Smart Procurement Of Naturally Generated Energy (SPONGE) for PHEV's

In this paper we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of PHEV's to absorb oncoming energy in an smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO is used to show simple simulations to support the efficacy of the proposed idea.Comment: Updated typos with respect to previous versio

Filtraggio e stima dello stato nei sistemi dinamici non lineari

In questa tesi viene mostrato un metodo per la stima dello stato nei sistemi dinamici non lineari e non gaussiani. La tecnica principale mostrata è il filtro a particelle. Viene mostrato un metodo di ricampionamento alternativo basato sul concetto di massima entropia e vengono presentate diverse simulazioni a supporto di tale idea

Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

Generalised Entropy of Curves for the Analysis and Classification of Dynamical Systems

This paper provides a new approach for the analysis and eventually the classification of dynamical systems. The objective is pursued by extending the concept of the entropy of plane curves, first introduced within the theory of the thermodynamics of plane curves, to Rn space. Such a generalised entropy of a curve is used to evaluate curves that are obtained by connecting several points in the phase space. As the points change their coordinates according to the equations of a dynamical system, the entropy of the curve connecting them is used to infer the behaviour of the underlying dynamics. According to the proposed method all linear dynamical systems evolve at constant zero entropy, while higher asymptotic values characterise nonlinear systems. The approach proves to be particularly efficient when applied to chaotic systems, in which case it has common features with other classic approaches. Performances of the proposed method are tested over several benchmark problems

An Assessment on the Use of Stationary Vehicles as a Support to Cooperative Positioning

In this paper, we consider the use of stationary vehicles as tools to enhance the localisation capabilities of moving vehicles in a VANET. We examine the idea in terms of its potential benefits, technical requirements, algorithmic design and experimental evaluation. Simulation results are given to illustrate the efficacy of the technique.Comment: This version of the paper is an updated version of the initial submission, where some initial comments of reviewers have been taken into accoun

A Multi-Objective Method for Short-Term Load Forecasting in European Countries

In this paper we present a novel method for daily short-term load forecasting, belonging to the class of “similar shape” algorithms. In the proposed method, a number of parameters are optimally tuned via a multi-objective strategy that minimizes the error and the variance of the error, with the objective of providing a final forecast that is at the same time accurate and reliable. We extensively compare our algorithm with other state-of-the-art methods. In particular, we apply our approach upon publicly available data and show that the same algorithm accurately forecasts the load of countries characterized by different size, different weather conditions, and generally different electrical load profiles, in an unsupervised manner

Comparison and clustering analysis of the daily electrical load in eight European countries

This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week. In particular, popular clustering algorithms like the Fuzzy c-Means, Spectral Clustering and Expectation Maximization are compared, and it is shown that the best results are obtained if the daily data are compressed with respect to a single feature, namely the so-called “Morning Slope”. Such a feature-based clustering appears to outperform the clustering results obtained upon using other classic features, and also with respect to using other conventional compression methods, such as the Principal Component Analysis, in all the examined European countries. This result is particularly interesting, as this feature provides a direct physical interpretation that can be used to obtain insights on the structure of the daily load profiles

A Markov-chain based model for a bike-sharing system

Bike-sharing systems are recently becoming ubiquitous in most cities, as an environmentally friendly alternative to other means of transportation. An optimal management of the bike-sharing service would in principle benefit from the availability of a mathematical model underlying the system. Accordingly, in this paper we propose a Markov-chain based approach to model the bike-sharing system, which we believe has a potential to develop alternative methods to implement classic control actions in a bike-sharing system (e.g., in terms of implementing alternative relocation strategies or planning advertising campaigns). The proposed methodology is validated on real data from the bike-sharing system in Boston, USA, and a first application of the proposed model is preliminarily illustrated in the paper