Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control
Doctoral thesis, 2020

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.

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

motion control

fleet sizing

automated driving systems

optimal energy management

predictive energy management and vehicle stability

longer heavier vehicles

total cost of ownership

long combination vehicles

vehicle stability

transportation mission

optimal control

electrified propulsion


single-track and two-track vehicle models

Opponent: Prof. David Cebon, University of Cambridge, UK


Toheed Ghandriz

Chalmers, Mechanics and Maritime Sciences, 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 proceedings

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 proceedings

Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles

Transportation Research, Part C: Emerging Technologies,; Vol. 115(2020)

Journal article

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

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

Heavy vehicles contribute approximately 25% of road transport production of CO2 that is one of the reasons for global warming and climate change caused by human activities. Therefore, regulations are set to reduce CO2 emissions of new heavy-duty vehicles. Among different solutions, one way of reducing such emissions is to use electric energy for powering these vehicles, coming from renewable sources, rather than using energy coming from diesel fuel. Another way is to use longer and heavier vehicles that consume less energy per unit freight transported and consequently produce less CO2 emissions. However, electrification of the heavy-duty vehicles, including the longer and heavier vehicles, is expensive, and it is difficult to control the motion of the longer and heavier vehicles in common traffic. This thesis proposes methodologies to reduce the cost of electrification and energy consumption of heavy-duty vehicles and suggests algorithms for safe and energy-efficient driving of longer and heavier vehicles. All the methods used in this thesis involve optimization.

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

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

Swedish Energy Agency, 2015-10-01 -- 2019-12-31.

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

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

Subject Categories

Mechanical Engineering

Transport Systems and Logistics

Vehicle Engineering

Energy Systems

Control Engineering

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance




Basic sciences


ReVeRe (Research Vehicle Resource)

Learning and teaching

Pedagogical work



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4882


Chalmers University of Technology


Opponent: Prof. David Cebon, University of Cambridge, UK

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