Integrated on-board battery chargers (OBCs) have been recently introduced as an optimal/elegant solution to increase electric vehicle (EV) market penetration as well as minimize overall EV cost. Unlike conventional off-board and on-board battery chargers, integrated OBCs exploit the existing propulsion equipment for battery charging without extra bulky components and/or dedicated infrastructure. OBCs are broadly categorized into three-phase and single-phase types with unidirectional or bidirectional power flow. This paper starts with surveying the main topologies introduced in the recent literature employing either induction or permanent magnet motors to realize fully integrated slow (single-phase) and fast (three-phase) on-board EV battery charging systems, with emphasis on topologies that entail no or minimum hardware reconfiguration. Although, permanent magnet (PM) motors with conventional double-layer distributed winding layouts have been deployed in most commercial EV motors, the non-overlapped fractional slot concentrated winding (FSCW) has been the prevailing choice in the most recent permanent magnet motor designs due to its outstanding operational merits. Hence, a thorough investigation of the impact different FSCW stator winding designs have on machine performance under the charging process is presented in this paper. To this end, the induced magnet losses, which represent a challenging demerit of the FSCW, have been used to compare different topologies under both propulsion and charging operation modes. Based on the introduced comparative study, the optimal slot/pole combinations that correspond to the best compromise under both operational modes have been highlighted.

Fault identification in Photovoltaic (PV) array is a contemporary research topic motivated by the higher penetration levels of PV systems in recent electrical grids. Therefore, this work aims to define an optimal Machine learning (ML) structure of automatic detection and diagnosis algorithm for common PV array faults, namely, permanent (Arc Fault, Line-to-Line, Maximum Power Point Tracking unit failure, and Open-Circuit faults), and temporary (Shading) under a wide range of climate datasets, fault impedances, and shading scenarios. To achieve the best-fit ML structure, three distinct ML classifiers are compared, namely, Decision Tree (DT) based on different splitting criteria, K-Nearest Neighbors (KNN) based on the different metrics of distance and weighting functions, and Support Vector Machine (SVM) based on different Kernel functions and multi-classification approaches. Also, Bayesian Optimization is adopted to assign the optimal hyperparameters to the fault classifiers. To investigate the performance of classifiers reported, both simulation and experimental case studies are carried out and presented.

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.

Six-phase induction machines have mostly shown promise in high-power electric drive applications as well as wind energy conversion systems. Different winding configurations for six-phase stators have been published, namely, dual three-phase (D3P), symmetrical six-phase (S6P), and asymmetrical six-phase (A6P) winding layouts. Although a body of research investigating six-phase machines and their control for different six-phase winding arrangements exists, a thorough comparative study between these different arrangements in terms of machine parameters and performance, has not been done so far. This paper employs a 12-phase stator with a configurable terminal box to compare different six-phase configurations by simply reconnecting the stator terminals of the twelve phases in different manners to obtain an equivalent six-terminal stator. This way, the same stator machine dimensions and copper volume will be assumed for all connections. The comparative study focuses on the effect of winding connection on machine parameters of the different subspaces, phase current quality and machine characteristic curves. Experimental validation has been carried out using a 1kW prototype system.

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.

Multiphase permanent-magnet synchronous machines (PMSMs) with nonsinusoidal back-electromotive force (back-EMF) offer high fault tolerance and torque density for electric vehicles. Most current-reference generation methods either minimize stator copper loss (SCL) or maximize achievable torque. Optimization of both goals is accomplished by full-torque-range minimum-loss (FRML) strategies, but so far just for sinusoidal back-EMF. Thus, FRML for nonsinusoidal back-EMF should be sought. Moreover, many methods are only suitable for healthy conditions or specific machines, harmonics, or open-phase-fault (OPF) scenarios. Additionally, the torque range may be extended by permitting torque ripple or (transiently) greater rms current, but this approach is not general nor FRML yet. This paper proposes online FRML current-reference generation for multiphase PMSMs with nonsinusoidal back-EMF: nonsinusoidal-back-EMF FRML (NSBE-FRML). When the torque reference is feasible, minimum SCL is attained while maximizing the achievable torque (i.e., FRML). For higher torque references, the instantaneous torque deviation is minimized, and the torque reference is saturated in consecutive samples limiting the torque ripple to a pre-specified threshold. Furthermore, the rms current is limited after transient overload by automatically decreasing the torque reference. The NSBE-FRML is suitable for any harmonics, healthy/OPF conditions, and multiphase PMSMs with negligible saliency ratio. Experiments are performed with a six-phase PMSM.