In vehicle control practice, there are some variables, such as lateral tire force, body slip angle and yaw rate, that cannot or is hard to be measured directly and accurately. Vehicle model, like the bicycle model, offers an alternative way to get them indirectly, however due to the widely existent simplification and inaccuracy of vehicle models, there are always biases and errors in prediction from them. When developing advanced vehicle control functions, it is necessary and significant to know these variables in relatively high precision. Kalman filter offers a choice to estimate these variables accurately with measurable variables and with vehicle model together. In this thesis, estimation models based on Extended Kalman Filter (EKF) and Uncented Kalman Filter (UKF) are built separately to evaluate the lateral tire force, body slip angel and yaw rate of two typical passenger vehicles. Matlab toolbox EKF/UKF developed by Simo Särkkä, et al. is used to implement the estimation models. By comparing their principle, algorithm and results, the better one for vehicle state estimation will be chosen and justified. The thesis is organized in the following 4 parts: First, EKF and UKF are studied from their theory and features. Second, vehicle model used for prediction in Kalman filter is build and justified. Third, algorithms of EKF and UKF for this specific case are analysed. EKF and UKF are then implemented based on the algorithms with the help of Matlab toolbox EKF/UKF. Finally, comparisons between EKF and UKF are presented and discussed.

I dagens samhälle har det blivit mer och mer populärt med miljövänliga lösningar. Inom detta område har fordonsindustrin utmärkt sig de senaste åren genom utvecklandet av eldrivna bilar. Därför börjar även företagen som utvecklar tunga maskiner bli intresserade av att börja utveckla mer miljövänliga produkter.   Syftet med arbetet som vi utfört i samarbete med Dynapac Compaction Equipment AB i Karlskrona har varit att utveckla ett koncept till en lösning av ett miljövänligt drivsystem för deras minsta tandemvält. Det är tänkt att motsvara den vardagliga prestationen av det dieseldrivna systemet, utan att bli för dyrt, samt undvika att använda specialbeställda produkter.

The purpose of this thesis is to develop a power seat structure that can serve as an alternativeto the one developed by Koenigsegg for their upcoming model Gemera and potentially others.The power seat structure will be designed around an existing chassis and will use it as areference to create an accommodating power seat structure. It will be designed to be used witha fixed-back seat that is used in the development process of the Gemera.Koenigsegg requires that the power seat structure allows for horizontal and lifting motion, aswell as tilting. These functions will be adapted to the power seat structure designed by theproject group.Due to confidentiality concerns, some parts of this thesis will not be made available to thepublic.

This thesis investigates the design of a local planning method for a reversing single joint tractor-trailer system that can be used in a sampling-based motion planner. The motion planner used is a Rapidly-exploring Random Tree (RRT) developed by Scania. The main objective of a local planning method is to generate a feasible path between two poses, which is needed when expanding the search tree in an RRT. The local planning method described in this thesis uses a set of curves, similar to Reeds-Shepp curves, feasible for a single joint tractor-trailer system. The curves are found by solving a constrained optimization problem that adheres to the kinematic model of the system. The reference for the tractor is generated by discretizing the path between curves. The reference for the trailer is generated by simulating the mission backwards where the curve radiuses are used as input. Simulating the mission backwards circumvents the instability of the system when reversing. The generated references are then compared to references generated by a lattice-based motion planner. The length of the references generated by the RRT are smaller than those generated by the lattice-based motion planner in simple open environments. The RRT had issues finding a path in cases where the environment was complex while the lattice-based motion planner found a path in every scenario. The computational time was significantly lower for the RRT in all simulations. The RRT generates all references between any two given poses while the lattice-based motion planner approximate the start and goal poses to the closest vertex in the search-space.  The references generated by the RRT did not perform optimally when small turns were needed along the curves due to the distance needed for maneuvering the tractor. Therefore, a new optimization problem has to be defined for which the small turns are considered.

As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.

The need for faster and more reliable logistics solutions is rapidly increasing. This is due to higher demands on the logistical services to improve quality,  quantity, speed and reduce the error tolerance. An arising solution to these increased demands is automated solutions in warehouses, i.e., automated material  handling. In order to provide a satisfactory solution, the vehicles need to be smart and able to solve unexpected situations without human interaction.  The purpose of this thesis was to investigate if obstacle detection and avoidance in a semi-unknown environment could be achieved based on the data from a 2D LIDAR-scanner. The work was done in cooperation with the development of a new load-handling vehicle at Toyota Material Handling. The vehicle is navigating from a map that is created when the vehicle is introduced to the environment it will be operational within. Therefore, it cannot successfully navigate around new unrepresented obstacles in the map, something that often occurs in a material handling warehouse. The work in this thesis resulted in the implementation of a modified occupancy grid map algorithm, that can create maps of previously unknown environments if the position and orientation of the AGV are known. The generated occupancy grid map could then be utilized in a lattice planner together with the A* planning algorithm to find the shortest path. The performance was tested in different scenarios at a testing facility at Toyota Material Handling.  The results showed that the occupancy grid provided an accurate description of the environment and that the lattice planning provided the shortest path, given constraints on movement and allowed closeness to obstacles. However, some performance enhancement can still be introduced to the system which is further discussed at the end of the report.  The main conclusions of the project are that the proposed solution met the requirements placed upon the application, but could benefit from a more efficient usage of the mapping algorithm combined with more extensive path planning. / <p>Digital framläggning</p>

In this study, CFD simulations of a complete truck are carried out to investigate the effect of altered simulation settings at cold climatic conditions. The aim of this study is to obtain knowledge through CFD simulations performed on a selected driving condition namely at a vehicle speed of 93 kph, an ambient temperature of -20 °C and for an engine operating at 25 % load. Data from measurement carried out in a climatic wind tunnel is available and utilized as boundary conditions for the simulations.The simulations are performed under steady state conditions utilizing the commercial software STAR-CCM+. The first simulation case (reference simulation case) is constructed through java macro-scripts as per the standard VTM settings at Scania. The results from the simulations are compared with the measurement data utilizing temperature validation probes. These probes are located around the engine and measure the air temperature in the underhood engine compartment. The results from the first simulation case show that the temperature of each probe located in front of the engine and above the engine agrees well with the measured probe temperatures. But the temperature of the remaining probes show larger differences with the measured probe temperatures. To investigate the larger differences in probe temperatures, additional simulations are carried out by changing specific simulation settings. For instance, this is achieved by including thermal radiation in the physics continua. Finally, a simulation of engine load of 100 % is carried out and the results from the simulation are compared with the measurement from the same engine load as well as the results from the measurement and simulation of 25 % engine load. The results from all the simulations indicate that additional boundaryconditions and/or different methodologies need to be explored to better replicate the cold climatic conditions in the simulations.

The new emission regulations for new trucks was made to decrease the CO2 emissions by 30% from 2020 to 2030. One of the solutions is hybridizing the truck powertrain with 48V or 600V that can recover brake energy with electrical machines and batteries. The control of this hybrid powertrain is key to increase fuel efficiency. The idea behind this approach is to combine two different power sources, an internal combustion engine and a battery driven electric machine, and use both to provide tractive forces to the vehicle. This approach requires a HEV controller to operate the power flow within the systems. The HEV controller is the key to maximize fuel savings which contains an energy management strategy. It uses the knowledge of the road profile ahead by GPS and maps, and strongly interacts with the control of the cruise speed, automated gear shifts, powertrain modes and state of charge. In this master thesis, the dynamic programming strategy is used as predictive energy management for hybrid electric truck in forward- facing simulation environment. An analysis of predictive energy management is thus done for receding and full horizon length on flat and hilly drive cycle, where fuel consumption and recuperation energy will be regarded as the primary factor. Another important factor to consider is the powertrain mode of the vehicle with different penalty values. The result from horizon study indicates that the long receding horizon length has a benefit to store more recuperative energy. The fuel consumption is decreased for all drive cycle in the comparison with existing Volvo’s strategy.

The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges. First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario. Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results. Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.

Scania CV is a premium truck, bus and engine manufacturer. At Scania there is a research and development department, in the thesis referred to as the R&amp;D department, which delivers ITtool solutions for internal customers at Scania. After a new IT-tool has been implemented The R&amp;D department want to be able to evaluate the benefits enabled by the IT-tool. Today the R&amp;D department mainly identifies tangible benefits such as improved quality, saved time and saved money due to the use of a new IT-tool. The R&amp;D department have expressed that they see room for improvement both in the phase of prioritizing between what IT-tool projects to start and in the evaluation of IT-tools. Scania often prioritize product development projects for external customers before IT-tool development projects for internal customers. The R&amp;D department believes that the position of IT-tool projects for internal customer could be strengthen if a more comprehensive IT-tool evaluation could be implemented. This way a better case could be made to emphasize the importance of prioritizing new IT-tool projects for internal customers. The purpose of this master thesis is to create a tool to manage benefits and disbenefits of IT-tools by making the R&amp;D department able to identify, categorize, rank and evaluate benefits and disbenefits enabled by IT-tools. The result of the thesis has been found through a literature study, multiple workshops, and semi structured interviews with employees linked to two different IT-tools A and –B together with validation in an improvement group linked to another IT-tool C. The result of the thesis is known as the IT-Tool Evaluator and consists of a list of benefit aspects with corresponding weights. Two surveys and a calculation template has been developed with the purpose to quantify how a new IT-tool fulfils these benefit aspects. One survey focus on the IT-tool user and one survey has a focus of a company perspective. The benefit aspects are visualised using radar diagrams, a score for each aspect is calculated and a total score for the IT-tool is generated. The difference in score in each of the 22 benefit aspects in the new IT-tool and the IT-tool that is being replaced decides if the new IT-tool brings a positive change (benefit) or negative change (disbenefit) in the given aspect. / Scania CV är en premiumtillverkare av lastbilar, bussar och motorer. På Scania finns en forsknings och utvecklingsavdelning, i det här examensarbetet refererad till som R&amp;Davdelningen, som hanterar IT-verktygslösningar för interna kunder på Scania. Efter att ett nytt IT-verktyg har implementerats vill R&amp;D-avdelningen kunna utvärdera vilka fördelar som möjliggjorts genom det nya IT-verktyget. Idag ser R&amp;D-avdelningen huvudsakligen till påtagliga fördelar så som förbättrad kvalité, sparad tid och sparade pengar som resultat av ett nytt IT-verktyg. R&amp;D-avdelningen har uttryckt att de ser förbättringspotential både i deras förmåga att prioritera mellan IT-verktygsprojekt och utvärdering av IT-verktygen som följer. Idag prioriterar Scania ofta produktutvecklingsprojekt för externa kunder före utvecklingsprojekt av IT-verktyg för interna kunder. R&amp;D-avdelningen är av uppfattning att detta kunde förändras om det fanns ett bättre sätt att hantera påtagliga och opåtagliga fördelar som möjliggörs genom IT-verktyg. På detta vis skulle resultaten ifrån IT-verktygsprojekt bättre kunna jämföras med fördelarna som genereras med produktutvecklingsprojekt för externa kunder. Syftet med masteruppsaten är att skapa ett verktyg som hanterar fördelar och nackdelar av IT-verktyg för att göra R&amp;D-avdelningen bättre på att identifiera, kategorisera, ranka och utvärdera fördelar och nackdelar som möjliggörs genom IT-verktyg. Resultatet av uppsatsen har tagits fram med hjälp av litteraturstudie, flertalet workshops och semistrukturerade intervjuer med Scania-anställda med kopplingar till två olika IT-verktyg samt en validering i en förbättringsgrupp kopplad till ett tredje IT-verktyg. Resultatet som döpts till ITverktygsutvärderaren består av en lista med 22 olika aspekter av potentiella nackdelar och fördelar med IT-verktyg som kategoriserats in i fyra huvudkategorier där varje aspekt har en tillhörande viktning. Två enkäter och en beräkningsmall har tagits fram med syfte att kvantifiera hur IT-verktyget uppfyller dessa aspekter. En enkät fokuserar på användaren av IT-verktyget och den andra enkäten har ett mer övergripande företagsperspektiv. De 22 oviktade aspekterna i kategorierna visualiseras med hjälp av spindeldiagram, en summa poäng räknas fram för varje kategori och en totalpoäng för IT-verktyget tas fram. Skillnaden i poäng i de 22 aspekterna mellan det nya IT-verktyget och det ersatta IT-verktyget bestämmer om det nya IT-verktyget medför en positiv förändring (fördel) eller negativ förändring (nackdel) i den givna aspekten.