Multicriteria PM Motor Design Based on ANFIS Evaluation of EV Driving Cycle Efficiency

Krasopoulos, Christos T.; Beniakar, Minos E.; Kladas, Antonios G.

doi:10.1109/tte.2018.2810707

Cited by 59 publications

(33 citation statements)

References 29 publications

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“…The main influencing factors are converted into numerical values through fuzzy rules [22]. According to the above research information on the factors influencing electric buses' power consumption, fuzzy quantitative analysis and processing of all factors are carried out.…”

Section: Establishment Of Power Consumption Feature Librarymentioning

confidence: 99%

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

et al. 2018

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Abstract:With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

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Section: Establishment Of Power Consumption Feature Librarymentioning

confidence: 99%

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

et al. 2018

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“…Nevertheless, the control, design, and optimization of rotating electrical machines require advanced techniques and modern mathematical models to keep up with the ever-increasing demand for energy [21][22][23][24][25]. In fact, practical solutions to engineering problems involve model-integrated computing [26,27]. Model-based computing approaches or so-called soft computing (SC) provide a challenging way to replace the procedure with a knowledge-based method [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

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Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

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“…Optimal design is a method of finding the values of design variables to obtain an optimal solution within a range of constraints. The optimal design for PMSMs is created by combining design methods such as the analytical model, magnetic equivalent circuits (MEC) model, and finite element analysis (FEA) with optimal design algorithms [1][2][3][4][5][6][7][8]. First of all, there are studies on optimal design using the analytical model [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…K-means clustering algorithm was utilized to obtain the best solution out of the eight clusters. Finally, some research on optimization combined with FEA have been published [5][6][7]. The work in [5] performed multi-objective shape optimization of a PMSM based on FEA and particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

You

2019

Energies

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To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

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Multicriteria PM Motor Design Based on ANFIS Evaluation of EV Driving Cycle Efficiency

Cited by 59 publications

References 29 publications

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

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