A Review of Synchronous Reluctance Motor-Drive Advancements

Heidari, Hamidreza; Rassõlkin, Anton; Kallaste, Ants; Vaimann, Toomas; Andriushchenko, Ekaterina; Belahcen, Anouar; Lukichev, Dmitry V.

doi:10.3390/su13020729

Cited by 60 publications

(31 citation statements)

References 185 publications

(252 reference statements)

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“…Different types of motors such as DC drives, induction motors (IM), synchronous (SR) machines, permanent magnet (PM) drives are used in EVs. An overview of the state-of-the-art solutions of electric motors for EVs is given in Alamoudi et al (2019), with an overview of automotive electric propulsion systems with reduced or no permanent magnets presented in Boldea et al (2014) and a review of synchronous reluctance motor-drive advancements provided by Heidari et al (2021).…”

Section: Integrated Intelligent Hardware and Sensor Fusion Functionsmentioning

confidence: 99%

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

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Section: Integrated Intelligent Hardware and Sensor Fusion Functionsmentioning

confidence: 99%

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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“…In recent studies in the literature, researchers have conducted numerous studies to increase the efficiency of motors [8]. Artificial intelligence has offered many popular optimization methods for motor designs.…”

Section: Introductionmentioning

confidence: 99%

“…Among different motor structures, SynRM represents a superior technology that has lately met the requirements of the IE5 standard [13]. This motor has proved to be a robust structure with a somewhat comparable performance to a well-developed permanent magnet synchronous motor and is potentially as cheap and affordable as an induction motor that lacks the rare-earth magnets in the rotor [8,14].…”

Section: Introductionmentioning

confidence: 99%

Design of a Research Laboratory Drive System for a Synchronous Reluctance Motor for Vector Control and Performance Analysis

et al. 2021

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Motor-drive systems have the most significant share in industrial energy consumption, which requires a deep study in every aspect of the field. This paper presents a synchronous reluctance motor (SynRM) drive system based on Plecs RT box 1. The system’s design provides the opportunity for the open-loop and closed-loop control of the motor and a characteristic performance analysis of the motor. This paper focuses on the hardware implementation of a research laboratory setup and the precise vector control of the SynRM in real-time. The application of the digital controller and inverter to drive SynRM is examined. The voltage, current, and speed transducers were employed for monitoring the protective measures and to control the motor in the closed-loop. The design of the signal conditioning and the intermediary cards for isolation and data acquisition are described in detail. An algorithm is proposed to measure the whole system parameters, including motor, inverter, and cables. Thanks to the RT box 1, the principle of real-time simulation of control algorithms is investigated, and the rapid control prototyping of field-oriented control (FOC) of SynRM was implemented. The simulation of the system was carried out in the Plecs platform, and the results are presented. The experimental results of the implemented control algorithms validate the setup’s performance and the control algorithm. Finally, as a study of the motor’s performance, the efficiency map of the motor is drawn in different speed and torque ranges.

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“…Modeling of motors is crucial for many objectives, including life-cycle analysis, performance analysis, and more importantly, control purposes [21][22][23]. In this paper, a modified dynamic equivalent circuit model of LIMs is proposed.…”

Section: Introductionmentioning

confidence: 99%

A Modified Dynamic Model of Single-Sided Linear Induction Motors Considering Longitudinal and Transversal Effects

Heidari

Rassõlkin

Razzaghi³

et al. 2021

Electronics

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This paper proposes a modified dynamic equivalent circuit model for a linear induction motor considering both longitudinal end effect and transverse edge effect. The dynamic end effect (speed-dependent end effect) is based on conventional Duncan’s approach. The transverse edge effect is investigated by using three correction factors applied to the secondary resistance and magnetizing inductance. Moreover, the iron saturation effect, the skin effect, and the air-gap leakage effect are incorporated into the proposed model by using the field-analysis method. A new topology of the steady-state and space-vector model of linear induction, regarding all mentioned phenomena, is presented. The parameters of this model are calculated using both field analysis and the finite-element method. The steady-state performance of the model is first validated using the finite-element method. Additionally, the dynamic performance of the proposed model is studied. The results prove that the proposed equivalent circuit model can precisely predict the dynamic and steady-state performances of the linear induction.

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A Review of Synchronous Reluctance Motor-Drive Advancements

Cited by 60 publications

References 185 publications

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Design of a Research Laboratory Drive System for a Synchronous Reluctance Motor for Vector Control and Performance Analysis

A Modified Dynamic Model of Single-Sided Linear Induction Motors Considering Longitudinal and Transversal Effects

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