Reducing the fuel consumption of today's vehicle fleet is of great importance due to the environmental impact of using fossil-based fuels. The turbocharged compression ignition (CI) engine is widely used for trucks. The CI engine efficiency is dependent on the operating point, in terms of rotational speed and load. The selection of load point can be controlled by selecting suitable gears, but remains a challenging task during dynamic driving, due to the turbocharger dynamics which introduces a lag in the system. Electric turbocharger technologies can improve the engine response time, but developing efficient control strategies can be challenging. Due to turbocharger lag, all conditions that are reachable in stationary operation for the turbocharged CI engine are not always reachable during dynamic events, for example after an up-shift where the engine speed and torque demand changes rapidly.  

In this work the fuel saving potential of electric turbocharging for a heavy-duty truck performing a long-haulage driving mission is investigated.  An electric turbocharger control strategy is proposed and evaluated. The results show that the fuel consumption can be reduced using the electric turbocharger, when comparing to a conventional turbocharged CI truck performing a long-haulage driving mission.

A turbocharged CI engine model suitable for optimal control of transient behavior is developed. Sub-models are validated using data describing the components, and the model suitability for optimal control is shown with a tip-in example. To increase the model accuracy, the torque model is extended with a further dependence on the air-fuel ratio and operating point dependent losses. The complete engine model is parameterized for a set of stationary load points. The model is validated using data from a dynamic engine test, where it is shown that both the stationary and dynamic features in the data is represented well by the model. The developed engine model is used as a foundation in an optimal control problem setup to solve fuel optimal accelerations including gear changes. The setup is used to investigate the impact of driveshaft flexibility on the optimal control results, when compared to a stiff driveshaft model. Apart from a slight increase in fuel consumption, the driveshaft flexibility is shown to have minor effects on the fuel optimal control signals, in terms of general torque output and gear shift characteristics. 

The hybrid electric vehicle (HEV) technology can potentially reduce the consumption of diesel fuel, but how to design and control the system, consisting of several degrees of freedom remains a challenging task. Energy optimal accelerations of a CI parallel HEV with electric turbocharger is investigated using the optimal control problem setup. The results show that the electric turbocharger is used when the electrical energy cost is high, and the usage of the crank shaft motor is increasing with decreasing electric energy cost. 

To summarize, the developed models and problem setups enable investigations of different powertrain configurations and optimal control of these. One conclusion is that the energy savings using an electric turbocharger and crank shaft motor during accelerations are significant.

Transport is an integral part of society and one of its basic prerequisites. Society is now facing a transition as it must go from dependence on fossil fuels to sustainability. Despite large investments by the vehicle manufacturers, the transition needs to be accelerated for the two-degree (Celsius) target to be reached, which requires new innovations and solutions. 

The development of computers has led to efficient software being available today to numerically solve optimization problems, which enables mathematical modeling and optimization as a systematic problem-solving method. However, taking advantage of the numerical solvers requires specialized knowledge and is a barrier for many engineers. To overcome this and make the problem-solving methodology available, tools that bridge the gap between the engineer’s problem and the numerical solvers are needed. 

The dissertation covers the complete chain from problem to solution, with methods and tools that support the problem-solving process. Software for optimal control is investigated with the aim of making the numerical solvers available to the user. The result is a design based on the introduction of a domain-specific programming language. It makes it possible to automatically reformulate the user’s problem into a form that the computer can handle, while making the program more user-friendly by reducing the difference between the problem domain and the computer’s domain. The result has been developed together with the software Yop, which is used by engineers and researchers nationally and internationally to solve control engineering problems, in academia as well as in industry. 

The software is used to investigate whether an electrified powertrain can be made more efficient by equipping the diesel engine with a larger and more efficient turbocharger, at the expense of increased inertia. The result indicates a gain and that the increased inertia can be compensated by the electric motor. As part of the work, a diesel engine model has been developed, where it has been investigated how relevant effects for turbocharger selection can be included in a way suitable for optimal control. The result is a validated and dynamic diesel engine model that has been made available to the research community through publications and open-source code.