Most engine models are developed for control purposes and, in some cases, hard coded with a single engine type usage in mind. This is a problem since a new model is also needed when new engines are developed, as it usually takes less time than changing or modifying the old one. To facilitate a more rapid development process, there is a desire to have control-oriented models that can be adapted to new types of hardware with ease while at the same time providing fundamental insights into the physics of the engine that limit the control performance. These objectives are fulfilled by creating an open-source mean value MATLAB/Simulink model, a generic engine model with parametrization and compatibility with both VVT/VVA and SI/CI combustion.The main idea is to build on a component-based structure where the components are designed to be reused for similar processes. The engine model models the air filter, intercooler, and exhaust system components as incompressible flow restrictions. Bypass, throttle, intake/exhaust valves, and wastegate are modeled as compressible flow restrictions. Adiabatic control volumes are placed between each component to keep track of masses, pressures, and temperatures. The few remaining components are modeled separately, with unique functions for each model. As a concept demonstration of the generality of the approach, two engines, a 6-cylinder 12.7-liter Scania diesel engine and a 4-cylinder 2.0-liter Volvo petrol engine, are used as case studies where the generic simulation platform is parameterized and validated against experimental data for both engines.

Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire.A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed. Dynamic tire simulations were conducted using a heat transfer model, considering road, ambient, shoulder, and apex temperatures. The simulation results were compared with measured data for ambient, shoulder, and apex temperatures, and the model captures both time constants and stationary levels. The resulting model can predict the dynamics of the rolling resistance and will, therefore, give a more accurate prediction when tires are cold and warming up. Driving range simulations of a long haulage battery-electric truck have also been conducted demonstrating how the range changes with varying ambient temperatures as well as the influence a snapshot consumption has on range estimation.

The potential of additional steering possibilities (like rear-wheel or all-wheel steering) is analyzed for critical situations to investigate possible safety improvements. For this purpose, a dynamic optimization problem is formulated to find the best possible maneuver. The optimization criterion is to maximize the entry speed into a constant radius -curve. The optimization problem is solved for different steering topologies, and the results quantify the increase in maximum entry speed, highlighting the potential for safety improvements. Further, the optimal steering strategies are determined, and they show interesting characteristics like initial diagonal driving or, in other cases, a transition from initial out-of-phase steering to in-phase steering.