Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control
Doctoral thesis, 2020
Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles.
Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment.
The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems.
heterogeneous heavy vehicle fleet
propulsion system tailoring
automated driving systems
optimal energy management
predictive energy management and vehicle stability
longer heavier vehicles
total cost of ownership
long combination vehicles
single-track and two-track vehicle models
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems
Optimization Based Design of Heterogeneous Truck Fleet and Electric Propulsion
Proceedings, ITSC. IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), November 1-4, 2016, Rio de Janeiro, Brazil,; (2016)p. Art no 7795575, Pages 328-335
Paper in proceeding
Sensitivity Analysis of Optimal Energy Management in Plug-in Hybrid Heavy Vehicles
2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE),; (2017)p. 320-327
Paper in proceeding
Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles
Transportation Research, Part C: Emerging Technologies,; Vol. 115(2020)
Optimization data on total cost of ownership for conventional and battery electric heavy vehicles driven by humans and by Automated Driving Systems
Data in Brief,; Vol. 30(2020)
Ghandriz, T, Jacobson, B, Islam, M, Hellgren, J, Laine, L. Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion, Powertrain Tailoring, and Fleet Sizing
Ghandriz, T, Jacobson, B, Murgovski, N, Nilsson, P, Laine, L. Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming
Computationally Efficient Nonlinear One-and Two-Track Models for Multitrailer Road Vehicles
IEEE Access,; Vol. 8(2020)p. 203854-203875
Ghandriz, T, Jacobson, B, Nilsson, P, Laine, L. Trajectory-Following and Off-Tracking Minimization of Long Combination Vehicles: A Comparison Between Nonlinear and Linear Model Predictive Control
In this thesis, the total cost of ownership of these vehicles is minimized by customizing them for the given transportation use-cases and by performing integrated optimization of the vehicle components, vehicle size, transportation mission infrastructure, etc. The thesis includes different types of powertrains: conventional (powered by diesel fuel), battery electric, and hybrid.
Furthermore, in the thesis, the impact of heavy vehicles electrification and automation on road freight transport is studied. It is shown that automation helps the reduction in total cost of ownership of battery electric heavy vehicles and consequently the easier adoption of those vehicles in freight transport.
Moreover, in longer and heavier vehicles, electric motors can be installed on different axles of different vehicle units to reduce the costs and to increase the total vehicle power. Therefore, the resulting vehicle can be hybrid because the tractor may still be powered by diesel fuel, where the rear units may push the front ones. Therefore, this thesis also proposes online optimal and predictive energy management and motion control strategies, based on the upcoming road and trip, to safely and efficiently control the energy usage and lateral and longitudinal motion of these vehicles.
Optimal Distributed Propulsion
Swedish Energy Agency (41037-1), 2015-10-01 -- 2019-12-31.
VINNOVA, 2015-10-01 -- 2019-12-31.
Using i-dolly for local distribution of container trailers to logistic terminals from a dry port
VINNOVA (2017-03036), 2017-09-01 -- 2020-08-31.
Transport Systems and Logistics
Innovation and entrepreneurship
Areas of Advance
ReVeRe (Research Vehicle Resource)
Learning and teaching
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4882
Opponent: Prof. David Cebon, University of Cambridge, UK