Abstract
This paper analyzes the implementation of new technologies in network industries through the development of a suitable regulatory scheme. The analysis focuses on Smart Grid (SG) technologies which, among others benefits, could save operational costs and reduce the need for further conventional investments in the grid. In spite of the benefits that may result from their implementation, the adoption of SGs by network operators can be hampered by the uncertainties surrounding actual performances. A decision model has been developed to assess the firms' incentives to invest in "smart" technologies under different regulatory schemes. The model also enables testing the impact of uncertainties on the reduction of operational costs, and of conventional investments. Under certain circumstances, it may be justified to support the development and early deployment of emerging innovations that have a high potential to ameliorate the efficiency of the electricity system, but whose adoption faces many uncertainties.

Abstract
The design of efficient and high power density electrical machines needs an accurate characterization of magnetic phenomena. Core losses estimation is usually addressed by empirical models, where its lack of accuracy is well known. Hysteresis models are able to take an insight into the magnetic physical mechanisms. Compared to the empirical models, they contribute to a higher accuracy in modeling electromagnetic systems, including core losses estimation. At a macroscopic level, two models are often used: the Preisach and the Jiles-Atherton (J-A) models. This paper presents their basic formulation, as well the main limitations and scope of application. This is a first step to investigate the possible application of hysteresis models, in order to reach accurate core losses estimation in switched reluctance machines.

Abstract
Unknown input observers (UIO) can be used in the model-based fault diagnosis (FD) system to reduce or eliminate the effect of unknown disturbances present on the process and used to create a set of residuals that are decoupled and sensitive to faults. In this work, a new FD scheme of the In-Wheel motors electric vehicle (IWM-EV) with active front steering was carried out, as well as the design of the fault isolation banks of UIOs. These banks are used to generate residuals that are robust againts to noise and are sensitive to only one fault. This way the faults in the steering or in-wheel actuator are detected and isolated with a higher rate of accuracy. The proposed FD scheme is verified by Carsim® and Matlab/Simulink® cosimulation. © 2017 Taylor & Francis Group, London.

Abstract
This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and super capacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

Abstract
This work is focused on the development of system able to keep tracking driver's behavior without a black box device mounted inside the car. Firstly, we intend to explore the data from GPS (Global Positioning System), accelerometer, gyroscope and magnetometer for a full characterization of the vehicle dynamics. Secondly, we develop an event detector that determines and classifies distinct kind of maneuvers, like turns, lane change, U-turns, among others. Finally, we developed a simple aggressiveness classifier using fuzzy logic. Experiments have been conducted and the initial results of the system were found to be encouraging on the implementation of a non-intrusive system for driver analysis.

Abstract
Recently, model-based approaches have been proposed for fault diagnosis as an emerging alternative to traditional techniques. Particularly, the inversion-and observer-based were proposed for fault diagnosis as successful model-based approaches. However, the research's results related to these approaches are reduced and generally applied to a specific converter or control type. Therefore, the inversion-and observer-based approaches performance presented in the literature does not permit to compare both techniques in order to conclude which one is more suitable for fault diagnosis. In this context, this paper presents a comparative study of inversion-and observer based approaches for fault diagnosis in a DC-DC boost converter. More specifically, after modeling both the power converter and the fault detection and isolation methods, it were inserted faults on the transistor and capacitor (degradation) in order to validate the performance of the proposed approaches. The results demonstrate that these are efficient alternatives to the conventional techniques that are usually used for fault diagnosis in power electronics systems. Both inverse-and observer-based approaches showed similarities and effectiveness in detecting and isolating faults on the studied DC-DC boost converter.