In the field of heavy-duty vehicles, fuel efficiency and environmental protection are factors that need to be focused on, while the aerodynamic drag generated during vehicle travelling is one of the most influential aspects. This thesis delves into the aerodynamic simulation of Scania trucks using the open-source Computational Fluid Dynamics (CFD) tool, OpenFOAM v2206. This study rigorously investigates the aerodynamics of two Scania truck models under different operating conditions, including scenarios with different crosswind environments at high speeds.The core of this study is to compare and analyse the computational results of OpenFOAM v2206 and its predecessor OpenFOAM v3.0+ in a number of aspects, in order to elucidate the evolution and improvement of CFD techniques and their practical impact on vehicle simulation performance. In order to save computational resources, the RANS method was used for the steady-state simulations. Preliminary comparisons were also made with results from PowerFLOW, another CFD software widely used within the Scania group.Another important part of this thesis is the exploration of an alternative meshing method (ANSA Hextreme Mesh) in CFD simulations. As a widely used pre-processing software in the Scania group today, analysing and comparing the advantages and disadvantages of ANSA and OpenFOAM in terms of meshing, such as the ease of meshing and the accuracy of aerodynamic predictions, can help to provide valuable guidance for the application of truck shape design and aerodynamic simulation.The results indicate that OpenFOAM v2206 excels in predicting aerodynamics and has utility in optimising truck design. Compared to OpenFOAM v3.0+, OpenFOAM v2206 shows smaller discrepancies in results with PowerFLOW. Further exploration is required regarding transient simulations using OpenFOAM. In terms of meshing methods, a simplified model (Allan Body) was investigated, and there is further research to be done on meshing the complete truck.In conclusion, this thesis presents a comprehensive and in-depth exploration of truck aerodynamics using advanced CFD tools. The results not only deepen the understanding of airflow dynamics around heavy vehicles, but also pave the way for the development of more aerodynamically efficient and environmentally friendly truck designs.

Automated gear-shifts are critical procedures for the driveline as they are demanded to work as fast and accurate as possible. The torque control of a driveline is especially important for the driver’s feeling of driveability. In the case of gear-shifts and torque control in general, the boost pressure is key to achieve good response and thereby a fast gear-shift. An experimental study is carried out to investigate the phenomena of boost pressure drop during gear-shift and gather data for the modelling work. Results confirm the stated fact on the influence of boost pressure drop on gear-shift completion time and also indicate a clear linear dependence between initial boost pressure and the following pressure drop. A dynamic predictive model of the engine is developed with focus on implementation in a heavy duty truck, considering limitations computational complexity and calibration need between truck configurations. The resulting approach is based on a mean value modelling scheme that uses engine control system parameters and functions when possible. To be able to be predictive, a model for demanded torque and engine speed during the gear-shift is developed as reference inputs to the simulation. The simulation is based on a filling and emptying process throughout the engine dynamics, and yields final values of several engine variables such as boost pressure. The model is validated and later evaluated in comparison to measurements gathered in test vehicle experiments and in terms of robustness to input and model deviations. Computer simulations yield estimations of the boost pressure drop within acceptable limits. Consid- ering estimations used prior to this thesis the performance is good. Input deviations and modelling inaccuracies are found to inflict significant but not devastating deviations to the model output, possibly more over time with ageing of hardware taken into account. Final implementation in a heavy duty truck ecu is carried out with results indicating that the current implementation of the module is relatively computationally heavy. At the time of ending the thesis it is not possible to analyse its performance further, and it is suggested that the module is optimized in terms of computational efficiency.

Research shows that climate changes we face today is a consequence of the increasing amounts of greenhouse gases that circulate in our atmosphere due to increased human industrial activity. Many firms and industries are therefore increasingly implementing environmental management strategies to reduce greenhouse gas emissions towards a more sustainable environment. These environmental management efforts can be broadly classified under two umbrellas which are sustainable production and sustainable consumption and these two parts need to work together in order to contribute effectively towards a more sustainable environment. The environmental management literature however reveals a gap between sustainable production and sustainable use of vehicles in the automotive industry showing that while the major global environmental impact - greenhouse gas emissions - occurs when vehicles are put to use, the environmental management efforts in the industry are skewed to the production of vehicles.An emerging trend to breach this gap is that vehicle manufacturers are providing extending services to help vehicle users minimize their greenhouse gas emissions. This study analyses the extended service packages of the global truck manufacturer, Scania, against the environmental objectives and initiatives of five road transport companies in Sweden. An exploratory case study approach was used from the perspective of the road freight transport companies to find out if extended services present suitable opportunities to extend environmental management from manufacturers to users in the road freight transport industry. The study found that the extended services provide solutions that help road freighttransport companies to achieve lower fuel consumption and lower emissions from theirvehicles. Road freight transport companies traditionally invest in environmental initiatives to gain marketing advantages but the extended services present a rare opportunity to the companies to compete on profit margins by investing in the extended services. / Market Making of a High-value Business Model in Low Cost Markets: Value Co-Creation in Swedish Industry, CeLS, Project manager: Leif-Magnus Jensen, leif-magnus.jensen@jibs.hj.se, +46 36 10 1881.

The aim of this thesis is an analysis of bending vibrations of the heavy duty truck transmission and a design of construction modifications. The next target is to evaluate the contribution of the construction modification. In the thesis is also performed analysis of shaft support stiffness.

Background  Global warming caused by greenhouse gas emissions necessitates a decrease in carbon emission caused by the truck transport industry. To combat the threat of global warming, goals are being set up on a global, national and corporate level. These goals are putting pressure on logistics service providers to decrease the emissions within the truck transport industry. Implementation of green practices is found to be especially challenging within heavy truck transport due to weight and distance of the transport characteristics. In order to achieve emission reductions, logistics service providers are dependent on the alignment with their transport buyers, known as shippers to implement green logistics practices. Purpose The purpose of this study is to identify challenges and enablers for sustainable green transitions within heavy truck transports among shippers and logistics service providers. The study formulates two research question to help achieve the purpose, these questions are focused on challenges and enablers among both shippers and logistics service providers.  Method The study employs an explorative research approach in a multiple case study setting. Three configurations of shipper-LSP relationships are studied and analyzed through thematic analysis, the researchers also conducted a cross-case analysis to compare and identify similarities and differences between the cases in order to draw conclusions.  Conclusion A green transition within the heavy truck transport sector face numerous challenges. The challenges include a high dependency on vehicle development, lacking infrastructure, alignment issues between shippers and logistics service providers and trade off dilemmas. In order to overcome the issues findings, suggest that shippers and LSPs should focus on creating shared goals in order to facilitate implementation of green logistics practices and mitigate the challenges.

The diesel engine in heavy-duty trucks is a complex system with many components working together, and a malfunction in any of these components can impact engine performance and result in increased emissions. Fault detection and diagnosis have therefore become essential in modern vehicles, ensuring optimal performance and compliance with progressively stricter legal requirements. One of the most common faults in a diesel engineis faulty injectors, which can lead to fluctuations in the amount of fuel injected. Detecting these issues is crucial, prompting a growing interest in exploring additional signals beyond the currently used signal to enhance the performance and robustness of diagnosing this fault. In this work, an investigation was conducted to identify signals that correlate with faulty injectors causing over- and underfueling. It was found that the NOx, O2, and exhaust pressure signals are sensitive to this fault and could potentially serve as additional diagnostic signals. With these signals, two different diagnostic methods were evaluated to assess their effectiveness in detecting injector faults. The methods evaluated were data-driven residuals and Random Forest classifier. The data-driven residuals, when combined with the CUSUM algorithm, demonstrated promising results in detecting faulty injectors. The O2 signal proved effective in identifying both fault instances, while NOx and exhaust pressure were more effective at detecting overfueling. The Random Forest classifier also showed good performance in detecting both over- and underfueling. However, it was observed that using a classifier requires more extensive data preprocessing. Two preprocessing methods were employed: integrating previous measurements and calculating statistical measures over a defined time span. Both methods showed promising results, with the latter proving to be the better choice. Additionally, the generalization capabilities of these methods across different operating conditions were evaluated. It was demonstrated thatthe data-driven residuals yielded better results compared to the classifier, which requiredtraining on new cases to perform effectively.

L’évolution croissante du prix des carburants ainsi que les normes antipollution de plus en plus drastiques obligent les fabricants de véhicules commerciaux à développer des solutions innovantes pour réduire la consommation de carburant. Dans cet objectif, comme une grande partie de l’énergie contenue dans le carburant est directement relâchée à l’ambient sous forme de chaleur, celle-ci peut être valorisée et transformée via un cycle thermodynamique secondaire. Dans ce cadre, l’importante utilisation du cycle de Rankine à travers le monde en font un candidat naturel pour une implémentation dans un véhicule. Mais contrairement à une utilisation stationnaire, de nombreux obstacles se dressent pour une intégration totale dans un poids lourd. De nombreuses études ont été menées ces trente dernières années afin de déterminer le potentiel réel d’un tel système une fois embarqué à bord d’un véhicule. Les nombreuses sources de chaleur valorisables, les contraintes inhérentes à l’application embarquée ou encore les forts régimes transitoires induits par l’utilisation du camion doivent mener à une optimisation à la fois de l’architecture du système ainsi que de son système de contrôle. L’optimisation du système mène à un choix en terme de sources chaudes et froides, de topologie, de fluide de travail ainsi que de dimensionnement des composants afin de maximiser les performances. Le système de contrôle joue lui un rôle primordial afin de tirer un bénéfice maximum d’un tel système connaissant ses limites physiques ainsi que d’assurer une utilisation efficace. Dans cette thèse, une méthodologie de conception d’un système de valorisation des rejets thermiques est proposée. En se basant sur des simulations du véhicule complet basées sur un modèle détaillé, les thématiques de la sélection du fluide de travail, des sources chaudes et froides ainsi que l’optimisation des composants et du cycle sont approchées. Par la suite, le problème de contrôle en ligne de la surchauffe à la sortie de l’évaporateur est formalisé. En tenant compte des contraintes numériques d’implémentation, différentes stratégies de commande sont mises en place, allant du contrôleur PID à des structures plus avancées telle que la commande prédictive par modèle ou une loi de commande basée sur un observateur. La plupart de ces stratégies sont validées expérimentalement sur un banc d’essai mis en place durant la thèse / The constant evolution of oil prices and the more and more stringent automotive emission standards force the original engine manufacturers to search for innovative solutions in order to reduce oil consumption. As an important part of the energy contained in the primary carrier (the fuel) is lost to the ambient through heat, it seems convenient to recover a part of this thermal energy and to turn it into fuel consumption reduction. Thermodynamic bottoming cycle such as the Rankine cycle could be used to meet this objective. Its popular use throughout the world for electricity generation makes it a natural candidate for on-board implementation in vehicles. However, a certain number of hurdles are still present before the system can be efficiently applied to heavy-duty trucks. In the last thirty years, numerous studies heave been carried out to evaluate the real potential of that kind of system on a vehicle but nothing has yet been commercialized. The heat sources to recover from, the constraints relative to the on-board application and the long and frequent transient behavior of the vehicle mean both the system architecture and its control strategy need to be optimized. The system optimization leads to a choice in terms of working fluid, heat sources and sinks, and components sizing in order to maximize power recovery and hence the fuel saving. The control plays a major role by using the capability of such a system to ensure an efficient and safe operation and limiting the interactions with the other vehicle sub-systems. In this thesis, a system design methodology is introduced to optimize the system architecture using complete model-based vehicle simulation. The constraints relative to the mobile application are taken into consideration to evaluate the potential of such a system. Modelbased control strategies for on controlled variable, namely the superheat level, are developed. Constrained by the implementation platform, different control frameworks ranging from PID to model predictive controllers or observer based controllers are developed to fit into a normal automotive electronic control unit. Most of these novel strategies were experimentally validated on a test rig developed during the thesis

Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that are otherwise used for simulation and control applications. A downside of model-based diagnosis is the modeling effort needed when no accurate models are available, which can take a large amount of time. This has motivated the use of data-driven diagnosis. Data-driven methods do not require as much system knowledge and modeling effort though they require large amounts of data and data from faults that can be hard to gather. Hybrid fault diagnosis methods combining models and training data can take advantage of both approaches decreasing the amount of time needed for modeling and does not require data from faults. In this thesis work a combined data-driven and model-based fault diagnosis system has been developed and evaluated for the exhaust treatment system in a heavy-duty diesel engine truck. The diagnosis system combines physical insights and neural networks to detect and isolate faults for the exhaust treatment system. This diagnosis system is compared with another system developed during this thesis using only model-based methods. Experiments have been done by using data from a heavy-duty truck from Scania. The results show the effectiveness of both methods in an industrial setting. It is shown how model-based approaches can be used to improve diagnostic performance. The hybrid method is showed to be an efficient way of developing a diagnosis system. Some downsides are highlighted such as the performance of the system developed using data-driven and model-based methods depending on the quality of the training data. Future work regarding the modularity and transferability of the hybrid method can be done for further evaluation.