Abstract
The basic idea of this work is to develop and test a control system for a solid-state transformer, interconnecting two distribution grids with also having the possibility of generating an isolated microgrid from the medium voltage grid. The system is modular and is easily adaptable to any power and voltage level, with different controllers for each subsystem. The system is assessed, through several MATLAB/Simulink simulations, for various operating points. The system is bidirectional and resilient to failures, being able to mitigate network anomalies, namely voltage and harmonic sags. When operating as an isolated microgrid, it can feed linear, non-linear, or unbalanced loads. © 2021 IEEE.

Abstract
Electric forklifts are extremely important for the world's logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best model is intended for a state of charge estimation on a battery applied on an electric forklift. The nonlinear grey box model with the nonlinear least square method presented a better result for this purpose. This model was estimated with the best experiment design which was defined considering the fit to validation data, the parameter standard deviation and the output variance. With this approach, it was possible to reach more than 80% of fit in different validation data, a non-biased and little prediction error and a good one-step ahead result.

Abstract
Li-ion battery State-of-Charge (SOC) estimation is a complex challenge for battery management systems designers, due to the battery's non-linear behaviour at different operating conditions and ageing levels. As a possible solution, multiple machine learning models have been proposed for SOC estimation throughout the years. These provide an advantage over model-based methods, as they do not require a deep knowledge and study of the battery's internal behaviour. However, many of these proposed models could not be considered due to their complexity. The high number of required stored parameters and/or elevated memory consumption during estimation may pose challenges to the application of these methods. Therefore, in this paper, several feedforward neural network models are proposed for SOC estimation, with an efficient method for online input preprocessing and low parameter requirement in storage. These models are simulated and validated using battery data, taken at different temperatures with several driving cycles and charge cycles, achieving lowest estimation Root Mean Squared Error (RMSE) of 1.096% over the whole validation dataset.

Abstract
To stimulate research in the area of automotive electronics and electric vehicles, the IEEE Vehicular Technology Society (VTS) initiated the Motor Vehicles Challenge. The objective of the 2021 edition of this challenge is to provide a benchmark problem for the energy management of a dual-motor electric vehicle. To solve this, we propose a pragmatic optimization-based energy management system (EMS) that minimizes the instantaneous power consumption of the vehicle through manipulation of torque distribution ratios among the electric motors. Numerical results obtained with the VTS benchmark simulation model demonstrate that the proposed EMS can extend the vehicle range up to 3% when compared to baseline solutions.

Abstract

Abstract