Abstract
The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.

Abstract
Bidirectional and efficient on-board charger systems for electric vehicles is a challenge nowadays. This paper describes a novel power converter system that implements bidirectional flow between the grid and an electric vehicle battery, where a dual active bridge is advantageous. With a bidirectional topology and proper control all the major grid constraints, such as power quality, harmonic rejection, active and reactive power control, and others, can be easily satisfied. A hardware prototype of the power converter is built. Experimental results from this hardware prototype verify the preliminary operation and claims of the V2G power interface.

Abstract
The Smart Vehicle-to-Grid Project at INESC TEC is currently studying the application of matrix converters to implement an isolated bidirectional AC-DC power converter using a single power conversion stage to provide a high-frequency link between the grid and vehicle. The single-stage structure and bidirectional power flow make the matrix converter an attractive solution for the charging applications of electric vehicles. A very brief overview of the matrix converter and its modulation strategy is presented, followed by detailed analysis. The power conversion system performance is investigated in terms of the switching commutation, input filter and input power factor. Simulations and experimental results of a prototype are also presented to further validate the proposed topology and operating principle.

Abstract
The scope of this work is to find the best approach to control advanced inverters used to connect electric vehicles to the grid. Phase-locked Loop (PLL) is a grid voltage phase detection that makes use of an orthogonal voltage to lock the grid phase. This method is suitable for both single and three phase systems, although in single-phase, because they have less information, more advanced systems are required. The easiest way to obtain the orthogonal voltage system is using a transport delay block to introduce a phase shift of 90 degrees with respect to the fundamental frequency of the grid voltage. This method is known as Synchronous Reference Frame PLL (SRF-PLL). The use of inverse Park transformation is also possible. To lower the complexity and increasing the filtering of the output signals, methods using adaptive filters are a good alternative. For this approach, the use of a second order generalized integrator (SOGI) or Adaptive Notch filter combined with PLL, Enhanced PLL (EPLL) and Quadrature PLL (QPLL), leads to satisfactory results. © 2011 INESC Coimbra.

Abstract

Abstract
This paper presents a new strategy for recharging electric vehicles in residential buildings. The proposed approach minimizes the difference between desired and final state of charge (SOC) by the end of the charging period, by adjusting the charging power for each vehicle in real-time. A non-linear optimization problem is formulated, considering the initial and final SOC, as well as available charging time, and total available power. Results were compared to a baseline and show that the proposed solution outperforms the currently most used nonoptimized method, particularly in high demand scenarios, where we achieve values of 9.3% of curtailed range when compared with the non-optimized methodology.