Behavioral Switching Loss Modeling of Inverter Modules

Stoyka, Kateryna; Ohashi, Ricieri A. Pessinatti; Femia, N.

doi:10.1109/smacd.2018.8434850

Cited by 8 publications

(3 citation statements)

References 13 publications

(18 reference statements)

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“…As the efficiency is function of M according to (9), the proposed behavioral model allows calculating the derivative of the efficiency with respect to ∆z. By using the first-order linear approximation (14), it is possible to obtain the function ∆z max (∆y) given in (15):…”

Section: Wpts Performance Sensitivity Analysismentioning

confidence: 99%

“…In this paper, we follow the second approach, by deriving, validating and using an analytical behavioral model of M. This modeling approach has been previously used to assess the losses and performances in power devices and power modules, like IGBTs [12], inductors [13], and inverter modules [14], and to study static WPTSs [15]. Compared to the static condition analyzed in [15], the dynamic problem analyzed herein introduces new challenges and features, related to the geometry and to the time-domain behavior.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Dynamic Wireless Power Transfer Systems Based on Behavioral Modeling of Mutual Inductance

et al. 2021

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This paper proposes a system-level approach suitable to analyze the performance of a dynamic Wireless Power Transfer System (WPTS) for electric vehicles, accounting for the uncertainty in the vehicle trajectory. The key-point of the approach is the use of an analytical behavioral model that relates mutual inductance between the coil pair to their relative positions along the actual vehicle trajectory. The behavioral model is derived from a limited training data set of simulations, by using a multi-objective genetic programming algorithm, and is validated against experimental data, taken from a real dynamic WPTS. This approach avoids the massive use of computationally expensive 3D finite element simulations, that would be required if this analysis were performed by means of look-up tables. This analytical model is here embedded into a system-level circuital model of the entire WPTS, thus allowing a fast and accurate analysis of the sensitivity of the performance as the actual vehicle trajectory deviates from the nominal one. The system-level analysis is eventually performed to assess the sensitivity of the power and efficiency of the WPTS to the vehicle misalignment from the nominal trajectory during the dynamic charging process.

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Section: Wpts Performance Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Wireless Power Transfer Systems Based on Behavioral Modeling of Mutual Inductance

et al. 2021

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“…In this paper, we present a possible solution to these issues, based on a computation approach combining electromagnetic 3D-FEM numerical solvers and Multi-Objective Genetic Programming (MOGP) evolutionary algorithm [9]. The MOGP algorithm was previously used for discovering power loss behavioral models of IGBTs, power inductors and inverter power modules [10]- [12]. The proposed approach uses 3D-FEM solvers to calculate only the mutual inductance over a limited small set of coils geometrical misalignment parameters (axial, lateral and rotational), and provide data sets to the MOGP algorithm generating analytical expressions of the mutual inductance as a function of misalignment parameters.…”

Section: Introductionmentioning

confidence: 99%

Mutual Inductance Behavioral Modeling for Wireless Power Transfer System Coils

Capua

Femia

Stoyka

et al. 2021

IEEE Trans. Ind. Electron.

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This paper derives low-complexity behavioral analytical models of the mutual inductance between the coupling coils of Wireless Power Transfer Systems (WPTSs), as functions of their reciprocal position. These models are extremely useful in the characterization and design optimization of WPTSs. Multi-Objective Genetic Programming (MOGP) algorithm is adopted to generate models ensuring an optimal trade-off between accuracy and complexity. The training and validation data sets needed for the generation of the models are here obtained by performing numerical full-3D electromagnetic simulations. The resulting behavioral models allow accurate and fast evaluation of the WPTS coils mutual inductance, over a wide range of misalignment conditions, enabling easier system analysis and optimization.

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