Abstract
In a design of a switched reluctance machine, there are a number of parameters that are chosen empirically inside a certain interval, therefore to find an optimum geometry it is necessary to determine how each parameter acts on the performance of the machine. This work presents a study on the influence of geometric dimensions on the performance of the switched reluctance machine. The analysis is done through finite element simulations based on the variation of one parameter while the others are fixed. Graphical and numerical results of torque and magnetic flux are presented for a 6/4 three-phase machine and an 8/6 four-phase machine. The study presented aims to provide consistent data on which dimensions should be modified for specific applications, and thus to base choices made in the design and optimization stage.

Abstract
Performance of series connected batteries is limited by the "weakest link" effect, i.e., the cell or group of cells with the poorest performance in terms of temperature, power, or energy characteristics. To mitigate the "weakest link" effect, this study deals with the design, modeling, and experimental demonstration of a smart and hybrid balancing system (SHBS). A cell-to-cell shared energy transfer configuration is proposed, including a supercapacitor bank in the balancing bus, thus enabling hybridization. Energy is transferred from each battery module connected in series to the balancing bus, and vice-versa, by means of low-cost bi-directional dc-dc converters. The current setpoints of the converters are obtained by means of a smart balancing control strategy, implemented using convex optimization. The strategy is called "smart" because it pursues goals beyond the conventional state-of-charge equalization, including temperature and power capability equalization, and minimization of energy losses. Simulations show that the proposed SHBS is able to achieve all these goals effectively in an e-mobility application and are also used to assess the impact of different hybridization ratios and cooling conditions. Finally, an experimental setup is developed to demonstrate the feasibility of the SHBS.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, modeling this machine is a most demanding task. While magnetic saturation is often addressed, hysteresis effect is usually disregarded. In order to include this phenomenon, an SRM drive simulation model was built, where magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. SRM losses estimation is a challenging task, which demands continuous research efforts. This paper intends to investigate hysteresis impact on SRM copper losses. Due to the machine features, skin and proximity effects are considered. Different steady-state operation scenarios are simulated and compared.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Most SRM models address magnetic saturation, but hysteresis effect is usually disregarded. This paper is based on a developed four-quadrant SRM drive simulation model, where magnetization characteristics are generated through the original Jiles-Atherton (J-A) hysteresis model. The main goal is to investigate the hysteresis impact on the SRM drive controller performance. Steady-state operation scenarios are simulated and compared. For the adopted current control strategy (PWM), the results show a significant impact in all drive components, particularly for low speed with high load operation.

Abstract
This work focuses on hybrid balancing systems, a recently-proposed concept that enables balancing of battery cells and hybridization with supercapacitors. To control this system, a model predictive control framework is developed. In addition to distributing the supercapacitor power among the balancing circuits, this framework is also able to minimize state-of-charge and thermal imbalances in the battery cells, as well as energy losses in the balancing circuits. The effectiveness of the proposed approach is verified via numerical simulations. It is shown that, in comparison with state-of-art balancing solutions, the proposed control approach is able to decrease battery stress in up to 9% and the maximum temperature in up to 4.5%.

Abstract
The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.