Online Learning Models for Vehicle Usage Prediction During COVID-19
Journal article, 2024

Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BEVs). BEVs have many advantages from a sustainability perspective, but issues such as limited driving range and long recharge times slow down the transition from combustion engines. One way to mitigate these issues is by performing battery thermal preconditioning, which increases the energy efficiency of the battery. However, to optimally perform battery thermal preconditioning, the vehicle usage pattern needs to be known, i.e., how and when the vehicle will be used. This study attempts to predict the departure time and distance of the first drive each day using online machine learning models. The online machine learning models are trained and evaluated on historical driving data collected from a fleet of BEVs during the COVID-19 pandemic. Additionally, the prediction models are extended to quantify the uncertainty of their predictions, which can be used to decide whether the prediction should be used or dismissed. Based on our results, the best-performing prediction models yield an aggregated mean absolute error of 2.75 hours when predicting departure time and 13.37 km when predicting trip distance.

Uncertainty quantification

Online machine learning

COVID-19 pandemic

Vehicle usage prediction

Author

Tobias Lindroth

Volvo Cars

Axel Svensson

Volvo Cars

Niklas Åkerblom

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Volvo Cars

Mitra Pourabdollah

Volvo Cars

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 25 1-10

EENE: Energy Effective Navigation for EVs

FFI - Strategic Vehicle Research and Innovation (2018-01937), 2019-01-01 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Vehicle Engineering

Computer Science

DOI

10.1109/TITS.2024.3361676

More information

Latest update

8/13/2024