Master thesis:ML-Based Correlation of Simulation & Physical Testing for Energy Economy & Durability
Göteborg, SE, 417 15
Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.
The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment, and marine and industrial engines under the leading brands Volvo, Renault Trucks, Mack, UD Trucks, Eicher, SDLG, Terex Trucks, Prevost, Nova Bus, UD Bus, Dongfeng, Sunwin Bus and Volvo Penta.
Volvo Group Trucks Technology encompasses the production of state-of-the-art products for the truck brands of the Volvo Group, as well as Volvo Group engines and transmissions, through an international world class industrial environment.
With Volvo Group Trucks Technology, you will be part of a global and diverse team of highly skilled professionals working with energy, passion and respect for the individual to become the world leader in sustainable transport solutions.
Background of thesis project
In modern automotive development, both simulation and physical testing play crucial roles in validating vehicle performance, durability, and energy efficiency. However, aligning and correlating results from simulation models with real-world data remains a complex challenge — especially when dealing with time series data from numerous vehicles, diverse operating conditions, and varied duty cycles.
This master’s thesis aims to develop a machine learning–based framework to correlate and bridge the gap between simulation outputs and physical test data. By leveraging large-scale vehicle data collected under real-world conditions, the project will explore data-driven methods to improve simulation fidelity, accelerate validation cycles, and enhance predictive accuracy for energy economy and durability assessments.
Suitable background
Distributed Computing & Systems, Machine Learning principles
Description of thesis work
The aim of this project is to develop a machine learning–based framework to correlate and bridge the gap between simulation outputs and physical test data. By leveraging large-scale vehicle data collected under real-world conditions, the project will explore data-driven methods to improve simulation fidelity, accelerate validation cycles, and enhance predictive accuracy for energy economy and durability assessments.
Possible research directions and objectives
Investigate current practices and challenges in simulation–test correlation in the automotive domain.
Analyze large-scale time series data from multiple vehicles and use cases.
Develop machine learning models to map and correlate simulation results with real-world test outcomes.
Quantify deviations and propose data-driven adjustments to simulation models.
Validate the approach on case studies related to energy consumption, component durability, and vehicle duty cycles.
Methodology
Study of relevant theories and principles, and existing research papers, related to data driven simulation models.
Develop machine learning models to map and correlate simulation results with real-world test outcomes.
Validate the approach on case studies related to energy consumption, component durability, and vehicle duty cycles.
Thesis Level: Master
Language: thesis is to be written in English.
Starting date: February 1st, 2025
Number of students: 2
Tutor
Binay Mishra, binay.mishra@volvo.com
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Who we are and what we believe in
We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.
Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.
Group Trucks Technology are seeking talents to help design sustainable transportation solutions for the future. As part of our team, you’ll help us by engineering exciting next-gen technologies and contribute to projects that determine new, sustainable solutions. Bring your love of developing systems, working collaboratively, and your advanced skills to a place where you can make an impact. Join our design shift that leaves society in good shape for the next generation.