Freight transport demand has escalated and will continue to do so as economiesgrow. As the traffic intensity increases, the drivers are faced with increasinglycomplex tasks and traffic safety is a growing issue. Simultaneously, fossil fuel usageis escalating. Heavy duty vehicle (HDV) platooning is a plausible solution to theseissues. Even though there has been a need for introducing automated HDV platooningsystems for several years, they have only recently become possible to implement.Advancements in on-board and external technology have ushered in new possibilitiesto aid the driver and enhance the system performance. Each vehicle is able to serveas an information node through wireless communication; enabling a cooperativenetworked transportation system. Thereby, vehicles can semi-autonomously travel atshort intermediate spacings, effectively reducing congestion, relieving driver tension,improving fuel consumption and emissions without compromising safety. This thesis presents contributions to a framework for the design and implementation of HDV platooning. The focus lies mainly on establishing and validating realconstraints for fuel optimal control for platooning vehicles. Nonlinear and linearvehicle models are presented together with a system architecture, which dividesthe complex problem into manageable subsystems. The fuel reduction potentialis investigated through simulation models and experimental results derived fromstandard vehicles traveling on a Swedish highway. It is shown through analyticaland experimental results that it is favorable with respect to the fuel consumption tooperate the vehicles at a much shorter intermediate spacing than what is currentlydone in commercially available systems. The results show that a maximum fuelreduction of 4.7–7.7 % depending on the inter-vehicle time gap, at a set speedof 70 km/h, can be obtained without compromising safety. A systematic designmethodology for inter-vehicle distance control is presented based on linear quadraticregulators (LQRs). The structure of the controller feedback matrix can be tailoredto the locally available state information. The results show that a decentralizedcontroller gives good tracking performance, a robust system and lowers the controleffort downstream in the platoon. It is also shown that the design methodologyproduces a string stable system for an arbitrary number of vehicles in the platoon,if the vehicle configurations and the LQR weighting parameters are identical for theconsidered subsystems. With the results obtained in this thesis, it is argued that a vast fuel reductionpotential exists for HDV platooning. Present commercial systems can be enhancedsignificantly through the introduction of wireless communication and decentralizedoptimal control. / QC 20111012

Class 8 heavy-duty truck platooning has demonstrated significant fuel economy benefits on routes with road grade less than±2% in literature, but there is little to no platooning research on routes with road grade greater than±2% - which make up a significant portion of U.S. highways. Therefore, the effort described in this thesis is aimed at assessing currently available two-truck platoon control strategies as well as developing new strategies to improve platoon performance on hilly terrain. Specifically, the strategies tested in this work include four types of lead truck speed control strategies and two types of platoon transmission shifting strategies. These strategies are tested using two experimentally validated heavy-duty, two-truck platoon simulation approaches where each approach has its own advantages and disadvantages. The trends observed from these two simulation approaches indicate that the lead truck speed control and transmission shifting strategies have a significant effect on the platoon fuel economy and gap control performance when the platoon operates on a hilly terrain route.

The transport industry is in the middle of a conceptual shift driven by delivering the targets set by the Paris Agreement. Proactive heavy-duty vehicle companies seek to further gather knowledge in a structured way on environmental impacts of its products and services. The method to be implemented is Life Cycle Assessment (LCA). For implementation of LCA certain organisational and operational factors pre-requirements need to be addressed. The study takes key factors of Life Cycle Management (LCM) as a framework for assessing the readiness of Scania CV AB to implement LCA. Said key factors of LCM are analysed through company-based case study observations and literature review. The results indicate the company is in the process of introducing majority of the key factors of LCM. The case study tested the possibilities of the company for LCA, and attempted second phase of LCA, Life Cycle Inventory (LCI). The greatest challenge to LCA is low availability and format of data for LCA. However, the case study deeply tested the data limits and offers good insight in actions to be taken.

This thesis presents a new method for iterative road grade estimation based on sensors that are commonplace in modern heavy duty vehicles. Estimates from multiple passes of the same road segment are merged together to form a road grade map, that is improved each time the vehicle revisits an already traveled route. The estimation algorithm is discussed in detail together with its implementation and experimental evaluation on real vehicles.  An increasing need for goods and passenger transportation drives continuing worldwide growth in road transportation while environmental concerns, traffic safety issues, and cost efficiency are becoming more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control and hybrid vehicle state-of-charge control benefit from preview road grade information. Using global navigation satellite systems an exact vehicle position can be obtained. This enables stored maps to be used as a source of preview road grade information. The task of creating such maps is addressed herein by the proposal of a method where the vehicle itself estimates the road grade each time it travels along a road and stores the information for later use.  The presented road grade estimation method uses data from sensors that are standard equipment in heavy duty vehicles equipped with map-based advanced driver assistance systems. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver in a Kalman filter, to form a road grade estimate based on a system model. The noise covariance parameters of the filter are adjusted during gear shifts, braking and poor satellite coverage. The estimated error covariance of the road grade estimate is then used together with its absolute position to update a stored road grade map, which is based on all previous times the vehicle has passed the same location.  Highway driving trials detailed in the thesis demonstrate that the proposed method is capable of accurately estimating the road grade based on few road traversals. The performance of the estimator under conditions such as braking, gear shifting, and loss of satellite coverage is presented. The experimental results indicate that road grade estimates from the proposed method are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort of heavy duty vehicles. / QC 20101119

Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors. In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES. The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems. Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.

As the greenhouse effect is an imminent concern, motivation for the development of energy efficient systems has grown fast. Today heavy-duty vehicles (HDVs) account for a growing part of the emissions from the vehicular transport sector. One way to reduce those emissions is by driving at short intervehicular distances in so called platoons, mainly on highways. In such formations, the aerodynamic drag is decreased which allows for more fuel efficient driving, meanwhile the roads are used more efficiently. This thesis deals with the question of how those platoons can be controlled without using communications between the involved HDVs. In this thesis, artificial neural networks are designed and trained to predict the velocity profile for an HDV driving over a section of road where data on the topography are available. This information is used in a model predictive controller to control the HDV driving behind the truck for which the aforementioned prediction is made. By having accurate information about the upcoming behaviour of the preceding HDV, the controller can plan the velocity profile for the controlled HDV in a way which minimizes fuel consumption. To ensure fuel optimal performance, a state describing the mass of consumed fuel is derived and minimized in the controller. A system modelling gear shift dynamics is proposed to capture essential dynamics such as torque loss during shifting. The designed controller is able to predict and change between the three highest gears making it able to handle almost all highway platooning scenarios. The prediction system shows great potential and is able to predict the velocity profile for different HDVs with an average error as low as 0.04 km/h. The controller is implemented in a simulation environment and results show that compared to a platoon without these predictions of the preceding HDV, the fuel consumption for the controlled HDV can be reduced by up to 6 %.

The thesis deals with computational modelling of heavy duty off-road vehicle driveline components. The thesis is divided to two main parts. The first part deals about modal analysis of selected driveline components. MBS computational study was performed based on modal analysis results. The second part of thesis deals with experimental verification of computational model. Pass-by noise and vibration measurement was performed. Measured signals were processed in frequency domain to find noise and vibration sources.

Heavy duty vehicle (HDV) platooning is currently a big topic both in the academic world and in industry. Platooning is a smart way to solve problems such as safety, traffic congestion, fuel consumption and hazardous exhaust emissions since its concept enables several vehicles to drive close to each other while maintaining all the security requisites. This way, each vehicle will use the so called slipstream effect, an atmospheric drag reduction that occurs behind a traveling vehicle, consuming less fuel and consequently reducing the exhausted gases. Furthermore, it increases the traffic flow since the distance between vehicles is significantly reduced. The concept and idea of platooning is not particularly new, but only in the last few decades new technology made it possible. HDV platooning scenarios for scale model trucks were developed in the completely renovated Smart Mobility Lab, in KTH, Stockholm. A LabVIEW application was developed giving a robust and stable control of the trucks while following and driving on a newly designed and built road network. The trucks are able to follow a predefined trajectory, change lane and road, platoon with each other with different platooning distances, overtake when the platoon master is changed in order to take the lead of the platoon and change speed to catch up, among other features. The last part of this thesis covers the analysis of the scenarios developed in the testbed. These scenarios represent several situations of HDV platooning, particularly the platoon catch-up case. The main object of this study was the saved fuel due to platooning, and the break-even point, i.e. the distance ratio when neither driving alone nor catching up a platoon ahead would be more feasible. Using real HDV models and their fuel consumption models, simulations were performed in order to check the benefits of platooning and the data got from the scenarios was analyzed. Finally, conclusions were drawn from the experiments where the parameters such as HDV weight, speed increment when catching up and intermediate distance when platooning were different in each trial. It was concluded that a single HDV has to travel 8 to 15 times more than the initial distance that separates it from the HDV(s) ahead and it can save 5 to 13% of fuel depending if catching up a single HDV or a platoon an already existing platoon. Furthermore, it is less beneficial for a platoon already formed to decide to catch up another HDV.

An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel with all the current software. Simulations were executed in a Simulink environment, and two test rides were performed on the E4 motorway. / En algoritm för hastighetsstyrning baserad på modell-prediktiv reglering har utvecklats och testats på befintlig styrsystem i ett Scania lastbil. Genom att använda en enkel modell av fordonet och kunskap om lutningen på vägen framför den kunde man sänka bränsleförbrukningen med nästan 1% i vissa sträckor, jämfört med en regelbaserad farthållare. Problemet är formulerat som en optimerings-problem där bränsleförbrukning och total restid måste minimeras. För att hitta den optimala lösningen användes dynamisk programmering och hela koden är skriven så att den kan exekveras på en Scania styrenehet. Koden är kan köras parallellt med den mjukvara som är installerad på styrenheten. Simuleringar utfördes i en miljö utvecklad i Simulink. Två test-körningar på E4 motorvägen utfördes.

Biomethane made from the anaerobic digestion of organic waste can provide several economic and environmental benefits such as: the valorisation of waste products, increased resource efficiency, increased retention of nutrients through recycling of biogas digestate (Banks, et al., 2011), reduction of greenhouse gas emissions (Börjesson, et al., 2016) as well as the reduction of nitrogen oxides and particulate matter emissions (Börjesson & Berglund, 2007).To help actors understand when and where biomethane solutions can succeed, including the qualitative and quantitative aspects of a solution, an Early Assessment Method has been developed. The categories included in the assessment are potential, feasibility, economic and environmental performance. The Early Assessment Method was developed using a multi-criteria framework and consists of 15 key areas and 24 key indicators that should be considered when assessing biomethane solutions. Each quantitative indicator can be assessed either with site-specific data or by using generic equations and average values while the qualitative indicators are given a five-grade scale to facilitate the assessment.The potential category focuses on assessing how much raw material there is in the investigated area and how much of the usable products can be produced. The final areas are: biomass potential, biomethane potential and bio-fertilizer potential. In the feasibility assessment, qualitative aspects are assessed using a five-grade scale. The key areas for feasibility include: customer demand, competing applications, strategies for renewable fuels, legislation, economic instruments and infrastructure suitability. Performance is assessed both for economic performance and environmental performance to understand how the biomethane solution would perform if implemented. Economic performance includes both an indicator for cost per unit produced and an indicator for the investment cost for each production step. The key areas included are: biogas generation cost, biogas upgrading cost and biomethane distribution cost. The environmental performance is evaluated to understand how environmental aspects would change if biomethane replaced an alternative fuel on the market in the studied region. Key areas to assess this are: climate impact, air quality and nutrient recycling. These areas highlight some important benefits of using biomethane over fossil fuels, which are the most common fuels for heavy-duty vehicles.A two-part Early Assessment Tool was also developed. The tool is included in the method, but can be used separately if the user has a basic knowledge of biomethane. It assists with information collection, through a questionnaire, and structuring and presenting data, through a spreadsheet. The design of the Early Assessment Tool favours simplicity and usability while striving to maintain relevant information. It is meant to be used both for educational and investigative purposes when providing an early assessment of biomethane solutions within a certain region. The result from the tool can aid when making decisions and help with identifying which local actors to involve and what consultancy work might be needed to realise a biomethane solution.