Optimization data on total cost of ownership for conventional and battery electric heavy vehicles driven by humans and by Automated Driving Systems
Journal article, 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

Author

Toheed Ghandriz

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Bengt J H Jacobson

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Leo Laine

Volvo Group

Chalmers, Mechanics and Maritime Sciences

Jonas Hellgren

Volvo Group

Data in Brief

2352-3409 (ISSN)

105566

Optimal Distributed Propulsion

Swedish Energy Agency, 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.

Using i-dolly for local distribution of container trailers to logistic terminals from a dry port

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

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Roots

Basic sciences

Subject Categories

Vehicle Engineering

Learning and teaching

Pedagogical work

DOI

10.1016/j.dib.2020.105566

More information

Created

4/18/2020