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13. Hysteresis Current Control for Six-Phase Induction Motor Drives With Reduced Ripple and Improved Tracking Based on Subspace Decomposition and Restrained Voltage Vectors

Author

Shawier, Abdullah, Abdel-Azim, Wessam E., Yepes, Alejandro G., Abdel-Khalik, Ayman Samy, Hamad, Mostafa S., Ahmed, Shehab, and Doval-Gandoy, Jesus

Abstract

Six-phase induction motor (6PIM) drives offer enhanced fault tolerance and reduced per-phase ratings. Hysteresis current control (HCC) is attractive for 6PIMs because it is simple, robust and fast. HCC is conventionally implemented so that each leg voltage is directly set based on the respective phase-current error. However, this approach does not consider that, in multiphase drives, phase voltages and currents are related through a combination of equivalent impedances corresponding to various subspaces. In general, there is a notable dissimilarity between these impedances, being typically small for secondary ($\bm {xy}$) subspaces. This can cause large current distortion and poor reference tracking. This article proposes an improved HCC for 6PIM drives. Instead of directly inputting the per-phase current error to the hysteresis comparator and directly applying the switching states chosen by it, the input and output components associated with different subspaces are segregated. The input and output $\bm {xy}$ components are nullified in open loop so that the $\bm {xy}$ impedance no longer affects the HCC behavior, even if low. This prevents the disrupting $\bm {xy}$ currents, ensures effective tracking of the torque/flux-producing $\bm {\alpha \beta }$ reference current, and enables reconfiguration-less fault tolerance. Experiments using 6PIMs with different winding configurations corroborate the significant advantages of the proposal.

Published
2024
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14. A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces.

Author

Issa, Reda, Badr, Mohamed M., Shalash, Omar, Othman, Ali A., Hamdan, Eman, Hamad, Mostafa S., Abdel-Khalik, Ayman S., Ahmed, Shehab, and Imam, Sherif M.

Subjects
ELECTRIC vehicle batteries, DIGITAL twins, MOTOR vehicle driving, STANDARD deviations, MICROSOFT Azure (Computing platform), ELECTRIC automobiles
Abstract

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]

Published
2023
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