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.

Abstract

Abstract
This article addresses the developments ongoing in SENSIBLE, an H2020 funded project focused on energy storage and energy management, which demonstration occurs in Évora-Portugal, Nottingham, UK and Nuremberg, Germany. Currently, the presented study focuses on the concepts and developments necessary in order to make possible that residential clients can participate in a market environment with their electrical flexibility, also considering distribution system operator (DSO) needs when gird is under stress caused by any technical constraint. Moreover, than the concept behind it is necessary to consider several developments: (i) a low layer where residential assets will live in customer’s houses; (ii) a high-level layers where market tools and DSO management tools will live; (iii) an intermediate layer, which bridge the gap between the low layer and high layer. These developments are a result of the ongoing works under one of SENSIBLE use cases which demonstrations occurs in a small village in Évora district in Portugal. © 2017 Institution of Engineering and Technology. All rights reserved.

Abstract
This paper presents an algorithm developed for the optimization of Low Voltage (LV) grids that takes advantage of Distributed Energy Resources (DER) such as storage devices and flexible loads. The proposed approach is based on a multi-temporal Optimal Power Flow (OPF) algorithm that feeds from forecasting tools for load and renewable generation, which means the optimization model looks at a 24-hours horizon with hourly resolution. Specific constraints to the OPF are added to adequately model storage devices, namely their State-of-Charge (SOC) limits as well as their charging and discharging efficiencies. Moreover, a full three-phase model was built due to the unbalanced nature of LV grids motivated by the presence of single-phase load and generation. The algorithm developed has been extensively tested through simulation using a real LV Portuguese network data to illustrate the performance of the algorithm in different scenarios with good results.