Abstract

In automotive electromagnetic (EM) compatibility (EMC), the cable bundle network study is of great importance. Indeed, a cable network links all the electronic equipment interfaces included the critical ones and consequently can be assimilated both to a reception antenna and to an emission antenna at the same time. On the one end, as far as immunity problem is concerned, where an EM perturbation illuminates the car, the cable network acts as a receiving antenna able to induce and propagate interference currents until the electronic equipment interfaces and potentially induce dysfunction or in the worst case destruction of the equipment. At low frequency, the interference signal propagating on the cable network is generally considered as more significant than the direct coupling between the incident field and the equipment. On the other end, as far as emission problem is concerned, the EM field emitted by the cable network may disturb itself the electronic equipments by direct coupling. To avoid these problems, automotive manufacturers have to perform normative tests before selling vehicles. These tests are applied on electronic equipments outside and inside the car first to verify that the equipments are not disturbed by an EM perturbation of given magnitude and second to ensure that the EM emission of each equipment does not exceed a limit value at a given distance. Obviously, these tests are not exhaustive and fully representative of real conditions. For example, in immunity tests, two polarizations (vertical and horizontal polarizations) of the EM perturbation are generally tested in free space conditions. In reality, the EM perturbation due for example to a mobile phone outside the car could happen from any direction of space and be reflected by all the scattering objects located in the close environment of the vehicle (ground, other vehicles, buildings,...). Consequently, the contribution of EM modelling is a great tool for automotive manufacturers in order to proceed to numerical normative, additional and also parametric tests at early stages of the car development on numerical models and for a reasonable cost. Moreover, numerical modelling will reduce the number of prototypes built during the Document type: Part of book or chapter of book

Abstract

This paper presents a model predictive current control applied to a slow electric vehicle (EV) battery charger. Taking into account the similarities between the power converters inside the EV, it is possible to combine the battery charger and the motor driver in a single integrated converter, thus reducing the weight and volume of the proposed solution, and also contributing to reduce the final price of the EV. Due to the bidirectional power flow capability of the integrated power converter, when working as a slow EV battery charger it can operate in grid-to-vehicle (G2V) mode and in vehicle-to-grid (V2G) mode, contributing to make EVs an important assets in the future smart grids. The integrated power converter working as battery charger operates with sinusoidal current and unitary power factor, contributing to improve the power quality of the electrical grid. This paper provides simulation and experimental results that validate the model predictive control algorithm applied to the proposed integrated power converter working as slow EV battery charger. (undefined) info:eu-repo/semantics/publishedVersion Document type: Part of book or chapter of book

Abstract

Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process. Document type: Part of book or chapter of book

Abstract

The recent and massive investments in Electric Vehicles (EVs) reveal a change of paradigm in the transports sector and the proliferation of EVs will contribute to an effective reduction in the emissions of greenhouse gases. Nevertheless, for the electrical power grids EVs will be extra loads, which will require the demand energy to charge their batteries. With the advent of the Smart Grids, besides the usual battery charging mode (Grid-to-Vehicle – G2V), where the batteries receives energy from the power grid, arises a new concept for the users of EVs and for the power grid market, denominated as Vehicle-to-Grid (V2G). In the V2G operation mode, EVs return to the power grid part of the energy stored in their batteries. The V2G concept requires the use of battery chargers for the EVs with bidirectional power flow capability and bidirectional communication with the Smart Grids through Information and Communication Technology (ICT) applications. It is important to highlight that the proliferation of EVs and the impact of their battery chargers on the power grid quality is a matter of concern, since conventional chargers present current harmonics and power factor problems. In this paper it is presented the preliminary studies resulting from a PhD work about a bidirectional battery charger for EVs, which was designed to operate in collaboration with the power grid as G2V and V2G through an ICT application. In this way, it is expectable to contribute to the technological innovation of the electric mobility in Smart Grids. To assess the behavior of the proposed battery charger under different scenarios of operation, a prototype has been developed, and some simulation and experimental results of the battery charger are presented. Fundação para a Ciência e a Tecnologia (FCT); FEDER Funds, through the Operational Programme for Competitiveness Factors – COMPETE, and by National Funds through FCT – Foun- dation for Science and Technology of Portugal, under the projects: FCOMP-01-0124- FEDER-022674 an d PTDC/EEA-EEL/104569/2008 Document type: Part of book or chapter of book

Abstract

Part 17: Power Conversion; International audience; This work is focused on the design of the contactless energy transmitters and testing of their electrical parameters, varying the working frequency in the kHz range. The intended application is related to the possibility to make most efficient wireless charging of different batteries from the grid, guaranteeing more acceptable use of the electric vehicles. Document type: Part of book or chapter of book

Abstract

Internal inspection of pipelines is an important tool for ensuring safe and reliable delivery of fossil energy products. Current inspection systems that are propelled through the pipeline by the product flow cannot be used to inspect all pipelines because of the various physical barriers they encounter. Recent development efforts include a new generation of powered inspection platforms that crawl slowly inside a pipeline and are able to maneuver past the physical barriers that can limit inspection. At Battelle, innovative electromagnetic sensors are being designed and tested for these new pipeline crawlers. The various sensor types can be used to assess a wide range of pipeline anomalies including corrosion, mechanical damage, and cracks. The Applied Energy Systems Group at Battelle is concluding the first year of work on a projected three-year development effort. In this first year, two innovative electromagnetic inspection technologies were designed and tested. Both were based on moving high-strength permanent magnets to generate inspection energy. One system involved translating permanent magnets towards the pipe. A pulse of electric current would be induced in the pipe to oppose the magnetization according to Lenz's Law. The decay of this pulse would indicate the presence of defects in the pipe wall. This inspection method is similar to pulsed eddy current inspection methods, with the fundamental difference being the manner in which the current is generated. Details of this development effort were reported in the first semiannual report on this project. This second semiannual report focuses on the development of a second inspection methodology, based on rotating permanent magnets. During this period, a rotating permanent magnet exciter was designed and built. The exciter unit produces strong eddy currents in the pipe wall. The tests have shown that at distances of a pipe diameter or more, the currents flow circumferentially, and that these circumferential currents are deflected by pipeline defects such as corrosion and axially aligned cracks. Simple sensors are used to detect the change in current densities in the pipe wall. Document type: Report

Abstract

Studies have estimated that irregular operations (flights affected by a disruption) can cost between 2% and 3% of the airline annual revenue and that a better recovery process could result in cost reductions of at least 20%. Even for small airlines this can represent millions of Euros. In this paper we propose a multi-agent system (MAS) whose members represent the roles, functionalities and competences existing in a typical Airline Operations Control Centre (AOCC), the airline entity responsible for managing the impact of irregular events on planned operations. This multiagent based system produces intelligent solutions in the sense that its outcomes are the result of an autonomous reaction and adaption to changes in the environment, solving partial problems simultaneously. We tested our MAS using real data from TAP Portuguese airline company and experimentally compared our system with solutions found by the human operators on TAP Portugal AOCC. A comparison was also made with a more traditional sequential approach that is the typical method followed by AOCCs when solving disruptions. Results from those comparisons show that it is possible to reduce costs and have a better integrated solution with the proposed system.

Abstract

Flywheel systems are attractive in hybrid and electric vehicles due to their ability to handle power during acceleration and braking. The combination of a flywheel device with a battery source has several advantages such as high peak power capacity, high energy density and reduction in the number of charge/discharge cycles of the battery. A flywheel based all-electric driveline is investigated in this thesis. The novelty of the system consists in the use of a double wound flywheel machine, which divides the system in two different power levels. Due to this configuration, the system becomes efficient and can handle the power developed during fast dynamical processes. The complete driveline consists of three main components: the battery, the flywheel machine and the wheel motor. The High-Power (HP) side of the driveline connects the flywheel machine to the wheel motor, whereas the Low-Power (LP) side connects the flywheel machine to the battery. The connections of different components of the system are made electrically through power converter devices. The present thesis focuses on the electrical converters and control strategies used in the flywheel based all-electric driveline. The control of power converters is responsible for the logic and functionality of the driveline, being a challenging step within this project. Different power converter topologies have been investigated: a DC/DC plus a DC/AC converter on the LP side, and an AC/DC/AC converter on the HP side. The design and assembly of the power electronics and their control scheme have been successfully implemented. Different control strategies have been suggested and a complete scaled driveline has been assembled and tested based on previous simulation results. Results have confirmed the functionality of the driveline, where smoothed output power has been obtained from the battery, whereas the flywheel handles power transients on the traction side. An average efficiency of about 87% (battery to wheels) has been obtained. The power converter systems have been shown to be efficient and robust, with control strategies able to handle the peak energy flow in the system. A regenerative braking strategy has been simulated and a wheel-to-wheel efficiency of about 80% has been estimated.

Abstract

In view of the success of machine learning based prediction algorithms in the recent years, in this study, we have employed a selection of these algorithms on some time series prediction problems in the context of smart grid. We have used real world data from the UCLA campus solar PV panels and parking lots. In the process of applying these algorithms on the Electric Vehicle (EV) charging load prediction problem, two new prediction algorithms have been proposed, namely Modified Pattern Sequence Forecasting (MPSF) and Time Weighted Dot Product Nearest Neighbor (TWDP NN). One of the objectives when predicting the EV charging load is speed of prediction since it is intended to be used in a real time application (smartphone application for EV customers). Using our dataset, TWDP NN decreased the processing time by a third. As missing data is a significant concern in real world data, the effect of missing values on the prediction quality has been investigated. Six different imputation methods have been applied to compensate for missing values in EV charging data. Based on non-parametric statistical tests, suitable (or unsuitable) imputation methods for each prediction algorithm are recommended. Forecasting of the Electric Vehicle (EV) charging load can be done based on two different datasets: data from the customer profile (charging record) and data from outlet measurements (station record). We found that charging records provide relatively faster prediction while putting customer privacy at jeopardy. On the other hand, station records provide relatively slower prediction while respecting the customer privacy. In general, both datasets generate comparable prediction error. Forecasting solar power generation with application on real-time control of energy system has also been investigated. Since predictions are made on every minute for one minute ahead values, the designed system has to be rapidly responsive. This has been pursued by: first, we have solely relied on past values of solar power data (rather than external data), hence lowering the volume of input data; second, the investigated algorithms are capable of generating predictions in less than a second. The results show that kNN and SVR show lower error.