Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.

Considering the rapid urbanization and the road congestion, the development of smart parking solutions becomes more crucial, especially in terms of economic interests. Thanks to IoT-connectivity and the cloud-integrated platforms, drivers can easily find a vacant parking lot with smart parking services. This paper intervenes in the profit of parking management systems. The paper proposes a new technique “paging technique” which increases the utilization factor of parking slots. The proposed method takes advantage of the idle time that exists between two successful parking services in the same slot. Besides, it investigates the possibility of using the idle times from different parking slots to provide a continuous parking time for an additional car. The paging technique is optimally implemented using mixed-integer linear programming that maximizes the utilization factor for the parking slots with minimum car transitions. Moreover, a data model for the parking management system has been constructed while considering the three major customers, namely, regular, prepaid, and walk-in customers. The difference between fixed and dynamic pricing for parking has been investigated. The technique has been validated using GAMS optimization software and hardware using DSP with Coin-or branch and cut solver (CBC) under real-life conditions. The statistical results prove that the revenue for the proposed parking system has increased significantly. Finally, a comparative analysis is performed, benchmarking our proposed method against recent competing algorithms in real world applications to demonstrate its superiority.

Due to simplicity, efficiency, and the ability to accommodate energy storage devices, DC distribution networks have been seen as an optimal alternative to AC distribution networks, especially aboard future electric ships. The emerging distribution DC system entails new control and management techniques. Therefore, an integrated DC power distribution network aboard an electric ship is selected as the case study in this paper. To meet the requirements of such a large-scale mobile power system, a multiport solid-state transformer (SST) based on silicon carbide (SiC) switches/MOSFETs is proposed. Thus, the system embodiment can significantly be reduced. Moreover, at the DC distribution level, a high penetration of renewable generation with energy storage is allowed and a six-phase asymmetrical induction machine (IM) can directly be integrated. Simulations have been conducted based on a 2 MW shipboard distribution network. The effects of the propulsion system dynamics on the SST are highlighted as well. Finally, a 2 kW lab-scale prototype has been implemented to validate the theoretical findings.

Proper operation of the grid-tie transformerless converters under unbalanced and distorted conditions entails a precise detection of the frequency and fundamental component of the grid voltage. One of the main problems that could arise during the estimation of grid parameters is the existence of a DC offset generated from measurement and A/D conversion. This undesirable induced DC offset could appear as a part of the reference sinusoidal current of grid-tie converters. Although literature has proposed the use of an extended complex Kalman filter (ECKF) for the estimation of positive and negative sequence voltage components as a promising competitor to phase locked loops, mitigating the effect of possible DC offsets when a Kalman filter is employed remains scarce. This paper proposes a new extended complex Kalman filter to improve the filter stability for estimating the frequency and the fundamental positive and negative symmetrical components of the grid voltages, where DC offset, scaling error, and noise can successfully be rejected. The theoretical findings are experimentally validated.

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. And transitioning to renewable energy sources is essential for achieving sustainability and combating climate change. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, considering the minimization of operational running costs, and the maximization of profit. In this paper, two optimization scenarios were studied to compare the results. In the first scenario, the minimization of carbon emissions was achieved. In the second scenario, the minimization of running costs and the maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then, the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly ones via the Kalman filter rather than by applying the classic polynomial interpolation methods.