Abstract
Hybrid balancing is a recently-proposed class of battery balancing systems that simultaneously provide capacity and thermal equalization, while enabling hybridization with supercapacitors. This integration of functions poses a challenging control problem, requiring the fulfillment of multiple objectives (e.g., reduction of charge and temperature imbalances, energy losses and battery stress) and the coordination of a large number of power converters. To tackle this challenge, we propose a multi-layer model predictive control (MPC) framework, which splits the control tasks into two layers. The first layer uses long prediction horizons and a simplified model of the energy storage system to compute the state-of-charge reference for the supercapacitors. The second layer uses module-level models of the battery pack to track this reference, while minimizing charge and temperature imbalances with a small prediction horizon. Simulation results demonstrate that the multi-layer MPC provides similar performance as single-layer MPC, but at a fraction of the computational effort.

Abstract
Agriculture plays a significant role in the labor force and GDP of Mozambique. Nonetheless, the energy source massively used for water pumping in irrigation purposes is based on fossil fuels (diesel oil). Despite the water availability and fertile soils in Moamba, Mozambique, farmers struggle with the high cost of fuels used in the pumping systems. This study was sought to analyze the feasibility of utilizing a solar photovoltaic system as a means to reduce the environmental impact caused by the diesel pumps and simultaneously alleviate the expenses regarding the use of non-environmentally friendly technologies. Site observations and interviews were undertaken in order to obtain local data regarding the water demand, current energy systems costs and distances from the source to the irrigated fields. CLIMWAT 2.0 was used for climate data acquisition and analysis. The environmental benefits, the cost effectiveness and local climate conditions show that the PV system is feasible in Moamba. Furthermore, parameters such as hydraulic energy, incident solar energy, pump efficiency and total system efficiency were used to predict the performance of the system. The results obtained are important to analyze the implementation of such energy systems.

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.