Work

Digital twin framework for electric truck route and infrastructure planning in the Nordic region

Year: 2026

Type: article

Abstract: The electrification of road transportation poses multi-faceted challenges in the Nordic region, where cold ambient temperatures, long distances, and limited charging infrastructure affect operational efficiency and feasibility. This study presents a novel integrated framework that combines artificial intelligence, digital twins, and co-simulation to support energy-efficient routing and infrastructure planning for heavy-duty electric vehicles in the Nordic region. A Gradient Boosting model was trained on over 180 real-world intercity trips of data collected from an electric truck to estimate battery depth of discharge based on key operational factors. Motor load, torque, and total weight emerged as dominant predictors, while ambient temperature had a moderate effect. A digital twin environment was developed to assess the impact of various charging infrastructure scenarios, and a real-time co-simulation model was used to evaluate energy flow and support cost analysis. Results confirm the framework’s capability to support infrastructure planning and fleet operation optimization during winter. The approach provides actionable insights for logistics operators and policymakers who are advancing the electrification of heavy-duty vehicles in the Nordic region.

Source: Transportation Engineering

DOI: 10.1016/j.treng.2026.100426

Authors: Tero Kaarlela, Sergei Alekseev, Petteri Maljamäki, Eero Ikäheimo, Emil Kurvinen

Institutions: University of Oulu, Centria University of Applied Sciences, Lapland University of Applied Sciences

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Climate action, Affordable and clean energy, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,670