Analysis of Dynamic Wireless Power Transfer Systems Based on Behavioral Modeling of Mutual Inductance
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- @Article{DiCapua:2021:Sustainability,
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author = "Giulia {Di Capua} and Antonio Maffucci and
Kateryna Stoyka and Gennaro {Di Mambro} and Salvatore Ventre and
Vincenzo Cirimele and Fabio Freschi and
Fabio Villone and Nicola Femia",
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title = "Analysis of Dynamic Wireless Power Transfer Systems
Based on Behavioral Modeling of Mutual Inductance",
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journal = "Sustainability",
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year = "2021",
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volume = "13",
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number = "5",
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month = mar,
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keywords = "genetic algorithms, genetic programming, behavioural
modeling, inductive coupling, mutual inductance,
wireless power transfer",
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ISSN = "2071-1050",
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URL = "https://www.mdpi.com/2071-1050/13/5/2556",
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DOI = "doi:10.3390/su13052556",
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size = "15 pages",
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abstract = "This paper proposes a system-level approach suitable
to analyse 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 behavioural model that relates mutual
inductance between the coil pair to their relative
positions along the actual vehicle trajectory. The
behavioural 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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notes = "also known as \cite{su13052556}",
- }
Genetic Programming entries for
Giulia Di Capua
Antonio Maffucci
Kateryna Stoyka
Gennaro Di Mambro
Salvatore Ventre
Vincenzo Cirimele
Fabio Freschi
Fabio Villone
Nicola Femia
Citations