clearly identified simplifications.

Benefits

Excellent opportunity to get closer to Scania and build relationships for the future.
Potential to start a Scania career with a degree project.
Opportunity to work on cutting-edge technology in the automotive industry.

Skills

machine learningcontrol theorythermal managementenergy managementelectric vehiclesdata analysispythonsimulink

#energy#thermal management#electric vehicles#control systems#machine learning#optimization#automotive

About Scania Group

Scania is a world-leading provider of transport solutions committed to a better tomorrow. Our purpose is to drive the shift towards a sustainable transport system. In doing so, we are creating a world of mobility that’s better for business, society and our environment. Employing more than 50,000 people in about 100 countries, Scania’s research and development is concentrated in Sweden, while production takes place in Europe and South America.

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