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

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@Article{DiCapua:2021:Sustainability,

• 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",
• title = "Analysis of Dynamic Wireless Power Transfer Systems Based on Behavioral Modeling of Mutual Inductance",
• journal = "Sustainability",
• year = "2021",
• volume = "13",
• number = "5",
• month = mar,
• keywords = "genetic algorithms, genetic programming, behavioural modeling, inductive coupling, mutual inductance, wireless power transfer",
• ISSN = "2071-1050",
• URL = "https://www.mdpi.com/2071-1050/13/5/2556",
• DOI = "doi:10.3390/su13052556",
• size = "15 pages",
• 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.",
• 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