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Modern vehicles use Electronic Control Units (ECU), connected via Controller Area Network (CAN) to perform functions. Many of these functions are distributed across several ECUs. This network interconnection enables the sharing of sensors, calculated information and actuators. As new functionality is added, the number of ECUs and their complexity increase. This paper describes the values and possibilities of a Hardware-In-the-Loop (HIL) based Integration Lab, which enables a wide range of automatic tests to be performed on networked ECUs. The Integration Lab is the complex rebuild of a Scania truck/bus, containing the ECU superset, for connecting and testing networked ECUs. It involves more than 30 ECUs and eleven CAN networks.

The upcoming European legislation for heavy duty vehicles, EuVI, includes rules on monitoring of urea consumption and urea tank volume. These new rules put new demands on the level sensing system monitoring the urea tank. The normal vehicle mounted liquid level sensing system today consists of a single, more or less vertically placed, sensor. When the vehicle is tilted the level of the fluid at the sensor position normally changes. This results in a measurement error in the calculated volume, as the volume is calculated based only on the information from the level sensor. The presented modeling studies investigate the feasibility of using sensor fusion to improve the accuracy of liquid volume estimation on a heavy duty truck. In the first study, the signals from multiple level sensors located in various liquid containing tanks on the truck are fused.

Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance tasks, determined by mileage, calendar time, and some data driven physical models. Flexible maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance tasks between maintenance occasions. Meanwhile, successful modelling with machine learning on big data, automatic planning using constraint programming, and route optimization are hinting on the ability to achieve even higher fleet utilization by further improvements of the flexible maintenance. The maintenance program have therefore been partitioned into its smallest parts and formulated as individual constraint rules. The overall goal is to maximize the utilization of a fleet, i.e. maximize the ability to perform transport assignments, with respect to maintenance.

The effect of blockage due to the presence of the wind tunnel walls has been known since the early days of wind tunnel testing. Today there are several blockage correction methods available for correcting the measured aerodynamic drag. Due to the shape of the test object, test conditions and wind tunnel dimensions the effect on the flow may be different for two cab variants. This will result in a difference in the drag delta between so-called open-road conditions and the wind tunnel. This makes it more difficult to evaluate the performance of two different test objects when they are both tested in a wind tunnel and simulated in CFD. A numerical study where two different cab shapes were compared in both open road condition, and in a digital wind tunnel environment was performed.

Traditionally, an in-vehicle map consists of only one type of data, tailored for a single user function. For example, the navigation maps contain spatial information about the roads. On the other hand, a map built for adaptive cruise control use consists of the detected vehicles and their properties. In autonomous vehicle research, the maps are often built up as an occupancy grid where areas are classified as passable or impassable. Using these kinds of maps separately, however, is not enough to support the traffic safety enhancing and advanced driver assistance systems of today and tomorrow. Instead of using separate systems to handle individual safety or planning tasks, information could be stored in one shared map containing several correlated layers of information. Map information can be collected by any number of different sensor devices, and fusion algorithms can be used to enhance the quality of the information.

Heavy-duty transportation is one of the sectors that contributes to greenhouse gas emissions. One way to reduce CO2 emissions is to use drop-in fuels. However, when drop-in fuels are used, i.e., higher blends of alternative fuels are added to conventional fuels, solubility problems and precipitation in the fuel can occur. As a result, insolubles in the fuel can clog the fuel filters and interfere with the proper functioning of the injectors. This adversely affects engine performance and increases fuel consumption. These problems are expected to increase with the development of more advanced fuel systems to meet upcoming environmental regulations. This work investigates the composition of the deposits formed inside the injectors of the heavy-duty diesel engine and discusses their formation mechanism. Injectors with internal deposits were collected from field trucks throughout Europe. Similar content, location and structure were found for all the deposits in the studied injectors.

Increasing demands for more efficient engines and stricter legislations on exhaust emissions require more accurate control of the engine operating parameters. Engine control is based on sensors monitoring the condition of the engine. Numerous sensors, in a complex control context, increase the complexity, the fragility and the cost of the system. An alternative to physical sensors are virtual sensors, observers used to monitor parameters of the engine thus reducing both the fragility and the production cost but with a slight increase of the complexity. In the current paper a virtual intake manifold cylinder port pressure sensor is presented. The virtual sensor is based on a compressible flow model and on the pressure signal of the intake manifold pressure sensor. It uses the linearized pressure coefficient approach to keep vital performance behaviors while still conserving calibration effort and embedded system memory.

When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system capable of automatically identifying possible causes of a failure and propose recommended actions on how to get the vehicle back on the road as cost efficiently as possible. This troubleshooting system is novel in the way it integrates the remote diagnosis with the workshop diagnosis when providing recommendations. To achieve this integration, a novel planning algorithm has been developed that enables the troubleshooting system to guide the different users (driver, help-desk operator, and mechanic) through the entire troubleshooting process. In this paper we formulate the problem of integrated remote and workshop troubleshooting and present a working prototype that has been implemented to demonstrate all parts of the troubleshooting system.

There is a need for reducing fuel consumption and thereby also reducing CO2 and other emissions in all areas of transportation and the forest industry is no exception. In the particular case of timber trucks special care have to be taken when designing such vehicles; they have to be sturdy and operate in harsh conditions and they are being driven empty half the time. It is well known that the aerodynamic resistance constitutes a significant part of the vehicles driving resistance and four areas in particular, front of vehicle, gap, side/underbody and rear of the vehicle contributes about one quarter each. In order to address these issues a wind tunnel investigation was initiated where a 1:6 scale model of a timber truck was designed to operate in a 3.6 m wind tunnel. The present model resembles a generic timber truck with a flexible design such that different configurations could be tested easily.

Biofuel can enable a sustainable transport solution and lower greenhouse gas emissions compared to standard fuels. This study focuses on biodiesel, implemented in the easiest way as drop in fuel. When mixing biodiesel into diesel one can run into problems with solubility causing contaminants precipitating out as insolubilities. These insolubilities, also called soft particles, can cause problems such as internal injector deposits and nozzle fouling. One way to overcome the problem of soft particles is by filtration. It is thus of great interest to be able to quantify fuel filters’ ability to intercept soft particles. The aim of this study is to test different fuel filters for heavy-duty engines and their ability to filter out synthetic soft particles. A custom-built fuel filter rig is presented, together with some of its general design requirements. For evaluation of the efficiency of the filters, fuel samples were taken before and after the filters.