Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

Numerical and physical modelling of axially loaded piles for offshore wind turbines

The scope of the thesis is to broaden the knowledge about axially loaded pipe piles, that can play as foundations for offshore wind turbines based on jacket structures. The goal of the work was pursued by interpreting experimental data on large-scale model piles and by developing numerical tools for the prediction of their monotonic response to tensile and compressive loads to failure. The availability of experimental results on large scale model piles produced in two different campaigns at Fraunhofer IWES (Hannover, Germany) represented the reference for the whole work. Data from CPTs, blow counts during installation and load-displacement curves allowed to develop considerations on the experimental results and comparison with empirical methods from literature, such as CPT-based methods and Load Transfer methods. The understanding of soil-structure interaction mechanisms has been involved in the study in order to better assess the mechanical response of the sand with the scope to help in developing predictive tools of the experiments. A lack of information on the response of Rohsand 3152 when in contact with steel was highlighted, so the necessity of better assessing its response was fulfilled with a comprehensive campaign of interface shear test. It was found how the response of the sand to ultimate conditions evolve with the roughness of the steel, which is a precious information to take account of when attempting the prediction of a pile capacity. Parallel to this topic, the work has developed a numerical modelling procedure that was validated on the available large-scale model piles at IWES. The modelling strategy is intended to build a FE model whose mechanical properties of the sand come from an interpretation of commonly available geotechnical tests. The results of the FE model were compared with other predictive tools currently used in the engineering practice.