The paper presents a summary of different double second-order generalized integrator (DSOGI)-based phase-locked loop (PLL) algorithms for synchronization with three-phase weak grids. The different methods are compared through simulation under a variety of grid conditions, such as unbalanced phase voltages, high low-order harmonics distortion, frequency steps, phase jumps, and voltage sags. Following the simulation results, the three methods that have shown the overall best results are compared through an experimental setup for further results validation under operation with a voltage-source converter. Based on the obtained results, a benchmark table is presented that allows ranking the performance of the tested methods for different expected grid conditions.

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

Battery State of Charge (SOC) evaluation in electrical vehicles (EVs) environment is a key point for widely acceptance of this new technology. As SOC cannot be measured it is imperative to use some method that can accurately make its estimation using measurable variables, such as battery terminal voltage and current. As Kalman Filters are commonly accepted as the best estimator for linear systems, and a good one for non-linear ones, it is expected that they can be successfully applied to battery SOC estimation. So, in this paper, it is presented an Extended Kalman Filter (EKF) based technique for SOC estimation. As EKF needs a process model it is used a new one-dimensional, physics based, battery model that solves Fick's diffusion equations subjected to its boundary conditions. Used Batteries are Lithium-polymer ones due to its promising application as EVs energy source. A set of experiments, with different current profiles, were made and its results confronted with simulated ones enabling to conclude about the quality of proposed battery model and EKF SOC estimation.