Volvo Group

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Assessment of Validation Evidence for Heavy-Truck Vehicle Dynamics Simulation Models
Background:
Simulation models are widely used in heavy-duty vehicle development to study vehicle behaviour, support design decisions, and reduce the need for physical prototypes. As simulation plays a larger role in development and verification, it becomes increasingly important to understand how well models represent real vehicle behaviour for their intended application.
A model is necessarily an approximation, and the required fidelity depends on how it will be used. Validation should therefore consider not only simulation-to-measurement differences, but whether the model represents the relevant behaviour with sufficient confidence for its intended use.
Vehicle dynamics models are commonly validated against physical test measurements. However, the resulting evidence depends on factors such as the selected manoeuvre, response signals, comparison metrics, test repeatability, signal processing, and reproduction of test conditions. Observed differences may therefore originate from model limitations, measurement uncertainty, or test setup, while some comparisons may provide little information about relevant model deficiencies.
A systematic understanding of which test cases and comparison methods provide meaningful validation evidence could improve both the interpretation and reuse of measurement-based vehicle-dynamics validation.
Objective:
The objective of this thesis is to develop and evaluate a systematic method for assessing measurement-based validation evidence for heavy-truck vehicle dynamics simulation models, using dSPACE Automotive Simulation Models (ASM).
Selected combinations of vehicle manoeuvres, response signals, and comparison KPIs will be evaluated with respect to their robustness to variations in measurement and analysis conditions and their sensitivity to relevant changes in the simulation model.
The work will primarily use available measurement data. The exact vehicle dynamics scope and case selection will be defined based on available data, relevant model behaviors, and practical testing opportunities. Additional physical testing may be included where suitable.
Based on the results, the study will propose how a compact and informative set of validation cases can be selected and evaluated, while identifying limitations in the available evidence and the conclusions it can support.
Possible Research Questions:
<ul>
<li>Which combinations of vehicle maneuver, response signal and KPI provide useful validation evidence for the selected vehicle dynamics scope?</li>
<li>How robust are the validation results to measurement variation, signal processing, synchronization and reproduction of test conditions?</li>
<li>How sensitive are the selected validation cases to relevant controlled changes in the simulation model?</li>
<li>Which validation cases provide complementary information, and which provide largely redundant information?</li>
<li>What limitations affect the conclusions that can be drawn, and how can the results be used to recommend a compact and effective validation set?</li>
</ul>
Desired Background:
We are looking for students with a background in areas such as Automotive Engineering, Mechanical Engineering, Applied Mechanics, Mechatronics, Engineering Physics or Control Engineering.
An interest in vehicle dynamics, simulation, numerical methods and data analysis is desirable. Programming experience, for example in Python or MATLAB, is beneficial.
Previous experience with vehicle testing, signal processing, sensitivity analysis or model validation is an advantage but not a requirement.
This thesis project is intended for 2 students.
If this sounds interesting, please send your application before 18th October 2026
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MSc Thesis for Validation

MSc Thesis for Validation

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