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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Permanent magnet (PM) machines with fractional slot concentrated windings (FSCW) constitute a notably remarkable proposition for electric vehicles. Additionally, an integrated onboard battery charger (IOBC) provides another superiority as it exploits the components of the powertrain to charge the battery without any additional components. Interior permanent magnet (IPM) motor arises as a credible choice due to its high torque density, resulting from the high saliency ratio. The optimal design of an IPM motor has been extensively presented from different perspectives, but the optimal design of a motor employed for IOBC application for both propulsion and charging modes has not been studied extensively. In this paper, the design and optimization of a 12-slot/10-pole IPM motor with IOBC are studied under both propulsion and charging modes. A finite-element-based optimization with the aid of a genetic algorithm technique is proposed to obtain the optimal machine by maximizing the average torque and minimizing the torque ripple, core losses, and magnet size. [ABSTRACT FROM AUTHOR]

Power converters generate switching common mode voltage (CMV) through the pulse width modulation (PWM). Several problems occur in the drive systems due to the generated CMV. These problems can be dangerous to the insulation and bearings of the electric machine windings. In recent years, many modulation methods have been developed to reduce the CMV in multiphase machines. Symmetrical nine-phase machines with single-neutral are considered in this paper. In this case, conventional PWM uses eight active vectors of different magnitudes in combination with two zero states in a switching cycle, and this generates maximum CMV. This paper proposes two PWM schemes to reduce the CMV in such a system. The first scheme is called active zero state (AZS). It replaces the zero vectors with suitable opposite active vectors. The second scheme uses ten large active vectors during switching and is called SVM-10L. Compared with conventional strategies, the AZS reduces the peak CMV by 22.2%, and the SVM-10L reduces the peak CMV by 88.8%. Moreover, this paper presents a carrier-based implementation of the proposed schemes to simplify the implementation. The proposed schemes are assessed using simulations and experimental studies for an induction motor load under different case studies. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) have started to play a significant role in the transport sector and automotive industries. The broader market penetration of BEVs has still not been achieved due to significant barriers associated with initial costs and short driving ranges. The purchase price and a limited driving range are barriers that are inevitably associated with battery technology. Therefore, the growing demand for BEVs has expedited new innovative approaches to improve battery capacity and performance and to reduce battery costs. Considerable advancements have been employed to meet the challenges. However, there are still many challenges to make BEVs affordable and convenient for users. In this review, the main aims are to identify and address challenges by considering the prospects of BEVs in the future market and to explore the technological and financial difficulties of low energy density of battery materials, fast charging rate, battery lifetime, and cost-effectiveness, associated with effectively implementing and adopting BEVs. Moreover, potential suggestions are proposed for researchers, manufacturers, users, and government policy planners. Finally, a concrete conclusion is drawn by disseminating a vision about the future adoption of BEVs. This review of technologies, challenges, prospects, and potential solutions associated with BEVs could provide a base for effective strategic policy and could help policymakers to frame strategies for adapting and achieving targets. This review could help to achieve sustainable BEV transport and to adopt next-generation green vehicles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Permanent magnet machines (PMs) equipped with fractional slot concentrated windings (FSCWs) have been preferably proposed for electric vehicle (EV) applications. Moreover, integrated on-board battery chargers (OBCs), which employ the powertrain elements in the charging process, promote the zero-emission future envisaged for transportation through the transition to EVs. Based on the available literature, the employed machine, as well as the adopted winding configuration, highly affects the performance of the integrated OBC. However, the optimal design of the FSCW-based PM machine in the charging mode of operation has not been conceived thus far. In this paper, the design and multi-objective optimization of an asymmetrical 12-slot/10-pole integrated OBC based on the efficient magnetic equivalent circuit (MEC) approach are presented, shedding light on machine performance during charging mode. An 'initial' surface-mounted PM (SPM) machine is first designed based on the magnetic equivalent circuit (MEC) model. Afterwards, a multi-objective genetic algorithm is utilized to define the optimal machine parameters. Finally, the optimal machine is compared to the 'initial' design using finite element (FE) simulations in order to validate the proposed optimization approach and to highlight the performance superiority of the optimal machine over its initial counterpart. [ABSTRACT FROM AUTHOR]

The multiphase induction motor is considered to be the promising alternative to the conventional three-phase induction motor, especially in safety-critical applications because of its inherent fault-tolerant feature. Therefore, the attention of many researchers has been paid to develop different techniques for detecting various fault types of multiphase induction motors, to securely switch the control mode of the multiphase drive system to its post-fault operation mode. Therefore, several fault detection methods have been researched and adapted; one of these methods is the indices-based fault detection technique. This technique was firstly introduced to detect open-phase fault of multiphase induction motors. The main advantage of this technique is that its mathematical formulation is straightforward and can easily be understood and implemented. In this paper, the study of the indices-based fault detection technique has been extended to test its applicability in detecting some other stator and rotor fault types of multiphase induction motors, namely, open-phase, open-switch, bad connection and broken rotor bar faults. Experimental and simulation validations of this technique are also introduced using a 1 kW prototype symmetrical six-phase induction motor. [ABSTRACT FROM AUTHOR]

The transition to electric vehicles (EVs) has received global support as initiatives and legislation are introduced in support of a zero-emissions future envisaged for transportation. Integrated on-board battery chargers (OBCs), which exploit the EV drivetrain elements into the charging process, are considered an elegant solution to achieve this widespread adoption of EVs. Surface-mounted permanent-magnet (SPM) machines have emerged as plausible candidates for EV traction due to their nonsalient characteristics and ease of manufacturing. From an electric machine design perspective, parasitic torque ripple and core losses need to be minimized in integrated OBCs during both propulsion and charging modes. The optimal design of EV propulsion motors has been extensively presented in the literature; however, the performance of the optimal traction machine under the charging mode of operation for integrated OBCs has not received much attention in the literature thus far. This paper investigates the optimal design of a six-phase SPM machine employed in an integrated OBC with two possible winding layouts, namely, dual three-phase or asymmetrical six-phase winding arrangements. First, the sizing equation and optimized geometrical parameters of a six-phase 12-slot/10-pole fractional slot concentrated winding (FSCW)-based SPM machine are introduced. Then, variations in the output average torque, parasitic torque ripple, and parasitic core losses with the slot opening width and the PM width-to-pole pitch ratio are further investigated for the two proposed winding layouts under various operation modes. Eventually, the optimally designed machine is simulated using analytical magnetic equivalent circuit (MEC) models. The obtained results are validated using 2D finite element (FE) analysis. [ABSTRACT FROM AUTHOR]

In rural and remote areas, solar photovoltaic energy (PV) water pumping systems (SPWPSs) are being favored over diesel-powered water pumping due to environmental and economic considerations. PV is a clean source of electric energy offering low operational and maintenance cost. However, the direct-coupled SPWPS requires inventive solutions to improve the system's efficiency under solar power variations while producing the required amount of pumped water concurrently. This paper introduces a new quadratic V/f (Q V/f) control method to drive an induction motor powered directly from a solar PV source using a two-stage power converter without storage batteries. Conventional controllers usually employ linear V/f control, where the reference motor speed is derived from the PV input power and the dc-link voltage error using a simple proportional–integral (PI) controller. The proposed Q V/f-based system is compared with the conventional linear V/f control using a simulation case study under different operating conditions. The proposed controller expectedly enhances the system output power and efficiency, particularly under low levels of solar irradiance. Some alternative controllers rather than the simple PI controller are also investigated in an attempt to improve the system dynamics as well as the water flow output. An experimental prototype system is used to validate the proposed Q V/f under diverse operating conditions. [ABSTRACT FROM AUTHOR]