Ship microgrids have recently received increased attention, mainly due to the extensive use of power electronically interfaced loads and sources. Characteristics of these microgrids are similar to islanded terrestrial microgrids, except the presence of highly dynamic large loads, such as propulsion loads. The presence of such loads and sources with power-electronic converter interfaces lead to severe power quality issues in ship microgrids. Generally, these issues can be classified as voltage variations, frequency variations and waveform distortions which are commonly referred to as harmonic distortions. Amongst the solutions identified, energy storage is considered to be the most promising technology for mitigating voltage and/or frequency deviations. Passive filtering is the commonly used technology for reducing harmonic distortions, which requires bulky capacitors and inductors. Active filtering is emerging as an alternative, which could be realised even within the same interfacing converter of the energy storage system. The aim of this paper is to investigate recent developments in these areas and provide readers with a critical review on power quality issues, energy storage technologies and strategies that could be used to improve the power quality in ship microgrids. Moreover, a brief introduction to ship power system architectures is also presented in the paper.

This paper investigates the effect of two soft magnetic materials on a high-speed machine design, namely, 6.5% silicon steel and cobalt-iron alloy. The effect of design parameters on the machine performance as an aircraft starter-generator is analyzed. The material properties which include B-H characteristics and the losses are obtained at different frequencies under an experiment and used to predict the machine performance accurately. In the investigation presented in this paper, it is shown that machines designed with 6.5% silicon steel at a high core flux density has lower weight and lower losses than the cobalt-iron alloy designs. This is mainly due to the extra weight contributed by the copper content especially in the end-windings. Due to the high operating frequencies, the core losses in the cobalt-iron machine designs are found to outweigh the copper losses incurred in the silicon steel machines. It is also shown that change in stack length/number of turns has a considerable effect on the copper losses at starting, however has no significant advantage on rated efficiency which happens to be in a field-weakening operating point. It is also shown that the performance of the machine designs depends significantly on material selection and the operating point of the core. The implications of the variation of design parameters on the machine performance is discussed and provide insight into the influence of parameters that effect overall power density.

Zero-voltage loop (ZVL) commutation in switched reluctance (SR) motor operation can result in a significant improvement of their performance by lowering the flux-linkage peaks and harmonic magnitudes of the magnetic flux densities within the core of the SR motor. The efficiency enhancement provided by ZVL commutation, especially in high-speed SR motoring has not been thoroughly investigated to-date. This paper investigates the energy conversion efficiency of SR motors with ZVL current commutation. The lowering of core losses is investigated with finite element modeling of a 250 kW 12/8 SR motor. An enhancement of efficiency in the ranges of 0--3% and 0--5% is computed considering operation below base-speed and above base-speed respectively. The effect of core material on the ZVL commutation performance is also investigated for the 250 kW 12/8 SR motor. The increase of efficiency with ZVL is further validated with experimental results on a 300 W 6/4 SR motor which achieves an enhancement in the range of 2-6%.

Modern applications demand challenging operation requirements from electrical machines. Variable-flux machine (VFM) concepts are found to offer new opportunities for improved machine design. This paper reviews the VFM technology. Three topologies, mainly the parallel hybrid VFMs, series hybrid VFMs, and Variable-Flux Flux Intensifying type VFMs are discussed in detail. The Variable-Flux Flux Intensifying machines are found to be the state-of-the-art VFM technology available to-date. The paper also reviews the magnetization process in VFMs and relates this with the B-H curve of the magnetic material. The conventional PM machine mathematical model is adapted to represent the operation of VFMs. The different operational limitations of VFMs are explained and the different modes of operation of VFMs have been reviewed and compared. The magnetization state manipulation techniques are reviewed. The AC magnetization is found to be the more preferable approach to reduce weight and manufacturing complexity of VFMs. The trapezoidal current pulse based approach is popular in AC magnetization state manipulation, however, new advanced forms of current vector trajectory control techniques offer comparatively better magnetic state manipulation and higher speed capability for remagnetization of the magnets in VFMs. The paper also reviews the different magnetization state estimation techniques that are useful during control systems development for VFMs. The magnetic material for VFM design is reviewed and potential alternative magnetic material that can satisfy the requirements of VFMs have been identified. Finally, the current status, challenges, as well as the potential of VFMs technology are discussed in the conclusion of the paper.

The real-time computational load of an optimization problem plays a major role in the application of model predictive control (MPC) to fast switching power electronic converters. It is, therefore, highly desired to alleviate the computational burden of the MPC to render it feasible for these applications. In this brief, an efficient MPC algorithm based on Laguerre functions is proposed for a phase shift full-bridge (PSFB) dc-dc converter, in which the main control objective is to maintain the converter's load voltage at the desired set point while fulfilling multiple physical constraints, including the nonlinear peak input current constraint. It is shown in this work that the Laguerre functions' parameterization of MPC offers a promising solution to the high computational requirement associated with the conventional MPC without compromising on the dynamic closed-loop performance. The efficacy of the proposed control algorithm is tested on 60-W experimental hardware for 40-μs sampling time. Experimental results are also compared with the conventional proportional-integral (PI) control structure in order to illustrate different aspects of the proposed control scheme.

Electric batteries have gained attention with recent developments in the transport sector, especially with electric vehicles (EVs) technology and with the rapid development in the energy storage sector with application to the electricity grid. Lithium-ion batteries (LIBs) are particularly popular due to their high-power density, high energy density, low self-discharge rate, and performance. LIB systems are also widely utilized in extreme operating conditions and harsh environments, and the safe operation of any battery management system requires rapid detection and accurate diagnosis of faults. To have an effective fault diagnosis, the nonlinear behavior of battery systems has been studied in considering the battery real-time operation. In addition, accurate battery models are used to mimic battery physical processes and predict aging. The knowledge of battery model parameters plays a crucial role in accurately predicting performance and ageing. This paper critically reviews different battery models, such as electrochemical models, equivalent circuit models, and data-driven models. Then, the parameter extraction methods for the electrochemical model were discussed critically since it has been identified as the most promising battery model. According to the literature parameter estimation for electrochemical models was discussed under the categories of online, offline, and analytical methods. By the state-of-the-art review conducted, it has been identified that the mixed method that combines the online and offline methods shows good performance compared to using them separately. This paper also discusses some future research directions to obtain better parameter extraction methods for electrochemical models to facilitate battery fault diagnosis.