Martin Claesson and Erik Norheim, Electrical Enginering

​Title: Energy Prediction of Electric Trucks' Auxiliaries
Students: Martin Claesson and Erik Norheim Erik (MPSYS)
Industrial supervisors: Victor Olsson, Volvo GTT
Supervisor/Examiner: Balázs Kulcsar

In today's society a large proportion of transportation is carried out on land by fossil-fueled vehicles but there is an increasing trend towards electrifying vehicles. A fundamental disadvantage of Electric Vehicles (EV) is the limited range. An often overlooked aspect of the energy consumption is the auxiliaries, which especially for trucks can be of a substantial proportion of the total energy consumed. This thesis investigates data driven methods to predict the auxiliary energy of electric trucks' auxiliaries using historical data from Volvo GTT.

The analysis and prediction was done on preprocessed data to ensure that the results are derived from feasible values of the signals measured. The analysis laid the groundwork of determining the quality of the data and which methods that were applicable on the problem. Results indicate that the energy consumption of auxiliaries are difficult to predict with the inputs available and does not always follow a typical nor expected pattern, despite a significant correlation with the ambient temperature and time. Furthermore, preprocessing of data proved to be a fundamental process in enabling accurate predictions.

Testing models of different complexity and types, the thesis found significant improvements of the energy prediction compared to algorithms found in relevant research papers when applied on the data. Machine Learning (ML) models performed well considering the complexity of the problem, the available signals and large amount of data. Lastly, important future work is presented that can further improve the prediction of auxiliaries and thereby contribute to more accurate range estimations.

Martin Claesson and Erik Norheim 
Category Student project presentation
Location: E2 Room 5430 Femman, Chalmers
Starts: 03 June, 2022, 14:00
Ends: 03 June, 2022, 15:00

Page manager Published: Tue 24 May 2022.