Many Electric Traction Drive Systems (ETDS) are driven by electric motors primarily composed of high-energy Rare-Earth (RE) Permanent Magnets (PMs) such as Neodymium-Iron-Boron (NdFeB) and Samarium-cobalt (SmCo). Given the high cost of RE materials, there is a great interest among various industry sectors in searching for breakthrough technologies that reduce the consumption of high-energy RE magnets in their electro-mechanical energy conversion systems. The reduction of the magnetic volume is typically realized either by replacing a portion of the high-energy RE magnet with the low-energy Ferrite magnet (knows as hybrid structure) or by modifying the magnetic arrangement of the machine itself, where a portion of the physical RE magnets is removed and replaced by induced magnets (known as consequent-magnetic pole structure). For this purpose, this paper presents a review on the current state-of-the-art of motor typologies with Less-Rear-Earth-PMs (Less-RE PM) and their key design challenges, followed up by a series of recommended guidelines to overcome related design challenges.

The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks.

This paper presents an automated deep learning-based design methodology to facilitate the design and optimization processes in electro-mechanical energy conversion devices. To validate the generality of the model, a complex machine structure with hybrid Permanent Magnets (PMs) is selected as the case study. First, the machine’s geometrical topology and the respective design variables are described in the cylindrical coordinate system and programmed into a Finite Element (FE) software package. Next, the program sweeps through the predefined ranges of selected design variables and captures corresponding air-gap flux distribution through an automated FE-based parametric analysis. The air-gap flux density data is post-processed and fed into a deep neural network (DNN) training algorithm. In particular, 10,000 data sets are utilized for training the DNN model. The trained model successfully predicts the machine’s performance for any random set of parameters, as confirmed via FE. Finally, by leveraging the trained model, the structural parameters of the machine are optimized to limit higher-order spatial flux harmonics and the cogging torque.

This paper presents a design for a line start Synchronous Reluctance Motor (SynRM) capable of operating in two different synchronous speeds. The proposed design is based on variable pole numbers for the stator and the rotor. To accommodate the stator with variable pole numbers, the stator winding consists of two sets of independent windings to switch the operating pole number for low and high speed regions. Likewise, the rotor is especially designed to create two different reluctance pole numbers to match with the operating stator pole numbers. The proposed design enables the motor to operate as a SynRM at two different operating speeds without requiring a drive unit. To verify the performance of the proposed design, the motor is analyzed in steady state and transient domain using 3-D Finite Element Analysis (FEA).

This paper investigates protection challenges for line current differential (87 L) and distance protection function (21) posed by the integration of inverter-based resources (IBR) into transmission systems. The reliability of these functions is investigated in a weak in-feed system, where the IBR is the only source of fault current. The analysis of the protection schemes is performed on a real-world 25 bus network system, using a time-domain transient program (EMTP). The test system is interconnected with an IBR source, converted into a white-box model in accordance with IEEE 2800-2022 matching the negative sequence injection, phase angle, voltage ride through, and reactive power control strategy. The IBR response to 15 fault cases is simulated and tested against EMTP protection function models, then recorded into COMTRADE files. These files are used in a power system simulator to test the response of manufacturer's 87 L and 21 protection functions inside three different types of physical microprocessor relays. The following was concluded: the simulation tripping time response matches closely to the hardware tripping time response, the relaying philosophy used in the presence of synchronous generators is not sufficient due to the unique IBR fault response, the effect on overcurrent supervisors of 21 and 87 L relays must be carefully considered, and ground protection functions are impacted based on the presence of a grounding transformer in the IBR.

This paper presents the aggregate model of single phase induction motors used in residential heat pump loads. The equivalent circuit parameters of the aggregated motor model are derived by performing no load and locked rotor tests. The proposed model captures the loads aggregated behavior (in steady state and transient regime) for any arbitrary number of loads at the distribution substation, which is beneficial for utilities' planning studies. The proposed load model is applied to the IEEE 39 bus system and subjected to a 3-phase line to ground (LG) fault to study the motor's recovery and potential system delayed voltage recovery. To verify the validity of the proposed load model, results are compared with the same case study connected to individual motor loads. The simulation results indicate that the motor will recover for 15 cycle 3-phase faults and not for delayed clearing faults such as 90 cycles faults.