Optimizing Battery Life for Electric UAVs using a Bayesian Framework
In summary, this paper lays a simple flight plan optimization strategy based on the particle filtering framework described in [5]. This is meant as a first step in formalizing computationally tractable stochastic programming techniques to optimally generate flight plans in response to battery life predictions. This approach takes advantage of the PF framework to simultaneously generate the optimal/sub-optimal flight plan simultaneously with predicting the RUL. Several steps lie ahead like a comparative analysis of alternative stochastic models in terms of optimality as well as computational cost. These options will need to be validated by flight tests where robustness to environmental conditions like air temperature and density as well as wind speed can be evaluated. The notion of risk-tolerance can be introduced via appropriate objective functions, thus allowing a non-zero risk of the dead stick condition in order to use more battery power.
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "Miryam Strautkalns", "@type": "vcard:Contact", "hasEmail": "mailto:miryam.strautkalns@nasa.gov" } |
| description | In summary, this paper lays a simple flight plan optimization strategy based on the particle filtering framework described in [5]. This is meant as a first step in formalizing computationally tractable stochastic programming techniques to optimally generate flight plans in response to battery life predictions. This approach takes advantage of the PF framework to simultaneously generate the optimal/sub-optimal flight plan simultaneously with predicting the RUL. Several steps lie ahead like a comparative analysis of alternative stochastic models in terms of optimality as well as computational cost. These options will need to be validated by flight tests where robustness to environmental conditions like air temperature and density as well as wind speed can be evaluated. The notion of risk-tolerance can be introduced via appropriate objective functions, thus allowing a non-zero risk of the dead stick condition in order to use more battery power. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "2012_IEEEAero_BatteryUAV.pdf", "format": "PDF", "mediaType": "application/pdf", "description": "2012_IEEEAero_BatteryUAV.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2012_IEEEAero_BatteryUAV.pdf" } ] |
| identifier | DASHLINK_703 |
| issued | 2013-04-25 |
| keyword |
[ "ames", "dashlink", "nasa" ] |
| landingPage | https://c3.nasa.gov/dashlink/resources/703/ |
| modified | 2025-03-31 |
| programCode |
[ "026:029" ] |
| publisher |
{ "name": "Dashlink", "@type": "org:Organization" } |
| title | Optimizing Battery Life for Electric UAVs using a Bayesian Framework |
