Optimization data on total cost of ownership for conventional and battery electric heavy vehicles driven by humans and by Automated Driving Systems
Artikel i vetenskaplig tidskrift, 2020

In road freight transport, the emerging technologies such as automated driving systems improve the mobility, productivity and fuel efficiency. However, the improved efficiency is not enough to meet environmental goals due to growing demands of transportation. Combining automated driving systems and electrified propulsion can substantially improve the road freight transport efficiency. However, the high cost of the battery electric heavy vehicles is a barrier hindering their adoption by the transportation companies. Automated driving systems, requiring no human driver on–board, make the battery electric heavy vehicles competitive to their conventional counterparts in a wider range of transportation tasks and use cases compared to the vehicles with human drivers. The presented data identify transportation tasks where the battery electric heavy vehicles driven by humans or by automated driving systems have lower cost of ownership than their conventional counterparts. The data were produced by optimizing the vehicle propulsion system together with the loading/unloading schemes and charging powers, with the objective of minimizing the total cost of ownership on 3072 different transportation scenarios, according to research article “Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles” (Ghandriz et al., 2020). The data help understanding the effects of traveled distance, road hilliness and vehicle size on the total cost of ownership of the vehicles with different propulsion and driving systems. Data also include sensitivity tests on the uncertain parameters.

Total Cost of Ownership Data

Electrified Propulsion

Automated Driving Systems

Heavy Vehicles

Transportation

Författare

Toheed Ghandriz

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Bengt J H Jacobson

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Leo Laine

Volvo Group

Chalmers, Mekanik och maritima vetenskaper

Jonas Hellgren

Volvo Group

Data in Brief

2352-3409 (ISSN)

105566

Distribuerad framdrivning mellan enheter i en lång fordonskombination

Energimyndigheten, 2015-10-01 -- 2019-12-31.

VINNOVA, 2015-10-01 -- 2019-12-31.

Highly Automated Freight Transports EUTS (AutoFreight)

VINNOVA, 2017-04-01 -- 2020-02-29.

Användning av i-dolly för lokal distribution av container trailers till logistikterminaler från en torr-hamn

VINNOVA, 2017-09-01 -- 2020-08-31.

Drivkrafter

Hållbar utveckling

Innovation och entreprenörskap

Styrkeområden

Transport

Energi

Fundament

Grundläggande vetenskaper

Ämneskategorier

Farkostteknik

Lärande och undervisning

Pedagogiskt arbete

DOI

10.1016/j.dib.2020.105566

Mer information

Skapat

2020-04-18