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Charging management simulation

Reference number
Coordinator Scania CV AB
Funding from Vinnova SEK 675 942
Project duration February 2022 - August 2022
Status Completed
Venture Accelerate Swedish partnership
End-of-project report 2022-00596sv.pdf(pdf, 480 kB) (In Swedish)

Important results from the project

The project aimed to develop a deeper understanding of how the transition to electrified fleets for heavy transportation can be facilitated and enabled by data-driven approaches. Within the Scania project, together the project partners Autofleet, EV Energy and Spark EV Technology have explored, modeled and developed ways to introduce electric transportation, with business and environmental benefits, in simulations, as well as on AI models to make accurate and predictive range calculations. The project succeeded deliver on set scopes and objective.

Expected long term effects

The possibility of creating value from a data-driven approach to understanding electrification potential has been confirmed. Combining operational data from an ICE fleet with data on charging infrastructures, BEV vehicle performance, cost-of-ownership and range of publicly available data sources the project has showed how to produce tangible and usable analysis for both OEM:s and truck operators. These insights will allow them to better understand the needs, possibilities and limitations of electrification in specific cases.

Approach and implementation

The project was initiated by Scania defining and planning the work together with each participating company. The approach to the project has been slightly adapted during delivery. The analysis, development and deployment of tools has been based on combining historical data on operating patterns from conventional, diesel-powered trucks, with other important data sources (both publicly available and proprietary to Scania). The parties have progressed from initial hypothesis via early development/testing to completing working and usable tool prototypes.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 14 October 2022

Reference number 2022-00596