Design Optimization with Flux Weakening of High-Speed PMSM for Electrical Vehicle Considering the Driving Cycle

Dang, L.; Bernard, Nicolas; Bracikowski, Nicolas; Berthiau, Gérard

doi:10.1109/tie.2017.2726962

Cited by 89 publications

(33 citation statements)

References 35 publications

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“…A Permanent Magnet Synchronous Motor (PMSM) and a three-phase inverter, powered by the DC bus, were taken as the traction-motor system of the FCEV. The Maximum Torque Per Ampere (MTPA) control principle was adopted to control the motor below the base speed and a flux-weakening method was used to achieve maximum motor speed [35][36][37].…”

Section: Structure Of the Fcev And Its Power Systemmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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Section: Structure Of the Fcev And Its Power Systemmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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“…The k-means clustering algorithm uses the Euclidean distance between a sample and cluster center as a criterion for similarity determination, and it has the advantages of efficiency and simplicity [19,43]. The Euclidean distance is calculated as shown in (7).…”

Section: Clustering Based On the K-means And Svmmentioning

confidence: 99%

“…However, the data of these international standard driving cycles were collected from internal combustion engine vehicles (ICEVs). Because of the differences in the torque characteristics, power characteristics [7][8][9][10][11], and braking characteristics between EVs and ICEVs, the driving cycles of EVs are significantly different from those of ICEVs [12][13][14]. The research on driving range calculation, state of charge estimation, energy consumption prediction, and energy management optimization strategies for EV under the international standard driving cycles can produce large errors.…”

Section: Introductionmentioning

confidence: 99%

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Zhao

et al. 2018

Journal of Advanced Transportation

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This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.

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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%

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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Design Optimization with Flux Weakening of High-Speed PMSM for Electrical Vehicle Considering the Driving Cycle

Cited by 89 publications

References 35 publications

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

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

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