Voyage Planning Framework for Autonomous Inland Waterway Vessels: Ship Performance Modelling and Operational Analysis
Doktorsavhandling, 2025
Inland waterway transport offers substantial potential to mitigate greenhouse gas emissions and congestion from road freight transport. The development of advanced inland vessels equipped with clean energy systems and a high degree of automation represents a promising direction for future transport networks. Nevertheless, inland waterways are often restricted by shallow water and limited manoeuvring space. Hence, the deployment of full-scale autonomous vessels requires careful consideration of environmental and operational challenges to ensure safety and energy efficiency. To enhance the automation of inland shipping, this thesis provides an in-depth analysis of how shallow and confined water affects ship resistance, manoeuvring, control implementation, and energy efficiency. The aim of this thesis is to develop a holistic simulation framework for ship performance prediction and operational analysis.
The development of such a comprehensive framework requires modelling of vessel characteristics with hydrodynamic and river hydraulic effects. Firstly, this thesis develops a novel ship energy performance model explicitly tailored for resistance and energy consumption prediction of inland waterway vessels. It aims to generate fast and accurate predictions based on a collection of purely empirical methods. Secondly, this thesis develops a Manoeuvring Modelling Group-based manoeuvring model for motion prediction under shallow water and bank effects. Building upon this, a systematic control design was conducted with consideration of ship hydrodynamic characteristics in confined waterways. A comparison of a conventional controller and an advanced model predictive control was performed to evaluate their performance and robustness in tackling path-following tasks under complex environments with various disturbances. With these models and control methods, an integrated voyage planning framework (VPF) is proposed for analysing vessel operations. It captures a vessel’s dynamic performance with energy consumption analysis under coupled interactions between ship hydrodynamics, river hydraulics, and motion control. Based on the analysis of ship energy performance, this thesis proposes a particle swarm optimisation (PSO) module for fuel optimisation in inland waterways.
Validation studies with full-scale trial measurements revealed that the energy performance prediction model achieved promising accuracy, ensuring a mean absolute error below 10% based on finite input parameters. It was also observed that disturbances in inland waterways, such as currents and bank effects, significantly affect a vessel’s course stability, which should be carefully considered in the control system development of autonomous vessels operating in narrow channels. A series of case studies showcased the VPF’s capabilities for enabling a wide range of applications, such as route assessment, evaluation of control design, and operational energy efficiency analysis. It was shown that the PSO-based optimisation achieved an average of 5.7% fuel savings in shallow water operations with water depth-aware speed initialisation methods.
The development of such a comprehensive framework requires modelling of vessel characteristics with hydrodynamic and river hydraulic effects. Firstly, this thesis develops a novel ship energy performance model explicitly tailored for resistance and energy consumption prediction of inland waterway vessels. It aims to generate fast and accurate predictions based on a collection of purely empirical methods. Secondly, this thesis develops a Manoeuvring Modelling Group-based manoeuvring model for motion prediction under shallow water and bank effects. Building upon this, a systematic control design was conducted with consideration of ship hydrodynamic characteristics in confined waterways. A comparison of a conventional controller and an advanced model predictive control was performed to evaluate their performance and robustness in tackling path-following tasks under complex environments with various disturbances. With these models and control methods, an integrated voyage planning framework (VPF) is proposed for analysing vessel operations. It captures a vessel’s dynamic performance with energy consumption analysis under coupled interactions between ship hydrodynamics, river hydraulics, and motion control. Based on the analysis of ship energy performance, this thesis proposes a particle swarm optimisation (PSO) module for fuel optimisation in inland waterways.
Validation studies with full-scale trial measurements revealed that the energy performance prediction model achieved promising accuracy, ensuring a mean absolute error below 10% based on finite input parameters. It was also observed that disturbances in inland waterways, such as currents and bank effects, significantly affect a vessel’s course stability, which should be carefully considered in the control system development of autonomous vessels operating in narrow channels. A series of case studies showcased the VPF’s capabilities for enabling a wide range of applications, such as route assessment, evaluation of control design, and operational energy efficiency analysis. It was shown that the PSO-based optimisation achieved an average of 5.7% fuel savings in shallow water operations with water depth-aware speed initialisation methods.
operational analysis
control design
energy efficiency
autonomous vessels
voyage planning
inland waterways
EE lecture hall, EDIT-building
Opponent: Professor Emeritus Apostolos Papanikolaou
Författare
Chengqian Zhang
Chalmers, Mekanik och maritima vetenskaper, Marin teknik
Inland waterways are an essential but often underused part of the transport system. They offer a low-emission alternative to road transport, helping ease congestion and reduce environmental impact. Recent advances in clean energy and automation have renewed interest in developing inland waterway transport. However, these waterways are shallow, narrow, and affected by currents, making autonomous navigation challenging. Understanding how such conditions influence ship motion and energy use is therefore crucial.
This thesis develops a simulation framework that captures the behaviour of inland vessels in confined waterways. It includes an energy prediction model tailored for inland ships, a manoeuvring model accounting for shallow-water and bank effects, and control methods ranging from conventional designs to model predictive control. Fuel-efficient speed profiles are explored using a particle swarm optimisation module.
Full-scale validation shows that the energy model achieves a mean absolute error below 10%. Confined water effects were found to substantially affect the ship’s manoeuvrability, highlighting the need for adapted control strategies. Case studies demonstrate the framework’s ability to support route assessment, control evaluation, and energy analysis. With depth-aware optimisation, fuel consumption was reduced by 5–6%, illustrating its practical value for future automated inland shipping.
This thesis develops a simulation framework that captures the behaviour of inland vessels in confined waterways. It includes an energy prediction model tailored for inland ships, a manoeuvring model accounting for shallow-water and bank effects, and control methods ranging from conventional designs to model predictive control. Fuel-efficient speed profiles are explored using a particle swarm optimisation module.
Full-scale validation shows that the energy model achieves a mean absolute error below 10%. Confined water effects were found to substantially affect the ship’s manoeuvrability, highlighting the need for adapted control strategies. Case studies demonstrate the framework’s ability to support route assessment, control evaluation, and energy analysis. With depth-aware optimisation, fuel consumption was reduced by 5–6%, illustrating its practical value for future automated inland shipping.
Strategiskt forskningsprojekt på Chalmers inom hydro- och aerodynamik
Stiftelsen Chalmers tekniska högskola, 2019-01-01 -- 2023-12-31.
AUTOBarge - European training and research network on Autonomous Barges for Smart Inland Shipping
Europeiska kommissionen (EU) (EC/H2020/955768), 2021-10-01 -- 2025-09-30.
Styrkeområden
Transport
Ämneskategorier (SSIF 2025)
Transportteknik och logistik
Marinteknik
Farkost och rymdteknik
Reglerteknik
DOI
10.63959/chalmers.dt/5796
ISBN
978-91-8103-339-7
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5796
Utgivare
Chalmers