Usage Driven Design of Power System and Multi-criteria Route Planning for Eco-Urban Electric Cars

Eco-urban electric cars (EC) are superior to conventional cars in terms of the operation cost and carbon footprint. However, the performance of EC in terms of their maximum speed and power, initial and maintenance cost and reliability in the available power is lower than conventional cars. The reliability in available energy can be viewed as the main concerns when comparing EC to conventional cars. Reliability in available energy is highly dependent upon the efficiency of the power system as well as the type and size of batteries. Type and size of batteries have a significant effect on the maintenance cost as well as the initial cost. This thesis is focused on two aspects of the research in electric cars, namely, (i) selection and size optimisation of components, and (ii) improving the reliability of the available energy. Traditionally, a robust design approach is adopted in design of the power system of cars. This is mainly aimed at providing the user with the luxury of using the car wherever there is a suitable road and whenever they want to use the car. This flexibility, however, comes with the price of heavier and more expensive power systems. By incorporating data on the dominant usage of an EC and adopting a deterministic design and optimisation method more cost-effective power systems, more compatible with the usage can be obtained. In this study, a power system simulation tool is developed. Using the simulation tool, the performance of the power system components can be analysed for different usage scenarios. Case studies are conducted. Each case is based on a dominant usage defined for a two-person EC driven in Kayseri city in Turkey. For each case, the best power system configuration is obtained. Another original contribution of this thesis is in the context of the reliability of the available energy, by providing a decision support system - a route planning advisor - that helps the user to select the most suitable route in terms of a variety of criteria both conventional, such as travelling time and travelling distance, as well as EC-related such as, available power, vicinity to a charging station. The optimiser of the developed multi criteria route planning advisor (MCRPA) tool is based on a robust hybrid Dijkstra - A* - NSGA-II algorithm. MCRPA incorporates information on EC characteristics (such as power system, aerodynamic shape, weight), city characteristics (current traffic flows, road types, speed limits, altitude, whether conditions), and city charging stations characteristics (capacity, charging level, crowding density). Carrying out case studies, the efficiency and performance of the MCRPA is evaluated

Algorithmic Foundations of Heuristic Search using Higher-Order Polygon Inequalities

The shortest path problem in graphs is both a classic combinatorial optimization problem and a practical problem that admits many applications. Techniques for preprocessing a graph are useful for reducing shortest path query times. This dissertation studies the foundations of a class of algorithms that use preprocessed landmark information and the triangle inequality to guide A* search in graphs. A new heuristic is presented for solving shortest path queries that enables the use of higher order polygon inequalities. We demonstrate this capability by leveraging distance information from two landmarks when visiting a vertex as opposed to the common single landmark paradigm. The new heuristic’s novel feature is that it computes and stores a reduced amount of preprocessed information (in comparison to previous landmark-based algorithms) while enabling more informed search decisions. We demonstrate that domination of this heuristic over its predecessor depends on landmark selection and that, in general, the denser the landmark set, the better heuristic performs. Due to the reduced memory requirement, this new heuristic admits much denser landmark sets. We conduct experiments to characterize the impact that landmark configurations have on this new heuristic, demonstrating that centrality-based landmark selection has the best tradeoff between preprocessing and runtime. Using a developed graph library and static information from benchmark road map datasets, the algorithm is compared experimentally with previous landmark-based shortest path techniques in a fixed-memory environment to demonstrate a reduction in overall computational time and memory requirements. Experimental results are evaluated to detail the significance of landmark selection and density, the tradeoffs of performing preprocessing, and the practical use cases of the algorithm