{"title":"Flight-Validated Electric Powertrain Efficiency Models for Small UASs","authors":"Farid Saemi, Moble Benedict","doi":"10.3390/aerospace11010016","DOIUrl":null,"url":null,"abstract":"Minimizing electric losses is critical to the success of battery-powered small unmanned aerial systems (SUASs) that weigh less than 25 kgf (55 lb). Losses increase energy and battery weight requirements which hinder the vehicle’s range and endurance. However, engineers do not have appropriate models to estimate the losses of a motor, motor controller, or battery. The aerospace literature often assumes an ideal electrical efficiency or describes modeling approaches that are more suitable for controls engineers. The electrical literature describes detailed design tools that target the motor designer. We developed SUAS powertrain models targeted for vehicle designers and systems engineers. The analytical models predict each component’s losses using high-level specifications readily published in SUAS component datasheets. We validated the models against parametric experimental studies involving novel powertrain flight data from a specially instrumented quadcopter. Given propeller torque and speed, our integrated models predicted a quadcopter’s battery voltage within 5% of experimental data for a 5+ min mission despite motor and controller efficiency errors up to 10%. The models can reduce development costs and timelines for different stakeholders. Users can evaluate notional or existing powertrain configurations over entire missions without testing any physical hardware.","PeriodicalId":48525,"journal":{"name":"Aerospace","volume":"1990 11","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerospace","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/aerospace11010016","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
Minimizing electric losses is critical to the success of battery-powered small unmanned aerial systems (SUASs) that weigh less than 25 kgf (55 lb). Losses increase energy and battery weight requirements which hinder the vehicle’s range and endurance. However, engineers do not have appropriate models to estimate the losses of a motor, motor controller, or battery. The aerospace literature often assumes an ideal electrical efficiency or describes modeling approaches that are more suitable for controls engineers. The electrical literature describes detailed design tools that target the motor designer. We developed SUAS powertrain models targeted for vehicle designers and systems engineers. The analytical models predict each component’s losses using high-level specifications readily published in SUAS component datasheets. We validated the models against parametric experimental studies involving novel powertrain flight data from a specially instrumented quadcopter. Given propeller torque and speed, our integrated models predicted a quadcopter’s battery voltage within 5% of experimental data for a 5+ min mission despite motor and controller efficiency errors up to 10%. The models can reduce development costs and timelines for different stakeholders. Users can evaluate notional or existing powertrain configurations over entire missions without testing any physical hardware.
期刊介绍:
Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.