Range Extension of Electric Vehicles with Independently Driven Front and Rear PMSM Drives by Optimal Driving and Braking Torque Distribution

Lekshmi, S; Priya, P S Lal

doi:10.1109/pesgre45664.2020.9070246

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…In summary, in these studies copper losses, state of charge, damage analysis, efficiency, power consumption, heat transfer analysis, core losses, fuel consumption, power losses, current density, rated power, torque density, iron losses, torque ripple, winding temperature and driver losses are investigated in terms of the performance metric such as acceleration, hill start, over loading, normal cruise, regenerative breaking, speed variation. And geometric e-motor design parameters are tried to be optimized by using the algorithm such as Frequency Cubic, Sequential Surrogate Optimizer, Genetic Algorithm, Sequential Quadratic Programming, System-Based Minimization, Machine-Based Minimization Algorithms, Bi-Objective Optimization, Multi-Objective Design Optimization, Multi-Objective Genetic Algorithm, Multi-Objective Sequential Optimization Method, Root-Mean-Square Error, Kriging model using NSGA-II, Central Composite Design, Loss-Minimization Algorithm, Differential Evolution Algorithm, Base Point Optimization, Particle Swarm Optimization, Design of Experiment and Non-dominated Sorting Genetic Algorithm [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]. All the algorithms mentioned here are techniques that are used for e-motor analysis, such as the numerical or iterative (finite element) method, to obtain results by running iteratively on algorithms.…”

Section: Design Optimization Studies For E-motormentioning

confidence: 99%

An Innovative Approach to Electrical Motor Geometry Generation Using Machine Learning and Image Processing Techniques

et al. 2023

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This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer's analysis process, and enhance the performance of EVs.INDEX TERMS Artificial neural network, feature extraction, image generation, interior permanent magnet motor, machine learning, 2D filter.The associate editor coordinating the review of this manuscript and approving it for publication was Mostafa M. Fouda .

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Section: Design Optimization Studies For E-motormentioning

confidence: 99%

An Innovative Approach to Electrical Motor Geometry Generation Using Machine Learning and Image Processing Techniques

et al. 2023

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“…2 that PMSMs have a number of advantageous design features, and by the fact that PMSM control systems can be characterised by a wide range of control algorithms with increased flexibility and performance, ranging from their mathematical description to their implementation in embedded systems [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Improved Performance of the PMSM Sensorless Control System Based on DTC Strategy and SMC Using Fractional Order and Fractal Dimension Calculus

Nicola,

Selișteanu

et al. 2024

Preprint

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Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

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“…It is worth remembering that PMSM is incorporated into servomotors used in robotics, computer peripherals, the aerospace industry, electric drives, etc. [3,4]. Thus, PMSM control takes on new dimensions, in terms of the complex concerns of researchers to improve the performance of control systems.…”

Section: Introductionmentioning

confidence: 99%

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

Nicola

Ionete

et al. 2023

Sensors

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This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

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Range Extension of Electric Vehicles with Independently Driven Front and Rear PMSM Drives by Optimal Driving and Braking Torque Distribution

Cited by 7 publications

References 7 publications

An Innovative Approach to Electrical Motor Geometry Generation Using Machine Learning and Image Processing Techniques

An Innovative Approach to Electrical Motor Geometry Generation Using Machine Learning and Image Processing Techniques

Improved Performance of the PMSM Sensorless Control System Based on DTC Strategy and SMC Using Fractional Order and Fractal Dimension Calculus

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

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