Research on Parameter Matching and Optimization of Pure Electric Vehicle Power System Based on Computer

Wu, Xianhuan

doi:10.1088/1742-6596/1744/2/022079

Cited by 2 publications

(3 citation statements)

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“…There are numerous optimization studies for the electric motor to satisfy desired performance requirements in the literature. Design of experiment (DoE), sequential quadratic programming, genetic algorithm, ant colony optimization, particle swarm optimization, and backtracking search algorithm are the most used optimization approaches in the literature [6], [7], [8], [9], [10].…”

Section: A Electric Motor As Drivetrain Traction Modulementioning

confidence: 99%

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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: A Electric Motor As Drivetrain Traction Modulementioning

confidence: 99%

“…In (7), p (r k ) is the probability density function (histogram distribution) of the image matrix. MN is the pixel number of the image, and n k is the frequency of the intensity value in the image matrix.…”

Section: ) Stepmentioning

confidence: 99%

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

et al. 2023

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“…For a given set of design layouts, identifying the appropriate design parameters that maximize the design objectives of a hybrid electric vehicle is necessary to solve the hybrid electric vehicle design problem [5,6]. The study of matching and optimizing the key powertrain parameters can effectively reduce the emissions and energy consumption of the vehicle, hence, in the face of the requirements of energy conservation and environmental protection, hybrid electric vehicles have gradually become a research focus [7,8]. Zhao et al studied the parameter matching and configuration analysis of a fuel cell electric vehicle (EV) driving system.…”

Section: Introductionmentioning

confidence: 99%

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

et al. 2022

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Based on the requisition for an ideal precise power source for a hybrid electric emergency rescue vehicle (HE-ERV), we present an optimistic parameter matching and optimization schemes for the selection of a HE-ERV. Then, given a set of optimized power source components, they are classified into different types of HE-ERV. In this study, due to the different design objectives of different types of emergency rescue vehicles and the problems of hybrid electric vehicle parameter matching, a multi-island genetic algorithm (MIGA) and non-linear programming quadratic Lagrangian (NLPQL) is proposed for the matched parameters. The vehicle dynamic model is established based on the AVL Cruise simulation platform. The power source performance parameters are matched by theoretical analysis and coupled to the simulation platform. Finally, the optimized matched parameters are classified based on the support vector machines classification model to determine the category of the HE-ERV. The classification results showed that there is an unprecedented level for categorizing several factors of the power source parameters. This research showed that its more logical and reasonable to match HE-ERVs with medium motor/engine power output and battery capacity, as these can attain dynamic performance, extended driving range, and reduced energy consumption.

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Research on Parameter Matching and Optimization of Pure Electric Vehicle Power System Based on Computer

Cited by 2 publications

References 0 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

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

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