Optimal sizing of electric multirotor configurations

Giulio Avanzini; Emanuele L. de Angelis; Fabrizio Giulietti; Edmondo Minisci

Optimal sizing of electric multirotor configurations

Giulio Avanzini, Emanuele L. de Angelis, Fabrizio Giulietti, Edmondo Minisci

Mechanical And Aerospace Engineering

Research output: Contribution to conference › Proceeding

12 Downloads (Pure)

Abstract

A sizing tool for the definition of the configuration of electrically powered multirotor platforms is developed, which accounts for a realistic battery discharge model. The tool is developed to provide the community with the possibility of deriving the best configuration for performing a given task, while accounting for specific constraints and performance requirements. An evolutionary algorithm is used for searching the design space and to identify feasible designs with optimal performance in terms of maximum hovering time on the target and payload weight fraction.

Original language	English
Publication status	Accepted/In press - 31 May 2018
Event	8th EASN-CEAS International Workshop on Manufacturing for Growth & Innovation - University of Glasgow, Glasgow, United Kingdom Duration: 4 Sep 2018 → 7 Sep 2018 https://easnconference.eu/i/

Conference

Conference	8th EASN-CEAS International Workshop on Manufacturing for Growth & Innovation
Country/Territory	United Kingdom
City	Glasgow
Period	4/09/18 → 7/09/18
Internet address	https://easnconference.eu/i/

Keywords

evolutionary algorithms
optimal sizing
electric Vehicles
UAVs
multirotor

Access to Document

Avanzini-etal-EASN2018-Optimal-sizing-of-electric-multirotor-configurationsAccepted author manuscript, 647 KBLicence: CC BY 4.0

Cite this

@conference{86bd30bb4ad04f929cde521461771c42,

title = "Optimal sizing of electric multirotor configurations",

abstract = "A sizing tool for the definition of the configuration of electrically powered multirotor platforms is developed, which accounts for a realistic battery discharge model. The tool is developed to provide the community with the possibility of deriving the best configuration for performing a given task, while accounting for specific constraints and performance requirements. An evolutionary algorithm is used for searching the design space and to identify feasible designs with optimal performance in terms of maximum hovering time on the target and payload weight fraction.",

keywords = "evolutionary algorithms, optimal sizing, electric Vehicles, UAVs, multirotor",

author = "Giulio Avanzini and {de Angelis}, {Emanuele L.} and Fabrizio Giulietti and Edmondo Minisci",

year = "2018",

month = may,

day = "31",

language = "English",

note = "8th EASN-CEAS International Workshop on Manufacturing for Growth & Innovation ; Conference date: 04-09-2018 Through 07-09-2018",

url = "https://easnconference.eu/i/",

}

TY - CONF

T1 - Optimal sizing of electric multirotor configurations

AU - Avanzini, Giulio

AU - de Angelis, Emanuele L.

AU - Giulietti, Fabrizio

AU - Minisci, Edmondo

PY - 2018/5/31

Y1 - 2018/5/31

N2 - A sizing tool for the definition of the configuration of electrically powered multirotor platforms is developed, which accounts for a realistic battery discharge model. The tool is developed to provide the community with the possibility of deriving the best configuration for performing a given task, while accounting for specific constraints and performance requirements. An evolutionary algorithm is used for searching the design space and to identify feasible designs with optimal performance in terms of maximum hovering time on the target and payload weight fraction.

AB - A sizing tool for the definition of the configuration of electrically powered multirotor platforms is developed, which accounts for a realistic battery discharge model. The tool is developed to provide the community with the possibility of deriving the best configuration for performing a given task, while accounting for specific constraints and performance requirements. An evolutionary algorithm is used for searching the design space and to identify feasible designs with optimal performance in terms of maximum hovering time on the target and payload weight fraction.

KW - evolutionary algorithms

KW - optimal sizing

KW - electric Vehicles

KW - UAVs

KW - multirotor

UR - https://easnconference.eu/i/

UR - https://www.matec-conferences.org/

M3 - Proceeding

T2 - 8th EASN-CEAS International Workshop on Manufacturing for Growth & Innovation

Y2 - 4 September 2018 through 7 September 2018

ER -

Optimal sizing of electric multirotor configurations

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this