Induction machine design for dynamic loss minimization along driving cycles for traction applications

Shi, Yuying; Lorenz, Robert D.

doi:10.1109/ecce.2017.8095793

Cited by 16 publications

(2 citation statements)

References 12 publications

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“…In this study, a controller based on RBF for network adaptation is considered [XYZ1]. The common RBF is a Gauss function in the RBF neural network that can be expressed as [34].…”

Section: A Proposed Artificial Neural Network Controllermentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

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This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional-integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

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“…In this study, a controller based on RBF for network adaptation is considered [XYZ1]. The common RBF is a Gauss function in the RBF neural network that can be expressed as [34].…”

Section: A Proposed Artificial Neural Network Controllermentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

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“…However, reducing steady‐state losses is the point of interest in the existing design methods [31, 32]. High and excessive current peak losses in the machine can occur during transit with varying flow links when conventional induction machines are designed for high stability efficiency.…”

Section: Introductionmentioning

confidence: 99%

Power optimisation scheme of induction motor using FLC for electric vehicle

Kassem

Sayed

Kassem

et al. 2020

IET Electrical Systems in Transportation

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In electric vehicles (EVs) and hybrid EVs, energy efficiency is essential where the energy storage is limited. Adding to its high stability and low cost, the induction motor efficiency improves with loss minimisation. Also, it can consume more power than the actual need to perform its working when it is operating in less than full load condition. This study proposes a control strategy based on the fuzzy logic control (FLC) for EV applications. FLC controller can improve the starting current amplitude and saves more power. Through the MATLAB/SIMULINK software package, the performance of this control was verified through simulation. As compared with the conventional proportional integral derivative controller, the simulation schemes show good, high-performance results in time-domain response and rapid rejection of system-affected disturbance. Therefore, the core losses of the induction motor are greatly reduced, and in this way improves the efficiency of the driving system. Finally, the suggested control system is validated by the experimental results obtained in the authors' laboratory, which are in good agreement with the simulation results.

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