Application of output Space Mapping method for fast optimization using multi-physical modeling

Khlissa, Radhouane; Vivier, Stéphane; Vargas, Leticia; Friedrich, Guy

doi:10.1109/ecce.2012.6342665

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…In most cases, SM methods use coarse models designed as simplified and therefore accelerated versions of the reference model (Cheng et al, 2015;Encica et al, 2007;Echeverría and Hemker, 2008;Khlissa et al, 2012).…”

Section: Compel 372mentioning

confidence: 99%

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Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

Vivier

2018

COMPEL

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PurposeThis paper aims to introduce an original application of the corrected response surface method (CRSM) in the context of the optimal design of a permanent magnet synchronous machine used as an integrated starter generator. This method makes it possible to carry out this design in a very efficient manner, in comparison with conventional optimization approaches. Design/methodology/approachThe search for optimal conditions is achieved by the joint use of two multi-physics models of the machine to be optimized. The former models most finely the physical functioning of the machine; it is called “fine model”. The second model describes the same physical phenomena as the fine model but must be much quicker to evaluate. Thus, to minimize its evaluation time, it is necessary to simplify it considerably. It is called “coarse model”. The lightness of the coarse model allows it to be used intensively by conventional optimization algorithms. On the other hand, the fine reference model makes it possible to recalibrate the results obtained from the coarse model at any instant, and mainly at the end of each classical optimization. The difference in definition between fine and coarse models implies that these two models do not give the same output values for the same input configuration. The approach described in this study proposes to correct the values of the coarse model outputs by constructing an adjustment (correcting) response surface. This gives the name to this method. It then becomes possible to have the entire load of the optimization carried over to the coarse model adjusted by the addition of this correction response surface. FindingsThe application of this method shows satisfactory results, in particular in comparison with those obtained with a traditional optimization approach based on a single (fine) model. It thus appears that the approach by CRSM makes it possible to converge much more quickly toward the optimal configurations. Also, the use of response surfaces for optimization makes it possible to capitalize the modeling data, thus making it possible to reuse them, if necessary, for subsequent optimal design studies. Numerous tests show that this approach is relatively robust to the variations of many important functioning parameters. Originality/valueThe CRSM technique is an indirect multi-model optimization method. This paper presents the application of this relatively undeveloped optimization approach, combining the features and benefits of (Indirect) efficient global optimization techniques and (multi-model) space mapping methods.

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Section: Compel 372mentioning

confidence: 99%

“…This actuator is an interior permanent magnet synchronous machine. A complete description of the ISG machine and its models can be found in Khlissa et al (2012).…”

Section: Case Of Application 31 General Descriptionmentioning

confidence: 99%

Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

Vivier

2018

COMPEL

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“…Currently, there are many articles about model predictive control of PMSM. The literature , respectively, designed an MPC speed controller and MPC current controller, which replaces the PI‐based speed controller in traditional vector control. The current controller improves the robustness of the system, but there are still problems in the control system regarding the overall optimization of the cascade control system.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, there are many articles about model predictive control of PMSM. The literature [15,16], respectively, designed an MPC speed controller and MPC current controller, which replaces the a Correspondence to: Fengyang Liu. E-mail: superlfy93@163.com *Harbin University of Science and Technology, 52 Xuefu Rd, Nangang, Harbin, Heilongjiang 150080, China **Harbin Institute of Technology, No.…”

Section: Introductionmentioning

confidence: 99%

Single‐loop model prediction control of PMSM with moment of inertia identification

Liu

Erliang

Cui

2020

IEEJ Transactions Elec Engng

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In order to improve the dynamic response performance and robustness of the permanent magnet synchronous motor control system and optimize the parameter adjustment of the control system, a motor control method based on single‐loop model prediction and moment of inertia parameter identification with forgetting factor is proposed. First, the motor state equation in the prediction model is designed, and the single‐loop model predictive control method is proposed. Then, comparing the effects of various parameters of the motor on the running stability of the motor, a least‐squares identification method based on forgetting factor is added to identify the moment of inertia online and optimize the prediction model in real time. The simulation and experimental results show that the proposed method has good dynamic performance for the permanent magnet synchronous motor adapting to the rotational speed and load variation. At the same time, the addition of the moment of inertia identification enhances the accuracy and system robustness of the motor model parameters during the model prediction process. This method provides an effective way of engineering control. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Demagnetization analysis methods include analytical methods, magnetic network analysis, finite element method (FEM), and field reconstruction method. Analytical method has advantage of short simulation time, and it is easy to realize modern optimization algorithms [14–17], such as particle swarm optimization, but difficult to model accurately for an interior permanent magnet motor because of flux leakage of the rotor magnetic field. Magnetic network analysis has high simulation speed, and can reflect the relation between the design parameters and capability of anti‐demagnetization [18–20], but the precision is relatively low and it cannot simulate partial demagnetization accurately.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimum design method with anti‐demagnetization of high‐density permanent magnet synchronous motor

Shi

Zhouyun²

2014

IEEJ Transactions Elec Engng

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This paper presents a method of multiobjective optimization design with anti‐demagnetization aiming at the problem of irreversible demagnetization in high‐density permanent magnet synchronous motors (PMSMs) due to temperature and external magnetic field, at the same time considering the volume of permanent magnets and cost, torque ripple, and core loss. In the first step, a two‐dimensional magnetic network model is used to rapidly assess the basic design parameters and its ability to avenge the anti‐demagnetization of the PMSM. In the second step, the finite element method (FEM) is used to design the key parts of motor, and regression models that solve the model of the multiobjective problem are built based on the simulation experiment data. On this basis, multiobjective optimization result using genetic algorithm is used that can achieve a fast and efficiently global optimal solution. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Application of output Space Mapping method for fast optimization using multi-physical modeling

Cited by 14 publications

References 10 publications

Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

Single‐loop model prediction control of PMSM with moment of inertia identification

Multiobjective optimum design method with anti‐demagnetization of high‐density permanent magnet synchronous motor

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