Electric Machines and Drives

Filizadeh, Shaahin

doi:10.1201/9781315169651

Cited by 13 publications

(3 citation statements)

References 90 publications

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“…The last four are included in the constraints. The DC voltage is provided by Equations ( 1) and ( 2) for three-phase PMSMs (see [16]):…”

Section: The Initializationmentioning

confidence: 99%

“…where p n is the number of poles, pw is the number of parallel wires, mag d is the diameter of the envelope of the magnets, and P2x is the inner radius of the stator minus the air gap. Constraint (15) specifies the minimum distance between the magnets and the rotor cuts, constraint (16) prevents the rotor and stator from crossing, constraint (17) specifies the minimum winding requirements, and constraint (18) ensures the correct shape of the magnets.…”

Section: The Constraintsmentioning

confidence: 99%

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Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Istenes,

Polák

2024

Energies

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In this paper, a software framework is presented through an application that is able to jointly optimize an electric motor and a gearbox for the design of a drive system for electric vehicles. The framework employs a global optimization method and uses both analytical and finite element method (FEM) models to evaluate the objective functions. The optimization process is supported by a statistical surrogate model, which allows a large reduction of runtime. An earlier version of this framework was only suitable for electric motor optimization. In the application presented in a previous paper, the motor of a belt-driven electric drive system was optimized. In this paper, the optimization of the same drive system is shown, but now with a combined optimization of a gear drive and motor. The objective functions of optimization are minimizing the total loss energy and the weight of the drive system. The optimization results are compared with previous results to demonstrate the further potential of joint optimization.

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“…The last four are included in the constraints. The DC voltage is provided by Equations ( 1) and ( 2) for three-phase PMSMs (see [16]):…”

Section: The Initializationmentioning

confidence: 99%

Section: The Constraintsmentioning

confidence: 99%

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Istenes,

Polák

2024

Energies

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“…The coefficients 𝐾𝐾 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝐾𝐾 𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝐾𝐾 𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠𝑠𝑠 , 𝐾𝐾 ℎ𝑠𝑠𝑟𝑟𝑠𝑠 and 𝐾𝐾 𝑃𝑃𝑃𝑃𝑠𝑠 are the corresponding losses coefficients. The losses in Equations ( 5)-( 8) can be calculated as follows (Kral et al, 2013;Filizadeh, 2013;Maroteaux, 2016;Nasir, 2020;IEC/TS, 2010):…”

Section: Parametrizing the Thermal Modelmentioning

confidence: 99%

Data-driven reinforcement learning-based parametrization of a thermal model in induction traction motors

Fattouh,

Sahoob

2023

Linköping Electronic Conference Proceedings

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Monitoring the temperature of induction traction motors is crucial for the safe and efficient operation of railway propulsion systems. Several thermal models were developed to capture the thermal behaviour of the induction motors. With proper calibrating of the thermal model parameters, they can be used to predict the motor’s temperature. Moreover, calibrated thermal models can be used in simulation to evaluate the motor’s performance under different operating conditions and find the optimal control strategies.Parameterization of the thermal model is usually performed in dedicated labs where the induction motor is operated under predefined operating conditions and calibrating algorithms are then used to find the model’s parameters. With the development of digital tools, including smart sensors, Internet of Things (IoT) devices, software applications, and various data collection platforms, operational data can be collected and used later to calibrate the parameters of the thermal model. Nevertheless, calibrating the model’s parameters from operational data collected from different driving cycles is challenging as the model has to capture the thermal behaviour from all driving cycles’ data.In this paper, a data-driven reinforcement learning-based parametrization method is proposed to calibrate a thermal model in induction traction motors. First, the thermal behaviour of the induction motor is modelled as a thermal equivalent network. Second, a reinforcement learning (RL) agent is designed and trained to calibrate the model parameters using the data collected from multiple driving cycles. The proposed method is validated by numerical simulation results. The results showed that the trained RL agent came up with a policy that adeptly handles diverse driving cycles with different performance characteristics.

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Implementation of Induction Motor Speed and Torque Control System with Reduced Order Model in ANSYS Twin Builder

Pliuhin

Tsegelnyk

Plankovskyy

et al. 2023

Lecture Notes in Networks and Systems

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Electric Machines and Drives

Cited by 13 publications

References 90 publications

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Data-driven reinforcement learning-based parametrization of a thermal model in induction traction motors

Implementation of Induction Motor Speed and Torque Control System with Reduced Order Model in ANSYS Twin Builder

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