Fully electric ships have become popular to meet the demand for emission-free transportation and improve ships' functionality, reliability, and efficiency. Previous studies reviewed the shipboard power systems, the different types of shipboard energy storage devices, and the influences of the shore-to-ship connection on ports' electrical grid. However, the converter topologies used in the electrification of ships have received very little attention. This article presents a comprehensive topological review of currently available shore-to-ship and shipboard power converters in the literature and on the market. The main goal is to anticipate future trends and potential challenges to stimulate research to accelerate more efficient and reliable electric ships.

Residential buildings can actively participate in energy management strategies by integrating advanced metering infrastructure to have a reliable and stable distribution system. This paper introduces a novel home energy management system (HEMS) framework to minimize total electricity costs by optimizing the charging of an electric vehicle (EV). The methodology incorporates a non-intrusive load monitoring algorithm to extract the EV information from the total power consumption. To ensure accuracy in planning, there is a need for an accurate prediction model for both total power and EV charging profiles. Therefore, deep learning methods are used together with signal processing techniques to accurately predict aggregated power consumption and decompose the EV from it. The effectiveness of the proposed HEMS algorithm is validated using a dataset collected from Northern Norway. The proposed methodology is validated by the test dataset. The implementation of HEMS resulted in a decrease of 15.4% in the total cost of electricity consumption and a significant reduction of 52.2% for charging the EV compared to the base case. Furthermore, the peak of total power is reduced by 23.19% with the adoption of HEMS.

This paper studies the capacitive coupling in a capacitive power transfer (CPT) system designed for charging applications. It proposes mathematical models using the conformal transformation for calculating air-gapped and underwater capacitance and verifies the proposed models using COMSOL multiphysics and measurements. The measured results show that we can achieve nano-farad capacitance ranges if we submerge the capacitor in seawater. The seawater’s capacitance slightly changes when we increase the gap distance or the operating frequency. As the under seawater CPT system can be an attractive option for loosely-coupled charging applications, we further examine the system by focusing on the cross-coupling effects. The results show that the cross-coupling between the plate degrades the system’s power transfer capability and efficiency. With negligible cross-coupling effects, the system gives 129 W output power at an efficiency of 81.2%.

Capacitive power transfer (CPT) is a near-field wireless power transfer (WPT) technology that has attracted attention in different charging applications. By utilizing electric fields, CPT gives charging systems advantages in terms of cost, weight, flexibility, and mobility. This paper surveys a number of empirical published works in a period between 2015 and 2023. Additionally, it discusses theoretical and practical design considerations of a CPT system to understand and improve the technology and its applications. The paper studies the one- and two-port measuring approaches using vector network analyzers to determine the coupling parameters and compares the measurements to the simulated values using COMSOL Multiphysics©. The two-port approach gives more accurate results than the one-port approach. The paper designs and tests a 13.56MHz cpt system using the two-port measurement results. The system transfers 100W at 87.4% efficiency and 30mm separation distance. Lastly, the paper discusses the design limitations and challenges of the CPT systems, aiming to emphasize the design obstacles that can drive the advancement of the CPT systems for wireless charging applications.

Capacitive power transfer (CPT) has attracted attention for on-road electric vehicles, autonomous underwater vehicles, and electric ships charging applications. High power transfer capability and high efficiency are the main requirements of a CPT system. This paper proposes three possible solutions to achieve maximum efficiency, maximum power, or conjugate-matching. Each solution expresses the available load power and the efficiency of the CPT system as functions of capacitive coupling parameters and derives the required admittance of the load and the source. The experimental results demonstrated that the available power and the efficiency decrease by the increasing of the frequency from 300 kHz to 1 MHz and the separation distance change from 100 to 300 mm. The maximum efficiency solution gives 83% at 300 kHz and a distance of 100 mm, while the maximum power solution gives the maximum normalized power of 0.994 at the same frequency and distance. The CPT system can provide a good solution to charge electric ships and underwater vehicles over a wide separation distance and low-frequency ranges.

Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%.