Modern equation-based object-oriented modeling languages which have emerged during the past decades make it easier to build models of large and complex systems. The increasing size and complexity of modeled systems requires high performance execution of the simulation code derived from such models. More efficient compilation and code optimization techniques can help to some extent. However, a number of heavy-duty simulation applications require the use of high performance parallel computers in order to obtain acceptable execution times. Unfortunately, the possible additional performance offered by parallel computer architectures requires the simulation program to be expressed in a way that makes the potential parallelism accessible to the parallel computer. Manual parallelization of computer programs is generally a tedious and error prone process. Therefore, it would be very attractive to achieve automatic parallelization of simulation programs. This thesis presents solutions to the research problem of finding practically usable methods for automatic parallelization of simulation codes produced from models in typical equationbased object-oriented languages. The methods have been implemented in a tool to automatically translate models in the Modelica modeling language to parallel codes which can be efficiently executed on parallel computers. The tool has been evaluated on several application models. The research problem includes the problem of how to extract a sufficient amount of parallelism from equations represented in the form of a data dependency graph (task graph), requiring analysis of the code at a level as detailed as individual expressions. Moreover, efficient clustering algorithms for building clusters of tasks from the task graph are also required. One of the major contributions of this thesis work is a new approach for merging fine-grained tasks by using a graph rewrite system. Results from using this method show that it is efficient in merging task graphs, thereby decreasing their size, while still retaining a reasonable amount of parallelism. Moreover, the new task-merging approach is generally applicable to programs which can be represented as static (or almost static) task graphs, not only to code from equation-based models. An early prototype called DSBPart was developed to perform parallelization of codes produced by the Dymola tool. The final research prototype is the ModPar tool which is part of the OpenModelica framework. Results from using the DSBpart and ModPar tools show that the amount of parallelism of complex models varies substantially between different application models, and in some cases can produce reasonable speedups. Also, different optimization techniques used on the system of equations from a model affect the amount of parallelism of the model and thus influence how much is gained by parallelization.

Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor. A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter. The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful. The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity. All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.

Producing more fuel efficient vehicles as well as lowering emissions are of high importance among heavy duty vehicle manufactures. One functionality of lowering fuel consumption is to use a so called \emph{look-ahead control strategy}, which uses the GPS and topography data to determine the optimal velocity profile in the future. When driving downhill in slopes, no fuel is supplied to the engine which lowers the temperature in the aftertreatment system. This results in a reduced emission reduction capability of the aftertreatment system. This master thesis investigates the possibilities of using preheating look-ahead control actions to heat the aftertreatment system before entering a downhill slope, with the purpose of lowering fuel consumption and $NO_x$ emissions. A temperature model of a heavy duty aftertreatment system is produced, which is used to analyse the fuel consumption and $NO_x$ reduction performance of a Scania truck. A Dynamic Programming algorithm is also developed with the purpose of defining an optimal control trajectory for minimizing the fuel consumption and released $NO_x$ emissions. It is concluded that the Dynamic Programming optimization initiates preheating control actions with results of fuel consumption reduction as well as $NO_x$ emissions reductions. The best case for reducing the maximum amount of fuel consumption results in 0.14\% lower fuel consumption and 5.2\% lower $NO_x$ emissions.

An increasing need for goods and passenger transportation drives continued worldwide growth in traffic. As traffic increases environmental concerns, traffic safety, and cost efficiency become ever 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 decrease the energy consumption of vehicles and increase the safety. These control systems can benefit significantly from preview road grade information. This information is currently obtained using specialized survey vehicles, and is not widely available. This thesis proposes new methods to obtain road grade information using on-board sensors. The task of creating road grade maps is addressed by the proposal of a framework where vehicles using a road network collect the necessary data for estimating the road grade. The estimation can then be carried out locally in the vehicle, or in the presence of a communication link to the infrastructure, centrally. In either case the accuracy of the map increases over time, and costly road surveys can be avoided. This thesis presents a new distributed method for creating accurate road grade maps for vehicle control applications. Standard heavy duty vehicles in normal operation are used to collect measurements. Estimates from multiple passes along a road segment are merged to form a road grade map, which improves each time a vehicle retraces a route. The design and implementation of the road grade estimator are described, and the performance is experimentally evaluated using real vehicles. Three different grade estimation methods, based on different assumption on the road grade signal, are proposed and compared. They all use data from sensors that are standard equipment in heavy duty vehicles. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver, using vehicle and road models. The operation of the estimators is adjusted during gearshifts, braking, and poor satellite coverage, to account for variations in sensor and model reliability. The estimated error covariances of the road grade estimates are used together with their absolute positions to update a stored road grade map. Highway driving trials show that the proposed estimators produce accurate road grade data. The estimation performance improves as the number of road segment traces increases. A vehicle equipped with the proposed system will rapidly develop a road grade map for its area of operation. Simulations show that collaborative generation of the third dimension for a pre-existing large area two-dimensional map is feasible. The experimental results indicate that road grade estimates from the proposed methods are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort in heavy duty vehicles. The grade estimators may also be used for on-line validation of road grade information from other sources. This is important in on-board applications, since the envisioned control applications can degrade vehicle performance if inaccurate data are used. / QC 20110408

Fuel consumption is one of the factors that are considered when deciding a vehicle’s optimal specification. In order to swiftly estimate the fuel consumed during real world driving scenarios, a simulation tool has been developed that is well suited for vehicle configuration exploration applications. The simulation method described in this paper differs from the static calculation method currently in use at Scania cv since the dynamic translation of the vehicle is considered, yet the simulation time is kept low. By adopting a more dynamic approach, the estimation accuracy is increased and simulation of fuel saving technology, e.g. intelli- gent driver support system, is enabled. In this paper, the modeling and implementation process is described. Different approaches is discussed and the choices made during the development is presented. In order to achieve a low simulation time and obtain a good compatability with Scania’s current software application, some of the influencial factors have been omitted from the model or described using simple relations. The validation of the fuel consumption estimation indicates an accuracy within three percent for motorway driving. Utilizing the newly devised simulation tool, a look-ahead cruise controller has been implemented and simulated. Instead of continuously finding the optimal control signals during the driving scenario like most look-aheadcontrollers, a dynamic programming algorithm is used to find a fuel efficient speed profile for the entire route. The speed profile is used as the reference speed for a conventional cruise controller and comparison with another simulation tool indicates that this is a fast and accurate way to emulate a real look-ahead controller.

Advances in engine oil technology and increased combustion efficiency has resulted in the longer oil intervals in vehicles. Current oil change interval practice only takes into account the mileage a vehicle has driven and does not consider other vehicle operations that affect oil life like extended idle. Routine oil sampling is one way to ensure optimal oil intervals, but the price continuous analysis can be prohibitive. It is possible to use on board diagnostic (OBD) data to correlate oil degradation to engine usage in order to develop an algorithm that is applicable to many vehicles. While much research has been conducted for light duty vehicles, little has been completed for heavy duty vehicles, in particular vehicles that idle a majority of their time. This study uses multiple heavy duty vehicles that are monitored by monthly routine oil analysis and logging of on board diagnostic data to determine the effects extended idle has on the wear rate of oil. The vehicles were used in their normal operation; this resulted in an average idle run time of 60% of run time and no less than 50% in a single vehicle. At each sample the quality of the oil and the operation of the engines were assessed. The results of the oil analysis showed very little degradation of oil. As expected, a negative correlation was seen in viscosity and total base number (TBN) but not abnormal when compared to base oil. Significant degradation was not seen even after using the vehicle passed the manufacturer recommended oil intervals. Analysis of engine operation showed that the temperature of the oil was optimal for 85% of idle operation. In addition, oil pressures at idle were sufficiently higher than the minimum pressure recommended by the manufacturer, but was less than half of the average in use oil pressure. The combination of low pressure and optimal temperature has resulted in little oil degradation. The results from the study have shown that extended idling in the study vehicles can be treated similar to long trip interval service for oil degradation. Additionally, extended idling did not result in abnormal engine wear or excessive contamination.

Typically the combustion in engines are open-loop controlled. By using an in-cylinder pressure sensor it is possible to create virtual sensors for closed-loop combustion control (CLCC). With CLCC it is possible to counteract dynamic effects as component ageing, fuel type and cylinder variance. A virtual sensor system was implemented based on a one-zone heat-release analysis, including signal processing of the pressure sensor input. A parametrisation of the heat-release based on several Vibe functions was implemented with good results. The major focus of the virtual sensor system was to perform a tolerance analysis on experimental data, where typical error sources in a production heavy-duty vehicle were identified and their effect on the estimates quantified. It could be concluded that estimates are very much dependent on the choice of heat-release and specific heat ratio models. Especially crank angle phasing has a large impact on estimation performance, stressing the importance of accounting for crankshaft torsion in production vehicles. Biodiesel advances the combustion angle and give a lower IMEP and total heat amount compared to standard diesel. However, error sensitivity is not affected. Further investigations must be made on improving the signal processing in terms of gain error compensation and filtering. Also a better understanding of how errors propagate between subsystems in a CLCC system is required for successful implementation.

Introducing an electric thermostat to the thermal management system is a way of actively controlling the temperature of the engine, which has been shown to have several possible gains regarding power, fuel consumption, emissions and engine durability. Complexity, cost and durability are key concerns that have led to no heavy duty truck on the market having an electrically controllable thermostat. This emphasizes the need for exploring alternative solutions that enables electric control of the thermostat according to the needs of heavy commercial vehicles. Several concepts have been generated to solve this problem and a model based approach in Simulink, Matlab and GT Suite was used for the development and evaluation. The most promising concept of combining a BLDC electric motor with a wax body enables electric control with a downsized actuator and full fail-safe function while showing improvements in temperature control performance compared to a traditional wax thermostat. This thesis has increased the knowledge on the subject and could allow for implementing an electrically controlled thermostat in future Scania heavy duty trucks, leading to a more durable engine with lower fuel consumption and emissions.

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<p>The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods.</p> <p> </p> <p>These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset.</p> <p> </p> <p>The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.</p>