Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang, Shen

doi:10.36227/techrxiv.16782748

Cited by 6 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-performance control of electric machine drives: Compared to the traditional sensorless control of PMSM drives, such as state observers, Kalman filters, fuzzy logic, highfrequency signal injection, etc. [66,132], AI-based methods provide more high-performance solutions for inverter nonlinearities [133]. Figure 10 illustrates the ANN-based structure scheme for improving IPMSM sensorless control.…”

Section: Discussionmentioning

confidence: 99%

“…This places FPGAs as the next candidate for autonomous drone design. In addition, while GPUs excel at parallel processing, FPGAs perform integrated AI and provide several advantages with low latency, high throughput, excellent flexibility, affordable cost, and low-power consumption [66]. Furthermore, it is suitable for TinyML-based acceleration on FPGA in terms of Processing-in-Memory (PIM) architecture [67].…”

Section: Hardware Accelerators With Fpga and Risc-vmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

View full text Add to dashboard Cite

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Hardware Accelerators With Fpga and Risc-vmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…This, in turn, serves as a foundation for otherwise unfeasible subsequent analyses. [43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: State Of the Art Overviewmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

View full text Add to dashboard Cite

The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

show abstract

“…Among the different categories of AI, machine learning is used primarily in the research of power systems such as electric drives [192]. A general overview about the utilization of AI methods for power electronics systems and electrical drives is given in [192,193], respectively. The machine learning techniques used to control or monitoring electrical drives are summarized in [194].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Recent Achievements in the Control of Interior Permanent-Magnet Synchronous Machine Drives: A Comprehensive Overview of the State of the Art

Stumpf,

Tóth-Katona

2023

Energies

View full text Add to dashboard Cite

Interior permanent-magnet synchronous machines (IPMSMs) are widely used as traction motors in electric drive-trains because of their high torque-per-ampere characteristics and potential for wide field-weakening operations to expand the constant-power range. This paper offers a categorization and a comprehensive overview of the control techniques applied to IPMSM drivesin addition to presenting the necessary theoretical background. The basic concept, features and limitations, as well as the latest developments of the strategies, are summarized in the paper. This overview helps to lay the theoretical basis as well as to clarify the opportunities, challenges and future trends for controlling IPMSM drives for traction applications.

show abstract

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Cited by 6 publications

References 23 publications

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Recent Achievements in the Control of Interior Permanent-Magnet Synchronous Machine Drives: A Comprehensive Overview of the State of the Art

Contact Info

Product

Resources

About