Electrical Engineering

Welcome to the master thesis presentation
Drive Cycle Analysis for the Electric Power Train in Terminal Tractors: A Data-Driven Approach to Performance Evaluation by Fabian Magnusson and Edwin Marjanovic
Master Thesis conducted at Volvo Penta, under the supervision of Erik Jansson
Monday, February 16, 15:00
Fredrik Lamms room, Chalmers Electric Power Engineering division, https://maps.chalmers.se/#7b3e6954-1f75-413c-be6c-ed79c0801bee
Examiner: Stefan Lundberg Opponents: Selma and Martin Abstract The transition toward electrified powertrains in industrial vehicles places increased demands on understanding real world operational behavior. Terminal tractors operate under highly variable and transient conditions, making traditional standardized drive cycles insufficient for accurate performance evaluation and optimization. This thesis presents a data driven framework for analyzing, categorizing, and simulating operational drive cycles of electric terminal tractors based on real world field data.
Multivariate time series data collected from electric terminal tractors were preprocessed and segmented into individual drive cycles using application specific operational signals. A set of interpretable features capturing both steady state and dynamic behavior was extracted for each cycle. Dimensionality reduction and feature selection were performed using Principal Component Analysis and Principal Feature Analysis to retain the most informative characteristics while maintaining interpretability.
Multiple clustering techniques, including hierarchical agglomerative clustering, K-means, K-medoids, and convolutional autoencoder based clustering, were applied and evaluated using internal validation metrics. The resulting clusters revealed two distinct operational regimes, representative usage patterns, and outlying behaviors across the fleet. Representative and atypical drive cycles from each cluster were subsequently integrated into a simulation model of the electric powertrain to evaluate component behavior, energy usage, and efficiency under different load configurations.
The results demonstrate that data driven cycle analysis can effectively characterize real world usage patterns and provide valuable insights for powertrain evaluation, optimization, and predictive maintenance. The proposed methodology offers a scalable approach for leveraging operational data to support the development and refinement of electric powertrains in industrial applications.
Welcome! Fabian, Edwin and Stefan

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Presentation of master thesis

Master thesis presentations at Electrical Engineering

16 February, 2026

15.00 Fabian Magnusson & Edwin Marjanovic, MPCAS & MPEPO